definition of visual representation of data

What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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Al Boicheva

Al is an illustrator at GraphicMama with out-of-the-box thinking and a passion for anything creative. In her free time, you will see her drooling over tattoo art, Manga, and horror movies.

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Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends.  Harvard Business Review  (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We’ll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or  Design Thinking  sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate  workflows .  Data modeling  also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn’t limited to performance dashboards. For example, while  text mining  an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team.

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The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published  The Visual Display of Quantitative Information  (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

• Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
• Pie charts and stacked bar charts:  These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
• Line charts and area charts:  These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
• Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.
• Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
• Heat maps:  These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.
• Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

• D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers.  D3.js  (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.
• ECharts:  A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc.  Echarts  (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.
• Vega:   Vega  (link resides outside ibm.com) defines itself as “visualization grammar,” providing support to customize visualizations across large datasets which are accessible from the web.
• deck.gl: It is part of Uber's open source visualization framework suite.  deck.gl  (link resides outside ibm.com) is a framework, which is used for  exploratory data analysis  on big data. It helps build high-performance GPU-powered visualization on the web.

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You’ll want the data that you provide to motivate people to act within their scope of their role. If you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual:  Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple:  Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

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Blog Graphic Design What is Data Visualization? (Definition, Examples, Best Practices)

What is Data Visualization? (Definition, Examples, Best Practices)

Written by: Midori Nediger Jun 05, 2020

Words don’t always paint the clearest picture. Raw data doesn’t always tell the most compelling story. 

The human mind is very receptive to visual information. That’s why data visualization is a powerful tool for communication.    

But if “data visualization” sounds tricky and technical don’t worry—it doesn’t have to be. 

This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.

Table of Contents

• What is data visualization?

What is data visualization used for?

Types of data visualizations.

• How to present data visually  (for businesses, marketers, nonprofits, and education)
• Data visualization examples

Data visualization is used everywhere. 

Businesses use data visualization for reporting, forecasting, and marketing. 

CREATE THIS REPORT TEMPLATE

Nonprofits use data visualizations to put stories and faces to numbers. 

Source:  Bill and Melinda Gates Foundation

Scholars and scientists use data visualization to illustrate concepts and reinforce their arguments.

CREATE THIS MIND MAP TEMPLATE

Reporters use data visualization to show trends and contextualize stories. 

While data visualizations can make your work more professional, they can also be a lot of fun.

What is data visualization? A simple definition of data visualization:

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart , infographic , diagram or map. 

The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. 

Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data. 

Data visualizations often use elements of visual storytelling to communicate a message supported by the data. 

There are many situations where you would want to present data visually. 

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Let’s look at some examples for each use case.

1. Make data digestible and easy to understand

Often, a large set of numbers can make us go cross-eyed. It can be difficult to find the significance behind rows of data. 

Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks. 

Related : How to Use Data Visualization in Your Infographics

For example, this infographic breaks down the concept of neuroplasticity in an approachable way:

Source: NICABM

The same goes for complex, specialized concepts. It can often be difficult to break down the information in a way that non-specialists will understand. But an infographic that organizes the information, with visuals, can demystify concepts for novice readers.

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2. Identify trends and outliers

If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly. 

Even better, charts enable you to pick up on trends a lot quicker than you would sifting through numbers.

For example, here’s a simple chart generated by Google Search Console that shows the change in Google searches for “toilet paper”. As you can see, in March 2020 there was a huge increase in searches for toilet paper:

Source: How to Use SEO Data to Fuel Your Content Marketing Strategy in 2020

This chart shows an outlier in the general trend for toilet paper-related Google searches. The reason for the outlier? The outbreak of COVID-19 in North America. With a simple data visualization, we’ve been able to highlight an outlier and hint at a story behind the data. 

Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information. 

3. Tell a story within the data

Numbers on their own don’t tend to evoke an emotional response. But data visualization can tell a story that gives significance to the data. 

Designers use techniques like color theory , illustrations, design style and visual cues to appeal to the emotions of readers, put faces to numbers, and introduce a narrative to the data. 

Related : How to Tell a Story With Data (A Guide for Beginners)

For example, here’s an infographic created by World Vision. In the infographics, numbers are visualized using illustrations of cups. While comparing numbers might impress readers, reinforcing those numbers with illustrations helps to make an even greater impact. 

Source: World Vision

Meanwhile, this infographic uses data to draw attention to an often overlooked issue:

Read More:  The Coronavirus Pandemic and the Refugee Crisis

4. Reinforce an argument or opinion

When it comes to convincing people your opinion is right, they often have to see it to believe it. An effective infographic or chart can make your argument more robust and reinforce your creativity. 

For example, you can use a comparison infographic to compare sides of an argument, different theories, product/service options, pros and cons, and more. Especially if you’re blending data types.

5. Highlight an important point in a set of data

Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion. 

Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.

For example, in this data visualization, contrasting colors are used to emphasize the difference in the amount of waste sent to landfills versus recycled waste:

Here’s another example. This time, a red circle and an arrow are used to highlight points on the chart where the numbers show a drop: 

Highlighting specific data points helps your data visualization tell a compelling story.

6. Make books, blog posts, reports and videos more engaging

At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics. 

The added benefit of creating engaging visuals like infographics is that it has enabled our site to be featured in publications like The Wall Street Journal , Mashable , Business Insider , The Huffington Post and more. 

That’s because data visualizations are different from a lot of other types of content people consume on a daily basis. They make your brain work. They combine concrete facts and numbers with impactful visual elements. They make complex concepts easier to grasp. 

Here’s an example of an infographic we made that got a lot of media buzz:

Read the Blog Post: Every Betrayal Ever in Game of Thrones

We created this infographic because a bunch of people on our team are big Game of Thrones fans and we wanted to create a visual that would help other fans follow the show. Because we approached a topic that a lot of people cared about in an original way, the infographic got picked up by a bunch of media sites. 

Whether you’re a website looking to promote your content, a journalist looking for an original angle, or a creative building your portfolio, data visualizations can be an effective way to get people’s attention.

Data visualizations can come in many different forms. People are always coming up with new and creative ways to present data visually. 

Generally speaking, data visualizations usually fall under these main categories:

An infographic is a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic. 

While infographics can take many forms, they can typically be categorized by these infographic types:

• Statistical infographics
• Informational infographics
• Timeline infographics
• Process infographics
• Geographic infographics
• Comparison infographics
• Hierarchical infographics
• List infographics
• Resume infographics

Read More: What is an Infographic? Examples, Templates & Design Tips

Charts 

In the simplest terms, a chart is a graphical representation of data. Charts use visual symbols like line, bars, dots, slices, and icons to represent data points. 

Some of the most common types of charts are:

• Bar graphs /charts
• Line charts
• Bubble charts
• Stacked bar charts
• Word clouds
• Pictographs
• Area charts
• Scatter plot charts
• Multi-series charts

The question that inevitably follows is: what type of chart should I use to visualize my data? Does it matter?

Short answer: yes, it matters. Choosing a type of chart that doesn’t work with your data can end up misrepresenting and skewing your data. 

For example: if you’ve been in the data viz biz for a while, then you may have heard some of the controversy surrounding pie charts. A rookie mistake that people often make is using a pie chart when a bar chart would work better. 

Pie charts display portions of a whole. A pie chart works when you want to compare proportions that are substantially different. Like this:

CREATE THIS CHART TEMPLATE

But when your proportions are similar, a pie chart can make it difficult to tell which slice is bigger than the other. That’s why, in most other cases, a bar chart is a safer bet.

Here is a cheat sheet to help you pick the right type of chart for your data:

Want to make better charts? Make engaging charts with Venngage’s Chart Maker .

Related : How to Choose the Best Types of Charts For Your Data

Similar to a chart, a diagram is a visual representation of information. Diagrams can be both two-dimensional and three-dimensional. 

Some of the most common types of diagrams are:

• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Use case diagrams

Diagrams are used for mapping out processes, helping with decision making, identifying root causes, connecting ideas, and planning out projects.

CREATE THIS DIAGRAM TEMPLATE

Want to make a diagram ? Create a Venn diagram and other visuals using our free Venn Diagram Maker .

A map is a visual representation of an area of land. Maps show physical features of land like regions, landscapes, cities, roads, and bodies of water. 

Source: National Geographic

A common type of map you have probably come across in your travels is a choropleth map . Choropleth maps use different shades and colors to indicate average quantities. 

For example, a population density map uses varying shades to show the difference in population numbers from region to region:

Create your own map for free with Venngage’s Map Maker .

How to present data visually (data visualization best practices)

While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively. 

Not a designer? No problem. Venngage’s Graph Maker  will help you create better graphs in minutes.

1. Avoid distorting the data

This may be the most important point in this whole blog post. While data visualizations are an opportunity to show off your creative design chops, function should never be sacrificed for fashion. 

The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely. 

There are a number of different ways data can be distorted in a chart. Some common ways data can be distorted are:

• Making the baselines something other than 0 to make numbers seem bigger or smaller than they are – this is called “truncating” a graph
• Compressing or expanding the scale of the Y-axis to make a line or bar seem bigger or smaller than it should be
• Cherry picking data so that only the data points you want to include are on a graph (i.e. only telling part of the story)
• Using the wrong type of chart, graph or diagram for your data
• Going against standard, expected data visualization conventions

Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.

Read More: 5 Ways Writers Use Graphs To Mislead You

Want to create an engaging line graph? Use Venngage’s Line Graph Maker to create your own in minutes.

2. Avoid cluttering up your design with “chartjunk”

When it comes to best practices for data visualization, we should turn to one of the grandfather’s of data visualization: Edward Tufte. He coined the term “ chartjunk ”, which refers to the use of unnecessary or confusing design elements that skews or obscures the data in a chart. 

Here’s an example of a data visualization that suffers from chartjunk:

Source: ExcelUser

In this example, the image of the coin is distracting for readers trying to interpret the data. Note how the fonts are tiny – almost unreadable. Mistakes like this are common when a designers tries to put style before function. 

Read More : The Worst Infographics of 2020 (With Lessons for 2021)

3. Tell a story with your data

Data visualizations like infographics give you the space to combine data and narrative structure in one page. Visuals like icons and bold fonts let you highlight important statistics and facts.

For example, you could customize this data visualization infographic template to show the benefit of using your product or service (and post it on social media):

USE THIS TEMPLATE

  This data visualization relies heavily on text and icons to tell the story of its data:

This type of infographic is perfect for those who aren’t as comfortable with charts and graphs. It’s also a great way to showcase original research, get social shares and build brand awareness.

4. Combine different types of data visualizations

While you may choose to keep your data visualization simple, combining multiple types of charts and diagrams can help tell a more rounded story.

Don’t be afraid to combine charts, pictograms and diagrams into one infographic. The result will be a data visualization infographic that is engaging and rich in visual data.

Design Tip: This data visualization infographic would be perfect for nonprofits to customize and include in an email newsletter to increase awareness (and donations).

Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:

Design Tip: This infographic could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more.

Make your own bar graph in minutes with our free Bar Graph Maker .

5. Use icons to emphasize important points

Icons are perfect for attracting the eye when scanning a page. (Remember: use visual cues!)

If there are specific data points that you want readers to pay attention to, placing an icon beside it will make it more noticeable:

Design Tip: This infographic template would work well on social media to encourage shares and brand awareness.

You can also pair icons with headers to indicate the beginning of a new section.

Meanwhile, this infographic uses icons like bullet points to emphasize and illustrate important points. 

Design Tip: This infographic would make a great sales piece to promote your course or other service.  

6. Use bold fonts to make text information engaging

A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text. 

Even if you have a lot of text information, you can still create present data visually. Use bold, interesting fonts to make your data exciting. Just make sure that, above all else, your text is still easy to read.

This data visualization uses different fonts for the headers and body text that are bold but clear. This helps integrate the text into the design and emphasizes particular points:

Design Tip: Nonprofits could use this data visualization infographic in a newsletter or on social media to build awareness, but any business could use it to explain the need for their product or service. 

As a general rule of thumb, stick to no more than three different font types in one infographic.

This infographic uses one font for headers, another font for body text, and a third font for accent text. 

Read More: How to Choose Fonts For Your Designs (With Examples)

Design Tip: Venngage has a library of fonts to choose from. If you can’t find the icon you’re looking for , you can always request they be added. Our online editor has a chat box with 24/7 customer support.

7. Use colors strategically in your design

In design, colors are as functional as they are fashionable. You can use colors to emphasize points, categorize information, show movement or progression, and more. 

For example, this chart uses color to categorize data:

Design Tip : This pie chart can actually be customized in many ways. Human resources could provide a monthly update of people hired by department, nonprofits could show a breakdown of how they spent donations and real estate agents could show the average price of homes sold by neighbourhood.

You can also use light colored text and icons on dark backgrounds to make them stand out. Consider the mood that you want to convey with your infographic and pick colors that will reflect that mood. You can also use contrasting colors from your brand color palette.

This infographic template uses a bold combination of pinks and purples to give the data impact:

Read More: How to Pick Colors to Captivate Readers and Communicate Effectively

8. Show how parts make up a whole

It can be difficult to break a big topic down into smaller parts. Data visualization can make it a lot easier for people to conceptualize how parts make up a whole.

Using one focus visual, diagram or chart can convey parts of a whole more effectively than a text list can. Look at how this infographic neatly visualizes how marketers use blogging as part of their strategy:

Design Tip: Human resources could use this graphic to show the results of a company survey. Or consultants could promote their services by showing their success rates.

Or look at how this infographic template uses one focus visual to illustrate the nutritional makeup of a banana:

CREATE THIS FLYER TEMPLATE

9. Focus on one amazing statistic

If you are preparing a presentation, it’s best not to try and cram too many visuals into one slide. Instead, focus on one awe-inspiring statistic and make that the focus of your slide.

Use one focus visual to give the statistic even more impact. Smaller visuals like this are ideal for sharing on social media, like in this example:

Design Tip: You can easily swap out the icon above (of Ontario, Canada) using Venngage’s drag-and-drop online editor and its in-editor library of icons. Click on the template above to get started.

This template also focuses on one key statistic and offers some supporting information in the bar on the side:

10. Optimize your data visualization for mobile

Complex, information-packed infographics are great for spicing up reports, blog posts, handouts, and more. But they’re not always the best for mobile viewing. 

To optimize your data visualization for mobile viewing, use one focus chart or icon and big, legible font. You can create a series of mobile-optimized infographics to share multiple data points in a super original and attention-grabbing way.

For example, this infographic uses concise text and one chart to cut to the core message behind the data:

CREATE THIS SOCIAL MEDIA TEMPLATE

Some amazing data visualization examples

Here are some of the best data visualization examples I’ve come across in my years writing about data viz. 

Evolution of Marketing Infographic

Graphic Design Trends Infographic

Stop Shark Finning Nonprofit Infographic

Source: Ripetungi

Coronavirus Impact on Environment Data Visualization

What Disney Characters Tell Us About Color Theory

World’s Deadliest Animal Infographic

Source: Bill and Melinda Gates Foundation

The Secret Recipe For a Viral Creepypasta

Read More: Creepypasta Study: The Secret Recipe For a Viral Horror Story

The Hero’s Journey Infographic

Read More: What Your 6 Favorite Movies Have in Common

Emotional Self Care Guide Infographic

Source: Carley Schweet

Want to look at more amazing data visualization? Read More: 50+ Infographic Ideas, Examples & Templates for 2020 (For Marketers, Nonprofits, Schools, Healthcare Workers, and more)

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What Is Data Visualization and Why Is It Important?

The sheer amount of data generated today means we need new ways to understand what’s happening in order to take action faster. Every click, transaction, subscription, loyalty card swipe, and social media interaction contributes to a digital footprint that continues to grow exponentially. The result? A massive explosion of data that is revolutionizing the way we live and work. Data visualization, in particular, plays a critical role in presenting data in a meaningful and understandable format. By using a visual representation of data , it’s much easier to identify patterns, trends, and relationships that may not be immediately apparent when sifting through large data sets.

Here’s what we’ll cover in this guide to data visualization: 

• Data Visualization Definition 

Benefits of Data Visualization

Why data visualization is important .

• Types of Data Visualization and Examples
• Evaluating Data Visualization Tools
• Take the Next Step and Start Analyzing With Data Visualization 

‍ Data Visualization Definition

Data visualization is the process of transforming raw data into visual formats, such as charts, graphs, or maps, to help identify patterns, trends, and insights that might not be apparent from numerical data alone. 

Additionally, it enables data to be more accessible, understandable, and impactful, especially when communicating with stakeholders, investors, or team members who may not be familiar with the data.

For example, data visualization could help:

• In retail, gaining insights into customer behavior, purchase patterns, and product performance.
• In finance, monitoring market trends, tracking portfolio performance, and conducting risk analysis. 
• In public health, showing the geographical distribution of outbreaks and helping track the spread of infectious diseases.
• In supply chain industries, tracking inventory levels, monitoring logistics operations, and optimizing resource allocation. 
• In sports, evaluating player performance, game strategies, and match statistics.
• In education, tracking student performance, analyzing learning outcomes, and identifying areas for improvement.

Data visualization has several benefits for businesses including: the ability to process information faster, identify trends at scale, and make data more digestible. Companies regularly use data to make decisions, and through data visualization, can find insights quickly and move to action. Data visualization specifically helps with the following:

• Visualizing patterns and relationships
• Storytelling, including specifically data storytelling
• Accessibility to information 

Exploration

Let’s take a look at each of these benefits in detail. 

‍ Visualize patterns and relationships

Data visualization constitutes an excellent method for the discernment of interconnections and patterns amidst vast collections of information. For example, a scatter plot can be used to display the relationship between two variables, such as the correlation between temperature and sales. This enables users to understand the relationship and identify trends and outliers more quickly and easily.

Read a guide of Sigma’s visual library.

Storytelling

Your audience, whether it's coworkers or clients, want to hear a coherent story from your data. Storytelling with data cannot be done successfully without visualizations. Colorful charting and dynamic pivots are just as important as characters and plots are in a traditional story, so using them to communicate information makes data that much more engaging and memorable for audiences. Data can be complex and convoluted for some audiences, so data storytelling is an approach to convey important information effectively through a captivating narrative. Good visualizations are a vital part of that narrative.  

For example, if an analyst is investigating the performance of e-commerce sales for their retail company over time, they may leverage several data sources such as spreadsheets, calculations, code, etc. to do so. However, when they report these new insights to their stakeholders, the analyst will need to summarize and communicate their findings in a digestible way. 

An easy way the analyst could do this is by using the data to create a map of the U.S. with a color gradient overlaying every state that is lighter or darker based on its total sales volume. This visual story tells the least and most successful retail locations at a glance.

Accessibility / Easily Share Information

Data visualization serves as an invaluable mechanism for the facilitation of accessibility, allowing for the communication of information amongst individuals, even for those who may not usually engage with data , which broadens the audience.

Visualizations help simplify complex information by leveraging people’s ability to naturally recognize patterns. A viewer typically does not have to be taught that bigger means more and that smaller means less. In a case where an analyst wants to highlight the difference in scale between one product’s profitability vs. another, a bar chart can clearly show the user which product is more profitable and by how much, making it easy for even non-technical team members to understand and compare the performance of different products.

Exploration is a key component of successful data visualization. The more flexible charting and dashboarding is, the more follow-up questions end users can ask directly of their data. For example, an interactive dashboard can be used to explore retail sales data over time, enabling users to filter and drill down into the data to identify trends and patterns.

Data visualization exploration is often associated with the concept of “drill downs.” Drill downs in data visualization refer to the process of starting with an overview of data and then narrowing the focus to more specific aspects of it. As an example, one might start with a visualization of global climate data and drill down to data about a specific country, a specific state, a specific city, or even a specific neighborhood within that city. Each drill down reveals more precise, detailed, and nuanced information. 

The main goal of data visualization is that it helps unify and bring teams onto the same page. The human mind is wired to grasp visual information more effortlessly than raw data in spreadsheets or detailed reports. Thus, graphical representation of voluminous and intricate data is more user-friendly. Data visualization offers a swift and straightforward method to communicate ideas in a universally understood format, with the added benefit of enabling scenario testing through minor modifications.

By translating information into visual form, it ensures everyone, irrespective of the complexity of the data or the depth of the analysis, can share a unified understanding. Any industry can benefit from using data visualization, because pretty much every industry relies on data to power it. That includes finance, marketing, consumer goods, education, government, sports, history, and many more. ‍ Another thing to keep in mind is that data visualization can be a double-edged sword. For example, charts can be manipulated and skewed to force a desired outcome. Ungoverned, static, desktop tools can become the wild west in suggesting an inaccurate outcome “proven by data.” Even in the cases where the visualization builder is acting in good faith, there are still pitfalls to watch out for. Always be considerate of:

• Individual outliers having an outsized impact, skewing the visual direction of a chart
• The need for for business users to see the underlying data
• Allowing for transparency down to row-level detail in data sets

Types of Data Visualizations & Examples

There is a long list of types of data visualization techniques and methods that can be used to represent data. While no type of data visualization is perfect, we’ll walk through different examples and when to apply each one. 

We’ll be looking at:

• Line charts and area charts
• Scatter plots 
• Pivot tables
• Box-and-whisker plots
• Sankey charts 

Tables, although more commonly thought of as a data source, can also be considered a type of data visualization. Especially when conditional formatting is applied to the table’s rows and columns, the data within the table becomes more visually engaging and informative. With conditional formatting, important insights and patterns can be highlighted, making it easier for viewers to identify trends and outliers at a glance. Additionally, tables offer a structured and organized way to present information, allowing for a comprehensive comparison of data points, which further enhances data understanding and analysis. ‍ For example, Sigma’s UI is based on a spreadsheet-like interface, which means almost everything in Sigma begins in a table format. That said, you can also create visual tables that display a smaller amount of data in order to tell a clearer story. In data visualization, tables are a simplified way of representing this interface. 

When to use tables:

• For detailed numeric comparisons, or when precision of data is key
• For displaying multidimensional data; tables can handle this complexity quite well

When to avoid tables: 

• When patterns, trends, or relationships need to be highlighted at a glance
• When dealing with large amounts of data

Pie charts —similar to stacked bar charts—are useful for displaying categorical data, such as market share or customer demographics. Pie charts are often used to display data that can be divided into categories or subgroups, and to show how each category or subgroup contributes to the whole. For example, a pie chart could be used to show the proportion of sales for different product categories in a given period of time, or the percent of a company's revenue broken down by various regions.

When to use pie charts:

• You want to display a proportion or percentage of a whole
• You’re visualizing only seven categories or less

When to avoid pie charts:

• You’re visualizing more than seven categories
• You want to compare something with more details, rather than just proportion
• You want to display and pinpoint exact values 

A bar chart, or bar graph, constitutes a variety of graphs that employ rectangular bars to depict data. These bars can be oriented either horizontally or vertically, with their extent being directly proportional to the numerical values they are intended to embody. Predominantly utilized for juxtaposing data across disparate categories or illustrating shifts in data over temporal progressions, bar charts offer a straightforward, yet potent means of conveying information visually. They frequently function as the initial tool in the exploratory process of data investigation.

When to use bar charts:

• Emphasizing and contrasting different sets of data, making the disparities or similarities between categories clear
• To display a subset of a larger dataset

When to avoid bar charts: 

• When a particular field encompasses an overwhelming variety of data types
• When the differences between fields are too subtle, or when these differences exist on different scales, as it could lead to confusion or misinterpretation

Line Charts & Area Charts

Line charts and area charts are two types of charts that are commonly used to visualize data trends over time. A line chart, also called a line graph, is a distinct type of graphical representation that exhibits information in the form of a multitude of data points, which are interconnected by unbroken lines. These line charts are typically employed to demonstrate transformations in data over a certain duration, where the horizontal axis symbolizes time, and the vertical axis signifies the values under scrutiny. Furthermore, they can serve to juxtapose several series of data within the same chart, or to graphically illustrate predicted time periods. 

For example, a line chart can be used to visualize a company's stock prices over the course of a year. Similarly, an area chart can be used to visualize the temperature changes over a day.

When to use line charts:

• When you’re displaying time-based continuous data 
• When you have multiple series or larger datasets 

When to avoid line charts:

• When you have smaller datasets, bar charts are likely a better way to present the information 
• Avoid when you need to compare multiple categories at once

When to use area charts:

• When you want to display the volume of the data you have 
• When comparing data across more than one time period 

When to avoid area charts:

• Avoid if you need to compare multiple categories, as well as when you need to examine the specific data value

Scatter Plots

A scatter plot , also called a scatter chart or scatter graph, is a specialized form of chart that demonstrates the correlation between two distinct variables by mapping them as a succession of individual data points. Each data point denotes a combined value of the two variables, with its specific placement within the chart dictated by these values.

Scatter charts prove instrumental in discerning patterns and trends within data, and they also help us understand how strong and in what direction the relationship is between two variables. They also serve as effective tools for identifying outliers, or those data points that deviate significantly from anticipated values based on the pattern displayed by other data points. These charts find widespread use across a range of fields including, but not limited to, statistics, engineering, and social sciences, for the purpose of analyzing and visualizing intricate data sets. In the realm of business, they are frequently utilized to identify correlations between different variables, for instance, examining the relationship between marketing outlays and resultant sales revenue. ‍ For example, a scatter plot might be used to visualize the relationship between the age and income of a group of people. Another example would be to plot the correlation between the amount of rainfall and the crop yield for a particular region.

When to use scatter plots:

• Highlight correlations within your data
• They are useful tools for statistical investigations
• Consider scatter plots to reveal underlying patterns or trends

When to avoid scatter plots:

• For smaller datasets, scatter plots may not be optimal
• Avoid scatter plots for excessively large datasets to prevent unintelligible data clustering
• If your data lacks correlations, scatter plots may not be the best choice

Pivot Tables

While pivot tables may not be what first comes to mind for data visualization, they can give important context with hard numbers and provide strong visual indicators through formatting. ‍ Pivot tables can also be enhanced with conditional formatting to provide color scales that make performance trends more visible. Data bars can also be added to cells to run either red or green for positive and negative values. 

When to Use Pivot Tables:

• Cohort analysis performance trends or portfolio analysis with a mix of positive and negative values

What Not to Use Pivot Tables:

• When your dataset is too large to get a good understanding of the whole
• When data can easily be summarized with a bar chart instead

An example of a pivot table, where colors are used to show positive or negative progress on a company’s portfolio. The user can pivot the table to show multiple categories in different ways.

A heat map is a type of chart that uses color to represent data values. It is often used to visualize data that is organized in a matrix or table format. The color of each cell in the matrix is determined by the value of the corresponding data point. Heat maps are best used when analyzing data that is organized in a two-dimensional grid or matrix.

For example, a heat map can be used to visualize a company's website traffic, where the rows represent different pages on the website, and the columns represent different periods of time.

When to use heat maps:

• When you need to visualize the density or intensity of variables
• When you want to display patterns or trends over time or space 

When to avoid heat maps:

• When precise values are needed; heat maps are better at showing relative differences rather than precise values
• When working with small data sets 

A tree map is a type of chart that is used to visualize hierarchical data. It consists of a series of nested rectangles, where the size and color of each rectangle represent a different variable. Tree maps are best used when analyzing data that has a hierarchical structure.

For example, a tree map can be used to visualize the market share of different companies in an industry. The largest rectangle would represent the entire industry, with smaller rectangles representing the market share of each individual company.

When to use tree maps:

• When you want to visualize hierarchical data
• When you need to illustrate the proportion of different categories within a whole 

When to avoid tree maps:

• When exact values are important
• When there are too many categories

Box-and-Whisker Plots

Box plots are useful for quickly summarizing the distribution of a dataset, particularly its central tendency and variability. For example, a box-and-whisker plot can be used to visualize the test scores of a group of students. 

Colloquially recognized as a box-and-whisker plot, a box plot is a distinct form of chart that showcases the distribution of a collection of numerical data through its quartile divisions. Box plots serve as efficient tools for rapidly encapsulating the distribution of a dataset, specifically its central propensity and variability. 

A box-and-whisker plot consists of a rectangle (the "box") and a pair of "whiskers" that extend from it. The box embodies the middle 50% of the data, with the lower boundary of the box signaling the first quartile (25th percentile) and the upper boundary of the box indicating the third quartile (75th percentile). The line situated within the box signifies the median value of the data. The whiskers project from the box to the minimum and maximum values of the data, or to a designated distance from the box referred to as the "fences." Any data points that reside outside the whiskers or fences are categorized as outliers and are plotted as individual points. When to use box plot charts:

• When you want to display data spread and skewness
• When showcasing the distribution of data, including the range, quartiles, and potential outliers
• When comparing multiple groups or categories side-by-side; they allow for easy comparison of different distributions.

When to avoid box plot charts:

• If you need to show more detail, since box plots focus on a high-level summary 
• When individual data points are important to the story you’re telling
• When your audience isn’t familiar with them, since they can sometimes be less intuitive than other types of visualizations

A histogram is a type of chart that displays the distribution of a dataset. It consists of a series of vertical bars, where the height of each bar represents the number of observations in a particular range. Histograms are best used when analyzing continuous data. It’s used the most when you want to understand the frequency distribution of a numerical variable, like height, weight, or age. For example, a histogram can be used to visualize the distribution of heights in a population. Read more about building histograms in Sigma here.

When to Use a Histogram:

• When understanding the shape of a distribution; for example, whether it’s symmetric, skewed to the left or right, or bimodal
• When identifying outliers, like which data points are significantly different from the rest of the data
• When comparing distribution of a variable across different groups, such as males and females, or different age groups.
• To set boundaries for data ranges; for example, you might use a histogram to determine what constitutes a "normal" or "abnormal" value for a particular variable

When to Avoid a Histogram:

• When you need to look at multiple dimensions at the same time
• If your data isn’t all on the same scale

Sankey Charts

We end our guide with the controversial Sankey chart. A Sankey chart is a type of diagram that illustrates the movement or transfer of data, resources, or quantities through various stages of a system or process. Common applications of Sankey charts include visualizing complex sequences like energy usage, material distribution, or even a website's user journey. The structure of the chart includes nodes and links—with nodes representing the starting points, endpoints, or intermediate steps, and links depicting the transition of quantities or data between these nodes.

The thickness of the links in a Sankey chart directly corresponds to the volume of data or resources being moved, offering an intuitive comparison of the relative sizes of these transfers. They can be invaluable for recognizing inefficiencies, bottlenecks, or potential areas for enhancement in a system or process. These charts serve as a powerful tool for communicating complex information in a straightforward and comprehensible way. However, if there are too many nodes or links, Sankey charts can become cluttered and challenging to interpret, hence their use should be considerate and targeted.

‍ When to use Sankey charts:

• When you want to show the data as part of a process

When to avoid Sankey charts:

• When it starts to feel too confusing, which can quickly happen when there are too many nodes or links
• When you need to see exact values, it might not be the most intuitive option. 

Evaluating Data Visualization Tools 

Data visualization tools have become increasingly popular in recent years, with a wide variety of options available to choose from. However, determining which tool best suits your needs can be challenging with so many options. When evaluating data visualization tools, there are several key questions to consider:

• What are your goals and needs?   It's crucial to clearly understand your goals and needs before selecting a data visualization tool. Are you looking to explore your data, communicate a specific message, or both? Understanding your objectives will help you choose the right tool for your project.
• What features do you require?   Different data visualization tools come with different features. Before selecting a tool, you should consider what features you need to achieve your goals. For example, do you require interactive capabilities or the ability to create custom visualizations?
• Where will your data come from?   The source of your data is another critical factor to consider when selecting a data visualization tool. Some tools are better suited for specific types of data, such as structured or unstructured data, while others may require specific file formats or data storage solutions.
• Where will you need to see your data?   Different data visualization tools may be more suitable for specific platforms or devices. For example, some tools may be optimized for mobile devices, while others are designed for desktop computers or specific web browsers. You may also be interested in embedding visualizations elsewhere , such as internal applications or external portals.
• Where would you like to publish your visualization?   Finally, consider where you would like to publish your visualization. Some tools may provide built-in publishing capabilities, while others may require you to export your visualization to a separate platform. Selecting a tool that supports your publishing needs is important to ensure your visualization reaches your intended audience.

By considering these key questions, you can evaluate different data visualization tools and select the one that best meets your needs.

Read a side-by-side comparison of Sigma against similar BI tools.

Take the Next Step & Start Analyzing With Data Visualization

Data visualization is a powerful tool for understanding and communicating complex data. While there are many data visualization tools on the market, Sigma offers an intuitive and familiar spreadsheet interface that allows users to easily explore, analyze, and collaborate on their data. 

Explore Sigma’s capabilities and start transforming your data today via a free trial of Sigma .

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What Is Data Visualization and Why Is It Important? A Complete Introduction

They say a picture is worth a thousand words, and this is especially true for data analytics.

Data visualization is all about presenting data in a visual format, using charts, graphs, and maps to tell a meaningful story. It’s a crucial step in the data analysis process—and a technique (or art form!) that all areas of business can benefit from.

In this guide, we’ll tell you everything you need to know about data visualization (also known as data viz). We’ll explain what it is, why it matters, some of the most common types, as well as the tools you can use to create them.

This guide is ideal for anyone who wants to present, communicate, and share data-driven insights.

If you’d like to learn more data analytics skills, try this free data short course .

• What is data visualization?
• Why is data visualization important?
• When should you visualize your data? 
• Different types of data visualization and when to use them
• Top data visualization tools
• Best practices and principles for effective data visualization
• Getting started with data visualization

So: What is data visualization? Let’s start with a definition.

1. What is data visualization? A definition

Data visualization is the graphical or visual representation of data. It helps to highlight the most useful insights from a dataset, making it easier to spot trends, patterns, outliers, and correlations.

Imagine you’re presented with a spreadsheet containing rows and rows of data. You probably won’t be able to decipher the data without delving into it, and it’s unlikely that you’ll be able to spot trends and patterns at first glance.

Now imagine seeing the same data presented as a bar chart, or on a color-coded map. It’s much easier to see what the data is telling you, right?

That’s the whole point of data visualization. It makes insights visible to the naked eye, so that virtually anyone can see and understand what’s going on. When done well, data visualization tells a story.

This storytelling aspect is crucial as it makes your data actionable. There’s a huge difference between simply having lots of data versus actually understanding how to use it to drive actions and decisions—and data visualization bridges that gap.

There are two broad categories of data visualization: exploration and explanation. Let’s take a look at those now.

What are the two main types of data visualization? Exploration vs. explanation

We’ll look at specific types of data visualization later on, but for now, it’s important to distinguish between exploratory and explanatory data visualization.

In a nutshell, exploratory data visualization helps you figure out what’s in your data, while explanatory visualization helps you to communicate what you’ve found. Exploration takes place while you’re still analyzing the data, while explanation comes towards the end of the process when you’re ready to share your findings.

Exploration

When faced with a new dataset, one of the first things you’ll do is carry out an exploratory data analysis . This is where you investigate the dataset and identify some of its main features, laying the foundation for more thorough analysis.

At this stage, visualizations can make it easier to get a sense of what’s in your dataset and to spot any noteworthy trends or anomalies. Ultimately, you’re getting an initial lay of the land and finding clues as to what the data might be trying to tell you.

Explanation

Once you’ve conducted your analysis and have figured out what the data is telling you, you’ll want to share these insights with others.

These could be key business stakeholders who can take action based on the data, for example, or public audiences who have an interest in your topic area.

Explanatory data visualizations help you tell this story, and it’s up to you to determine which visualizations will help you to do so most effectively. We’ll introduce some of the most common types of data visualization (and when to use them) in section four.

Want to learn more about data visualization, and try your hand at creating visualizations of your own?  Give this free introductory tutorial a go. We’ll show you, step by step, how to create bar charts, line graphs, and more for a real dataset in Google Sheets.

2. Why is data visualization important?

The importance of effective data visualization is rooted in the importance of data analytics in general.

We’re living in an increasingly data-rich world; at the start of 2020, the digital universe comprised approximately 44 zettabytes of data . For perspective, one zettabyte is roughly equal to a trillion gigabytes. By 2025, it’s estimated that around 463 exabytes of data will be created every 24 hours across the globe. An exabyte is equivalent to one billion gigabytes. Basically, we’re producing tons and tons of data all the time.

Data analytics allows us to make sense of (at least some of) that data. From a business perspective, it enables companies to learn from the past and plan ahead for the future. In fields like healthcare, it can help to improve patient care and treatment. In finance and insurance, it can help to assess risk and combat fraudulent activity. Essentially, we need data analytics in order to make smart decisions—and data visualization is a crucial part of that.

Data visualization helps us to understand what certain data is telling us, presenting it in a way that’s accessible to a range of audiences—not just data experts. It’s how you bridge the gap between your expertise as a data analyst or data scientist, and those people who can use or act upon the insights you discover.

A line graph and a bar chart taken from the Fitbit app.

The advantages and benefits of effective data visualization at a glance

Data visualization allows you to:

• Get an initial understanding of your data by making trends, patterns, and outliers easily visible to the naked eye
• Comprehend large volumes of data quickly and efficiently
• Communicate insights and findings to non-data experts, making your data accessible and actionable
• Tell a meaningful and impactful story, highlighting only the most relevant information for a given context

Now we know what data visualization is and why it matters, let’s take a look at when and why you might need to visualize your data.

3. When should you visualize your data?

Aside from exploratory data visualization which takes place in the early stages, data visualization usually comprises the final step in the data analysis process . To recap, the data analysis process can be set out as follows:

• Define the question: What problem are you trying to solve?
• Collect the data: Determine what kind of data you need and where you’ll find it.
• Clean the data: Remove errors, duplicates, outliers, and unwanted data points—anything that might skew how your data is interpreted. You can learn more about data cleaning (and how to do it) in this guide .
• Analyze the data: Determine the type of data analysis you need to carry out in order to find the insights you’re looking for.
• Visualize the data and share your findings: Translate your key insights into visual format (e.g. graphs, charts, or heatmaps) and present them to the relevant audience(s).

Essentially, you visualize your data any time you want to summarize and highlight key findings and share them with others. With that in mind, let’s consider what kinds of insights you can convey with data visualizations.

What is data visualization used for?

Within the broader goal of conveying key insights, different visualizations can be used to tell different stories. Data visualizations can be used to:

• Convey changes over time: For example, a line graph could be used to present how the value of Bitcoin changed over a certain time period.
• Determine the frequency of events: You could use a histogram to visualize the frequency distribution of a single event over a certain time period (e.g. number of internet users per year from 2007 to 2021). Learn how to create a histogram in this guide .
• Highlight interesting relationships or correlations between variables: If you wanted to highlight the relationship between two variables (e.g. marketing spend and revenue, or hours of weekly exercise vs. cardiovascular fitness), you could use a scatter plot to see, at a glance, if one increases as the other decreases (or vice versa).
• Examine a network: If you want to understand what’s going on within a certain network (for example, your entire customer base), network visualizations can help you to identify (and depict) meaningful connections and clusters within your network of interest.
• Analyze value and risk: If you want to weigh up value versus risk in order to figure out which opportunities or strategies are worth pursuing, data visualizations—such as a color-coded system—could help you to categorize and identify, at a glance, which items are feasible.

So far, we’ve taken a rather broad, high-level look at data visualization. Now let’s drill down to some specific types of data visualization and when to use them.

An example of data visualization, as seen in the Fitbit app.

4. How to visualize your data: Different types of data visualization (and when to use them)

There are many different options when it comes to visualizing your data. The visualization you choose depends on the type of data you’re working with and what you want to convey or highlight. It’s also important to consider the complexity of your data and how many different variables are involved. Not all types of data visualization lend themselves to elaborate or complex depictions, so it’s important to choose a suitable technique.

Before we explore some of the most common types of data visualization, let’s first introduce five main data visualization categories.

Five data visualization categories

When considering the different types of data viz, it helps to be aware of the different categories that these visualizations may fall into:

• Temporal data visualizations are linear and one-dimensional. Examples include scatterplots, timelines, and line graphs.
• Hierarchical visualizations organize groups within larger groups, and are often used to display clusters of information. Examples include tree diagrams, ring charts, and sunburst diagrams.
• Network visualizations show the relationships and connections between multiple datasets. Examples include matrix charts, word clouds, and node-link diagrams.
• Multidimensional or 3D visualizations are used to depict two or more variables. Examples include pie charts, Venn diagrams, stacked bar graphs, and histograms.
• Geospatial visualizations convey various data points in relation to physical, real-world locations (for example, voting patterns across a certain country). Examples include heat maps, cartograms, and density maps.

With those categories in mind, let’s explore some of the most common types of data visualization.

Five common types of data visualization (and when to use them)

In this section, we’ll introduce some useful types of data visualization. We’ll also point you to our more comprehensive guide where you can learn about additional data visualization methods and how to use them.

1. Scatterplots

Scatterplots (or scatter graphs) visualize the relationship between two variables. One variable is shown on the x-axis, and the other on the y-axis, with each data point depicted as a single “dot” or item on the graph. This creates a “scatter” effect, hence the name.

Source: displayr.com

Scatterplots are best used for large datasets when there’s no temporal element. For example, if you wanted to visualize the relationship between a person’s height and weight, or between how many carats a diamond measures and its monetary value, you could easily visualize this using a scatterplot.

It’s important to bear in mind that scatterplots simply describe the correlation between two variables; they don’t infer any kind of cause-and-effect relationship.

2. Bar charts

Bar charts are used to plot categorical data against discrete values.

Categorical data refers to data that is not numeric, and it’s often used to describe certain traits or characteristics. Some examples of categorical data include things like education level (e.g. high school, undergrad, or post-grad) and age group (e.g. under 30, under 40, under 50, or 50 and over).

Discrete values are those which can only take on certain values—there are no “half measures” or “gray areas.” For example, the number of people attending an event would be a discrete variable, as would the number of sales made in a certain time period (think about it: you can’t make “half a sale” or have “half an event attendee.”)

Source: chartio.com

So, with a bar chart, you have your categorical data on the x-axis plotted against your discrete values on the y-axis.

The height of the bars is directly proportional to the values they represent, making it easy to compare your data at a glance.

3. Pie charts

Just like bar charts, pie charts are used to visualize categorical data.

However, while bar charts represent multiple categories of data, pie charts are used to visualize just one single variable broken down into percentages or proportions. A pie chart is essentially a circle divided into different “slices,” with each slice representing the percentage it contributes to the whole.

Thus, the size of each pie slice is proportional to how much it contributes to the whole “pie.”

Imagine you have a class of thirty students and you want to divide them up based on what color t-shirt they’re wearing on a given day.

The possible “slices” are red, green, blue, and yellow, with each color representing 40%, 30%, 25%, and 5% of the class total respectively. You could easily visualize this using a pie chart—and the yellow slice (5%) would be considerably thinner than the red slice (40%)! Pie charts are best suited for data that can be split into a maximum of five or six categories.

4. Network graphs

Not all data is simple enough to be summarized in a bar or pie chart. For those more complex datasets, there are a range of more elaborate data visualizations at your disposal—network graphs being one of them.

Network graphs show how different elements or entities within a network relate to one another, with each element represented by an individual node. These nodes are connected to other, related nodes via lines.

Source: networkofthrones.wordpress.com

Network graphs are great for spotting and representing clusters within a large network of data.

Let’s imagine you have a huge database filled with customers, and you want to segment them into meaningful clusters for marketing purposes. You could use a network graph to draw connections and parallels between all your customers or customer groups.

With any luck, certain clusters and patterns would emerge, giving you a logical means by which to group your audience.

5. Geographical maps

Geo maps are used to visualize the distribution of data in relation to a physical, geographical area.

For example, you could use a color-coded map to see how natural oil reserves are distributed across the world, or to visualize how different states voted in a political election. Maps are an extremely versatile form of data visualization, and are an excellent way of communicating all kinds of location-related data.

Some other types of maps used in data visualization include dot distribution maps (think scatterplots combined with a map), and cartograms which distort the size of geographical areas to proportionally represent a given variable (population density, for example).

Source: pmfias.com

Here, we’ve introduced just a handful of data visualization types. If you want to learn more, check out our complete guide to different types of data visualization and when to use them .

5. Top data visualization tools

When it comes to creating informative, eye-catching visualizations, there are plenty of tools at your disposal.

When choosing a tool, it’s important to consider your needs in terms of the kinds of visualizations you want to create, as well as your own technical expertise; some tools will require coding knowledge, while others are more suited to non-technical users.

In this section, we’ll briefly introduce some of the most popular data visualization tools. If you’re on the market for a data viz tool and want a more thorough comparison, this guide to the seven best data visualization tools will help you. For now, here are our top three data viz tools to get familiar with:

• Plotly: Open-source software built on Python. Plotly is ideal if you’ve got some coding knowledge and want to create highly customizable visualizations.
• D3.js: A free, open-source data viz library built using JavaScript. As with Plotly, you’ll need some programming knowledge in order to use this data viz tool.
• Tableau: Perhaps one of the most popular data analytics tools , Tableau is known for its user-friendliness—you don’t need any coding knowledge to create beautiful visualizations in Tableau. And, unlike some other BI tools, it’s good at handling large volumes of data.

Before deciding on a tool, it’s worth trying out a few options. The good news is that there are plenty of data viz tools on the market— as well as a number of free tools —allowing you to create beautiful and informative visualizations—even if you’re a newcomer to the field.

What are data dashboards?

Dashboards are another useful tool for data tracking and visualization. A data dashboard essentially allows you to keep track of multiple data sources, visualizing them in one single location for easy viewing.

A common example is the Google Analytics dashboard , which displays a whole host of visualizations on one page—a geo map showing where your website visitors are located, for example, or a pie chart showing what percentage of your users access your website using specific devices.

If you want multiple stakeholders to be able to access and view certain data insights, a dashboard can help you to create a single hub with easy-to-understand visualizations.

A snapshot of a data dashboard, taken from Google Analytics.

6. What are some data visualization best practices?

Data visualization truly is an art form—but the goal is always, first and foremost, to provide valuable information and insights.

If you can do this by way of beautiful visualizations, you’re onto a winner. So, when creating data visualizations, it’s important to adhere to certain best practices.

These will help you strike the right balance, keeping your audience engaged and informed. Here’s how to excel at data visualization.

1. Define a clear purpose

Like any data analytics project, it’s important to define a clear purpose for your data visualizations.

What are the priorities in terms of what you want to convey and communicate? What should your audience take away from your visualization? It’s essential to have this defined from the outset; that way, you can ensure that you’re only presenting the most valuable information—and giving your audience something they can use and act upon.

2. Know your audience

The purpose of data visualization is to communicate insights to a specific audience, so you’ll want to give some thought to who your audience is and how familiar they are with the information you’re presenting.

What kind of context can you provide around your visualizations in order to help your audience understand them? What types of visualization are likely to be most accessible to this particular group of people? Keep your audience in mind at all times.

3. Keep it simple

When creating visualizations, it’s often the case that less is more.

Ultimately, you want your visualizations to be as digestible as possible, and that means trimming away any unnecessary information while presenting key insights clearly and succinctly. The goal is to keep cognitive load to a minimum—that is, the amount of “brainpower” or mental effort it takes to process information.

Even if the data is complex, your visualizations don’t have to be, so strive for simplicity at all times.

4. Avoid distorting the data

You should strive to present your findings as accurately as possible, so avoid any kind of visual “tricks” that could bias how your data is perceived and interpreted.

Think about the labels you use, as well as how you scale your visualizations. For example, things like “blowing up” certain data segments to make them appear more significant, or starting your graph axis on a number other than zero are both bad practices which could mislead your audience. Prioritize integrity and accuracy!

5. Ensure your visualizations are inclusive

Last but by no means least, make sure that your visualizations are accessible and inclusive.

Think about how colors, contrasts, font sizes, and the use of white space affect the readability of your visualization. Is it easy for your users to distinguish between the data and see what’s going on, regardless of whether they have twenty-twenty vision or a visual impairment?

Inclusivity and accessibility are central to good data visualization, so don’t overlook this step.

7. Getting started with data visualization

By now, you hopefully have a good understanding of what data visualization is and why it matters.

Of course, the best way to get to grips with it is to see it in action. Check out our round-up of some of the most beautiful and informative data visualization examples from around the web.

Keen to give it a go yourself? Why not download a free dataset and see what you can do! If you’d like to learn it more, then check out this list of data visualization courses out there to try.

Data visualization is an excellent skill to have, whether you’re forging a career in the data industry or just want to share valuable insights with your colleagues. If you are pursuing a career as a data analyst or data scientist, be sure to include data visualizations in your data portfolio —it’s something that employers will be looking out for.

CareerFoundry’s  Data Visualizations with Python course is designed to ease you into this vital area of data analytics. You can take it as a standalone course as well as a specialization within our full Data Analytics Program, you’ll learn and apply the principles of data viz in a real-world project, as well as getting to grips with various data visualization libraries.

Want to learn more? Try your hand at this free, introductory data analytics short course , and check out the following guides:

• What is data quality and why is it important?
• What is web scraping? A beginner’s guide
• An introduction to multivariate analysis

• Documentation

Data Visualization Essentials: Tips, Techniques, and Tools

Written by Tom Czaban   |  May 17, 2023

Table of Contents

What Is Data Visualization?

Why is data visualization important, types of data visualization, out-of-the-box visualizations, fully-customized visualizations, what is real-time data visualization, what is interactive data visualization, advantages and disadvantages of data visualization, what tools do i need for data visualization, examples of data visualization, data visualization best practices, ready to get started with data visualization, discover more about data visualization.

Data visualization is the use of visual representations to display information. This definition might sound modern, but data visualization has been around for centuries. One of the earliest and most obvious examples is maps, which developed out of the need to graphically display geographic data. Since then data visualization has continued to develop to meet the needs of today’s users.

There are multiple ways to visualize data (including charts, graphs, and infographics), and technology is constantly evolving to present information in more eye-catching and useful ways. Examples of this include making visualizations interactive and allowing the end user to filter and display different metrics. Regardless of these updates, the aim remains the same: to present key insights and make it easier to engage with and understand data.

Any discussion of the meaning of data visualization would be incomplete without a mention of creating dashboards . It is important to note that although data visualization and dashboards are closely related they are not in fact the same thing. The aim of a dashboard is to offer an overview of the key performance indicators (KPIs) of the presented area. Typically a dashboard will contain multiple visualizations, which together provide an overview of the key insights — as shown in the image below.

Data visualization helps to ensure data insights aren’t lost in delivery; most of us can’t process big blocks of statistics, our brains aren’t built like that. Anyone who has looked at a long list of numbers will understand the disconnect this can cause. Graphical representation solves this pain point by making statistics and data easier to absorb.

Data visualization is not only about creating simple and attractive visuals. It can be used to create insights by identifying patterns and trends that would otherwise be difficult to spot. Displaying a set of data on a scatter plot , for example, might reveal connections between outliers that previously went unnoticed when the statistics were in a table.

Data visualization is also an important business intelligence (BI) tool, allowing companies to effectively communicate their data and improve decision-making. High-quality visualizations can help an organization promote a data culture because the insights needn’t be explained to non-technical end users. Decisions can be made with increased accuracy and speed because everyone is on the same page. As well as improving internal processes, visualizations help to increase external engagement by making the data more accessible to partners and customers.

There are different types of visualizations to choose from, and each is better suited to showcasing certain attributes and metrics. It is important to use the visualization that makes the most sense for the insights you aim to convey. Below is a brief overview of the visualizations you might choose for some typical scenarios. For more on this, check out our post on how to choose the best chart type to visualize your data .

There are instances when you may need to display one key figure , for example, the number of customers, or the number of returned items. A KPI visualization is best suited to this purpose because it shows one big number. However, this number will mean nothing on its own; you have to, at the very least, provide a date range and compare it with another metric to give it some context.

To show comparisons between categories , for example, the number of sales each staff member has made in the last month, it is best to use a bar chart or column chart . A stacked bar chart gives you the option to add another category, so as well as showing how many sales each staff member has made, you might also include the product type they sold by adding color and a key.

When comparing parts to the whole it is best to use a pie chart , donut chart , or treemap . An example of part-to-whole comparison is the number of people who answered ‘yes’ or ‘no’ to a specific question. Generally speaking, it is a bad idea to use a pie chart or donut chart for more than three categories because it becomes difficult for users to accurately absorb the data. With more categories, it is better to use a treemap.

To show changes over time the most effective options are line charts , area charts, or column charts. You might, for instance, choose one of these to display month-by-month revenue. If you want to add an additional category (such as product type) you can use a line chart with multiple lines or a stacked area chart. But it's best to tread carefully with these because they can become confusing if not properly executed.

To show the details of many items it is best to use a table. Some people avoid using tables because they seem too basic, but when you have many items (such as a lot of customer details) a table can be the right choice. Amid the myriad of visualization options available, tables can be quite striking when combined with other types of charts and graphs on a dashboard.

Most analytics platforms offer out-of-the-box visualizations that you can use to display your data. Out-of-the-box refers to the standard visualizations available to all customers who have purchased the BI tool. These options typically include tables, column charts, bar charts, line charts, donut charts, etc. Not all out-of-the-box offerings are created equal, so when choosing a BI tool, it is helpful if it has at least some of the following capabilities:

• An interface that makes it easy to change metrics, dimensions, or data visualization types from the out-of-the-box options, such as drag-and-drop.
• The ability to add filters and drill into visualizations.
• The option to adjust certain aspects to match your brand, for example, fonts, colors, and logos.
• The potential to populate visualizations with live data.

While you can do plenty with out-of-the-box visualizations, your options are limited to the standard charts and graphs that come with the tool. For this reason, you’re often better off with a solution that allows you more creative options. With custom visualization, you can tailor your visualizations to your exact needs. You are no longer limited to standard chart types, and the only barrier to creating highly original visuals is your imagination.

A good analytics platform achieves custom visualization by enabling access to third-party charting libraries, such as D3.js, Chart.js, Fusion Charts, Google Charts, and more. These libraries are the best route to creating advanced visuals that are graphically amazing.

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With real-time data visualization, users can see the data changing as it is being updated or generated. For example, they might see the height of bar charts changing, or colors adjusting themselves on a heatmap.

To create this kind of visualization, a company needs the ability to perform real-time data reporting. In other words, their data architecture must operate in real-time to build up-to-the-minute visuals. This architecture includes components such as data processing, data streaming, and all the logic of the defined analysis that leads to the displayed insights.

Real-time analytics and their visualization can be crucial under certain circumstances. On other occasions, this can be unnecessary and even confusing. Before deciding whether to employ automated real-time visualization, a company might ask themselves the following questions:

• Do we really need to see in-the-moment data to make decisions?
• Do we have enough new data at frequent intervals to necessitate real-time updates?
• Will these instant updates make our decision-making processes more confusing and create delays?
• Will real-time visualization help our users, i.e., what value will they get from the live updates?

Based on the above questions, scenarios where real-time visualizations may be beneficial include:

• Security and fraud prevention, e.g., when monitoring for major security breaches.
• A situation where a company needs to act promptly to a crisis.
• Goal monitoring (but only if these goals are affected by rapidly changing information)
• In financial teams – where it is crucial for team members to receive up-to-the-minute financial information (for example, stock markets).

Below is an example of real-time visualization in action:

On the surface, interactive data visualization tends to look similar to regular (or static) visualization. The difference is there is an option to click a button or move a slider, so the user can interact with the data, rather than just look at it. This ability to manipulate charts, graphs, and maps can positively influence user experience (UX).

Sometimes interactive features are custom-designed for a specific purpose, but generally speaking, the most common interactive features are:

• Filtering: Allows you to filter for the exact information required, highlighting the relevant data and reducing the data that is currently unimportant.
• Drilling: Enables you to move between different visualizations and send an action from the dashboard.
• Zooming and panning: Creates the possibility to hone in on a particular detail; you can zoom in on a specific part of the visualization and pan across it.

Interactive features can be extremely useful, for example, when users quickly need to answer specific queries, which is why they’re often used in BI reports. However, there are also times when a static visualization might be the best choice; for instance, when visualizations need to be printed and shared as reports, or when it is unnecessary for users to manipulate the information and all they need to do is to look at it. For more on this, check out our article that compares interactive and non-interactive visualizations using the example of world happiness levels.

While there are huge benefits to visualizing data, if not done properly the technique can spread misinformation. Below we look at some of the main advantages of data visualization and how to mitigate its downsides.

Benefits of data visualization

• Easy to spot trends. Visualization allows users to see patterns in the data they might otherwise have missed.
• Simple sharing of information. It is far easier to share data with charts, graphs, and infographics.
• Makes data accessible to non-technical users. With visualization, you no longer need to be a mathematician to understand the data insights.
• Easy to remember. Charts and graphs are not only easier to digest; they also tend to stay in the memory more easily than lists of numbers and statistics.
• Increase revenue. When all the decision-makers have the information at their fingertips, it empowers management to make quick and accurate decisions.

Problems with data visualization

• Information still needs to be accurate. Great visualizations [/blog/8-ways-turn-good-data-great-visualizations/] don’t make up for bad data. If best practices are not followed then visualizations can fall into the trap of becoming style over substance.
• Data visualization is an investment. Companies that want to effectively organize and visualize their data, or provide this ability to their customers, will either need a lot of involvement from analytics engineers (if they have the resources)), or an integrated analytics solution. Neither of these options comes without its costs, and pricing can vary depending on requirements. This then raises the question of whether to build the analytics solution in-house or buy off the shelf .
• Correlation does not equal causation. Visualizations often show the correlation between two or more metrics, so users often assume causation. But just because there is a correlation it doesn’t necessarily mean that one is caused by the other. There may be several other factors at play that aren’t included in the visualization.
• Users can still misinterpret the information. While visualization makes it easier for users to absorb data, it is still open to misinterpretation. For example, users might focus on the wrong thing when viewing it. This once again highlights the importance of using the right visualization type for the data displayed and the desired outcome.
• Confusing visualizations. Visualizations are supposed to simplify data, but if done badly they can make matters even more complicated. Perhaps the wrong chart type has been chosen, or there is too much information — as in the picture below.

The tools required to visualize data will depend on the project and the complexities of what you need to achieve. If you’d like to visualize some basic data for a presentation, you can use Excel to create some simple charts and graphs. If your data is more complex, you’ll need to create the insights first, which may involve data analysis or data mining. To achieve this, you will likely need to learn a programming language like Python. Alternatively, you can invest in a BI solution(s) such as those listed below.

• Chart generators or plugins: These tend to be used by developers and data engineers because the software requires a more advanced level of expertise. The plugins have many visualization types to choose from and there may even be a data-processing API that allows you to create actionable insights from your data. These tools usually have the capability to categorize and analyze basic data, and so can be used as the foundation of a company’s BI platform.
• Visualization reporting software: This is most often used by report developers and BI engineers. The software creates business and data analysis reports, which can then be turned into visualizations using a selection of built-in charts.
• A fully integrated BI and analytics solution: As the name suggests, this is the most complete solution. A good BI platform will allow you to easily explore data on your own, and create interactive dashboards and charts via a user-friendly no-code UI. The top solutions offer plug-and-play integrations, no-code tools, and flexible embedding options (such as React, Iframes, and Web Components) that allow you to seamlessly embed visualizations and dashboards into your product in a way that matches the brand.

To find out more about some of the best BI solutions with visualization tools, check out Gartner’s Magic Quadrant , which provides an objective analysis of the market leaders.

Data visualization has become so omnipresent that we use it every day without even noticing. Weather maps, bus schedules, computer audio levels, and fitness trackers – all employ visualizations to provide information in a more palatable way.

The above are just some of the ways companies use customer-facing visualizations to improve UX. But they also call upon data visualization to make internal decisions, for example, when looking at how to improve the supply chain, checking product sales across different countries, or how a specific marketing campaign or project is performing. Data visualization is also useful to get a better sense of external factors that might impact the company, such as the economic climate.

Data visualization use cases across different industries

Data visualization is used across all industries, from banking to healthcare . Below we look at how it can be used to improve processes in four different sectors.

• Software as a service (SAAS): Data is the beating heart of most software companies, and many of these embed analytics into their applications. They can then share the data with their customers through visualizations in a user-friendly way. A good example of this is Zendesk, whose software is all about improving the relationship between a business and its customers. Using advanced analytics and visualization tools, Zendesk gives their customers immediate access to the insights they need, which in turn speeds up response times and increases satisfaction among the clients of their customers.
• E-commerce: Data visualization helps brands and suppliers streamline their logistics and supply chains and optimize their operations. An e-commerce platform might use visualization to both improve the consumer experience and optimize brand performance. They could, for example, use their data to get a better understanding of how shoppers are using their site, helping them to better target customers and grow their business. Visualization is necessary to share these customer insights both internally and externally.
• Financial services: Financial firms leverage data and analytics to meet compliance requirements, manage risk, improve efficiency, and grow their business. Time is money, so the quicker they can make insights available to their employees and partners, the better. Visualization can also be used to help mitigate risk in real-time, and create highly personalized experiences for customers within financial service products.
• Insurance: Insurance firms rely on analysts to create impactful insights. These insights are then shared across the enterprise with people such as adjusters, underwriters, and marketers. Data visualization is crucial for insurance companies as it allows them to present clear insights, increase speed, and reduce inaccuracy.

With the right analytics platform, it is easy to create insights from your data and beautifully visualize them on dashboards. So why do some visualizations fail to achieve what they set out to do? The simple answer is that you need to be intentional about the information you want to convey. Good data visualization requires thoughtful human design. To that end, it can be useful to ask yourself the following questions to ensure best practice.

• What story am I trying to tell?

Visualization is a form of storytelling , only you’re using visuals instead of words. You need to find a clear way to tell the story to ensure it has the greatest impact. Data metrics and attributes must be relevant to the story that you are trying to tell. If you’re allowing users to filter, consider carefully how this will allow them to create their own stories from one source of truth.

• Have I designed the visualization for the viewer’s eye?

Once you’re clear on who the visualization is designed for and what you want them to take from it, you can focus on UX. Big-picture clarity is crucial, but it’s important not to neglect the details either. For example, what do the colors you’re using mean? Red usually implies danger, green is considered more positive. Can you customize the visualization to fit the brand? What about shortening numbers to make them easier to read (e.g., using K, M, B for thousands, millions, billions)?

• Am I trying to display too much data?

When it comes to data visualization, less is more. Although it’s tempting to present as many insights as possible, there is a fine line to be walked between insightful and overwhelming.

• Have I provided context?

Without a goal or a benchmark, users may miss the key insights. It is important to standardize benchmarks so that comparisons are not being made between two different things – which can lead to misleading takeaways.

The above are just some of the questions you might consider when creating visualizations. For a deeper dive, check out our article on visualization best practices .

Our analytics platform allows you to fully integrate real-time customized dashboards into your app or product. To see how this works and get your questions instantly answered, book a demo to help you decide whether this is the right tool for your needs. Alternatively, for some first-hand experience with data visualization, sign up for a free GoodData trial .

To learn more about data visualization and how it can help your business, you might like to check out some of our other resources:

Interactive vs. non-interactive data visualizations

7 ways to create great data visualizations

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What is data visualization

Why use data visualization, the advantages and benefits of good data visualization, principles of successful data visualization, how to choose a chart type, how to tell the visual story, designing the dashboard, why data visualization is important, reporting and visualization software tools comparison, looker studio by google, google sheets, key takeaways, what is data visualization: definition, principles, examples, tools.

Vlada Malysheva ,  Creative Writer @ OWOX

Top 30 Handpicked Google Looker Studio Dashboards for Marketers

65% of people are visual learners , making data visualization an effective way to communicate information.

When Excel spreadsheets aren’t enough to connect the dots between your data and there’s no possibility to involve data or digital analyst to get the report quickly, data visualization software tools and tools is what you need to become data-savvy.

In this article, which was last updated in January 2024, we’ll show you what data visualization techniques are available, how to visualize data correctly, which tools can be used for engaging and interactive visualizations without any help from developers or data professionals, and how to choose a tool that suits your specific needs.

The definition of  data visualization is the visual representation of your data. With the help of charts, maps, and other graphical elements these tools provide a simple and comprehensible way to clearly see and easily discover insights and patterns in your data.

Data visualization is the graphical representation of data using visual elements such as charts, graphs, and maps.

It is a way to communicate complex information in a visual and intuitive manner, making it easier for people to understand and analyze the data. By transforming raw data into visual representations, data visualization allows patterns, trends, and insights to be easily identified and interpreted.

Data visualization is also a powerful storytelling tool. Visual storytelling helps to uncover hidden patterns, relationships, and correlations that may not be apparent, or not visible in raw data. Through visualizations, data can be presented in a way that is engaging, impactful, and memorable, enabling effective communication and data-driven decision-making .

Data visualization is not limited to a specific field or industry. It's not only about marketing data and is used in various domains such as business, finance, healthcare, education, or journalism. In business, data visualization is used to analyze sales trends, key performance indicators, and present business metrics. In healthcare, it is used to visualize patient data, monitor disease outbreaks, and analyze medical research. In journalism, it is used to create better stories and increase reach and consumption.

If you want your Facebook post to be read by as many people as possible, what will you do? You’ll add an interesting visual. This trick works perfectly with reports too. Data-driven visuals attract more attention, are easier to understand, and assist in getting your message across to the audience quickly.

With the help of descriptive graphics and dashboards, even difficult information can be clear and comprehensible.

Why is that?

Most people are visual learners. So if you want the majority of your partners, colleagues, and clients to be able to interact with your data, you should turn boring charts into beautiful graphics. Here are some noteworthy numbers, based on research, that confirm the importance of visualization:

• People get 90% of information about their environment from the eyes.
• 50% of brain neurons take part in visual data processing.
• Pictures increase the wish to read a text up to 80%.
• People remember 10% of what they hear, 20% of what they read, and 80% of what they see.
• If a package insert doesn’t contain any data illustrations, people will remember 70% of the information. With pictures added, they’ll remember up to 95%.

With  OWOX BI , your data is collected, normalized, attributed & prepared for reporting. 

Use our templates to get reports built in minutes, or use your data to prepare the data for any report you need and visualize it in Looker Studio (formerly Google Data Studio), Google Sheets or the BI tool of your choice. Save 70+ hours on data preparation every month and automate your entire digital marketing reporting.

Relevant visualization brings lots of advantages for your business:

• Fast decision-making.  Summing up data is easy and fast with graphics, which let you quickly see that a column or touchpoint is higher than others without looking through several pages of statistics in Google Sheets or Excel... or even a database or a CRM or CMS system.
• More stakeholders are involved.  Most people are better at perceiving and remembering information presented visually and delivered on time in a visual-appealing format. 
• Higher level of involvement.  Beautiful and bright graphics with clear messages attract readers’ attention.
• Better understanding.  Perfect reports are transparent not only for technical specialists, analysts, and data scientists but also for CMOs, CEOs and other C-levels or managers, and help each and every worker make decisions in their area of responsibility.

The first thing to do before creating any chat is to check all information for accuracy and consistency.

For example, if the scaling factor is 800%, whereas the average is 120–130%, you should check where this number is coming from. Maybe it’s some kind of an outlier that you need to delete from the graph so it doesn’t skew the overall picture: 800% downplays the difference between 120% and 130%. This kind of outlying data in a report can lead to incorrect decisions made.

To increase the chances of success in marketing, the right message should be delivered to the right person at the right time. 

The same three rules are applied for data visualization:

• Choose the right chart  to visualize the answer to specific question based on your goal.​
• Confirm that the message to deliver the result of your report suits your audience (the stakeholder).
• Use an appropriate design for the chart to deliver that message.

If your message is timely but the chat or graphic isn’t dynamic, or it provides incorrect insights. or the design is not attractive, then you won’t achieve the results you were dreaming of.

If you choose the wrong chart or graph, your readers will be confused or interpret or read the results incorrectly. That’s why before creating a report with charts, it’s important to decide what data you want to visualize and for what purpose, for example: 

• To  compare different data points
• ​To show data distribution : for instance, which data points are frequent and which are not
• To show the structure of something with the help of data
• To follow the connections, references or correlation between data points

Let’s have a look at the most popular types of charts and the goals they can help you achieve.

1. Line chart

A line chart is a type of data visualization that uses a series of data points connected by straight lines. It is commonly used to show the relationship between two variables over a continuous period of time. Foe example, the x-axis represents the time or the independent variable, while the y-axis represents the value or the dependent variable.

By plotting the data points and connecting them with lines, the line chart provides a visual representation of how the values change over time.

Pros of Line Charts

One of the main advantages of line charts is their ability to display trends and patterns in data . They make it easy to identify the overall direction of change, whether it is increasing, decreasing, or remaining stable.

Line charts also allow for the comparison of multiple data series on the same chart, making it simple to  analyze the correlation between different variables .

Additionally, line charts are visually appealing and easy to understand, making them accessible to a wide range of audiences.

Cons of Line Charts

However, line charts also have some limitations. They are most effective when used with continuous data, such as time series data, and may not be suitable for categorical or discrete data .

Line charts can become cluttered and confusing if there are too many data points or series plotted on the chart. They may also not be the best choice for displaying data with irregular or inconsistent intervals. It is important to consider these factors when deciding whether to use a line chart for data visualization.

Use cases of Line Charts

The best use cases for line charts include analyzing sales or revenue data over time , tracking website traffic or user engagement metrics , visualizing stock market trends, or monitoring changes in weather patterns.

Line charts are particularly useful when there is a need to understand the overall trend or pattern in the data and identify any significant changes or anomalies. They are also effective for presenting data to a non-technical stekholders, as they provide a clear and really easy and intuitive representation of the data.

2. Bar chart

Type of diagram that represents data using rectangular bars is called bar chart. Each bar corresponds to a specific metric or variable, while its length or height represents the value associated with that metric.

Bar charts are typically used to compare different metrics or track changes over time  providing simplicity and versatility.

Horizontal bar charts are often used when you need to compare lots of data sets or to visually emphasize the distinct advantage of one of the data sets.

Vertical bar charts display how data points change over time — for example, how the annual company profit has changed over the past few years.

Pros of Bar Charts

Bar charts are ease of read and consume, no background in data analysis is required. 

The clear and straightforward presentation of data in bar charts allows for quick insights and understanding.

Additionally, bar charts can accommodate large datasets and display multiple variables simultaneously (and stay usable).

Cons of Bar Charts

Continuous data, such as temperature measurements over time, may not be as suitable for bar charts.

Bar charts may also not be the best choice for displaying complex relationships or correlations between variables, as they primarily focus on comparing values within categories.

Use cases of Bar Charts

Some common use cases include sales analysis, market research, financial reporting, and survey results. For example, a bar chart can be used to compare the market share of different companies in a specific industry , or to visualize the responses to a survey question with multiple answer options.

A bar chart can also represent the sales figures of different products in a given month , with each bar representing a product or a category, and its height indicating the sales quantity. This visual representation allows for easy comparison and identification of trends or patterns in the data.

The  of bar charts make them a valuable tool for data visualization in various domains.

3. Histogram

A histogram is often mistaken for a bar chart due to their visual similarities, but the goals of these charts are different.

 A histogram shows the distribution of a dataset across a continuous interval or a definite time period. It is a graphical representation of the frequency of data values in different intervals or bins. The x-axis of a histogram represents the range of values in the dataset, divided into equal intervals, while the y-axis represents the frequency or count of data values falling within each interval. The height of each bar in the histogram corresponds to the frequency of data values in that interval. This chart provides a visual summary of the underlying data distribution.

Unlike a histogram, a bar chart doesn’t show any continuous interval; each column displays a category of its own. It’s easier to demonstrate the number of purchases in different years with the help of a bar chart. 

If you want to know the number of order beween $10 and 100, $101 and 200, $201 and 300, etc. of purchases, it’s better to choose a histogram. The histogram will show you the frequency of orders falling within each price range, allowing us to identify patterns such as a normal distribution, skewed distribution, or outliers.

Histogram allows you to quickly identify the central tendency, spread, and shape of the dataset. Histograms are particularly useful when dealing with large datasets or continuous data, as they provide a visual summary without overwhelming the viewer with individual data points.

What are the limitation of the histogram?

First, the choice of bin size or interval width can impact the interpretation of the data. A smaller bin size can provide more detailed information but may also result in a cluttered or noisy chart . At the same time, a larger bin size can oversimplify the data distribution .

Second, histograms may not be suitable for datasets with categorical or ordinal variables , as they require numerical data to create meaningful intervals.

4. Pie chart

A pie chart is a type of data visualization that displays shares of each value in a data set.

It is divided into slices, where each slice represents a proportion or percentage of the whole. The size of each slice is determined by the value it represents in relation to the total value of the data set. Pie charts are commonly used to show the distribution or composition of a categorical variable.

Pie chart visually displays the relative proportions of different categories within a data set. It allows viewers to quickly grasp the overall distribution of the data and easily compare the sizes of different categories.

The angles of the slices in the pie chart represent the proportions of the categories, making it easy to understand the relationship between the parts and the whole. For instance, what percentage of general sales is attributed to each product category?

Pie charts are particularly useful when dealing with data that has a small number of categories or when the emphasis is on comparing the parts to the whole. They can also be useful for highlighting a specific category or identifying outliers.

The biggest pie chart limitation is that they can become difficult to interpret when there are too many categories or when the differences between the categories are small. It can be challenging to accurately compare the sizes of the slices, especially if they are similar in magnitude.

Additionally, pie charts do not easily don't represent the trends over time. Pie charts are commonly used in business and marketing to represent market share, customer demographics, or product sales by category. Pie charts are also used in survey data to display the distribution of responses for multiple-choice questions. Overall, pie charts are most effective when the data is simple, the categories are distinct, and the emphasis is on comparing the parts to the whole.

5. Scatter plot

A scatter plot chart displays the relationship between two numerical variables . It uses a Cartesian coordinate system, where each data point is represented by a dot or marker on the chart.

The x-axis represents one variable, while the y-axis represents the other variable. By plotting the data points on the chart, you can visually analyze the correlation or pattern between the variables.

The scatter plot chart allows you to identify trends, clusters, or outliers in the data.

Additionally, scatter plots can be used to detect any patterns or irregularities in the data distribution. 

The main limitation is that it can only represent two variables at a time. If there are more than two variables to analyze, additional charts are required. Also, scatter plots may not be suitable for large datasets, as the overlapping data points can make it difficult to make decisions based on the chart accurately.

For example, with the help of a scatter plot, you can find out how the conversion rate changes depending on the size of the product discount.

6. Bubble chart

This is an interesting chart that allows you to compare two parameters by means of a third.

It is a variation of a scatter plot, where the size of the bubbles is used to convey additional information. The bubble chart is particularly useful when visualizing three variables, as it allows for the representation of two continuous variables on the x and y axes, while the size of the bubbles represents the third variable. This makes it easy to identify patterns and relationships between the variables in a single chart.

Bubble chat allows you to display large amounts of data in a visually appealing and intuitive way. By using different colors or shades, you can also incorporate a fourth variable into the chart, further enhancing the information conveyed. The size of the bubbles provides a quick visual cue, allowing for easy comparisons between data points.

Additionally, the bubble chart can be interactive, allowing users to hover over or click on the bubbles to reveal more detailed information.

Basically, the main drawback of the bubble charts is that the size of the bubbles can sometimes be misleading, as it may not accurately represent the magnitude of the data point. This can be mitigated by scaling the size of the bubbles appropriately or by providing a clear legend or scale.

Also, it is important to strike a balance between the number of data points and the readability of the chart.

The best use cases for bubble charts are situations where you want to visualize relationships between three variables.

For example, you can use a bubble chart to show the relationship between the price, size, and number of orders of different products. It can also be used to compare data across different categories or groups, such as comparing the revenue, market share, and growth rate of different companies in an industry.

Bubble charts are particularly effective when the size of the bubbles is meaningful and provides valuable insights into the data.

7. Geo chart

The geo chart is a simple one. It’s used when you need to demonstrate a certain data distribution across regions, countries, and continents.

By visualizing data on a map, a geo chart provides a clear and intuitive way to understand spatial patterns and user behavior. For example, a geo chart can show shopping frequency across countries, GDP per capita by country, or election results by region. It allows viewers to quickly grasp the variations and disparities between different locations. Basically, geo chart works best if metric dimension is geographical.

By mapping data onto a familiar geographic locations, it becomes easier for viewers to interpret and remember the information.

Since a geo chart relies on colors or patterns to represent data, it is important to choose appropriate color schemes and legends to avoid confusion or bias. Furthermore, a geo chart may not be suitable for displaying complex or detailed data, as the level of granularity is often limited to the size and boundaries of the regions on the map. It is important to carefully select the level of detail and aggregation that best suits the purpose of the report.

For example, when analyzing sales data, a geo chart can show the distribution of sales across different regions , helping businesses identify potential markets or areas of improvement.

Overall, geo charts are particularly effective when the spatial dimension of the data is crucial for decision-making or storytelling.

The second important thing that you have to take into account while working with visualization is  choosing the right message for the audience . The information you talk about, the story you tell in the report should be clear and informative for your readers.

Here’s a chart that was awarded the prestigious Data Journalism Award. 

For people who aren’t familiar with the background to the story, this chart looks like a picture made by a three-year-old. However, when you find out a little bit more about it, you can see the huge amount of work done by its authors.

Charles Seife and Peter Aldhous, Buzzfeed News editors, used the R language to  visualize flight data obtained by FBI and DHS agents as part of air surveillance. Specifically, this chart shows flights above the house and mosque of those responsible for the mass shooting in December 2015 in San Bernardino, California.

While choosing the parameters you want to visualize on one chart, you have to confirm that they can be combined. Some combinations just aren’t logical, though at first sight the information correlates perfectly. Here’s an example of such a chart with a faulty correlation. It shows that the number of people who drowned by falling into a pool correlates with the number of Nicolas Cage films.

The next things you should take into account when creating a chart are the scale and scope. People are used to the fact that measurements on axes start from the bottom and from the left. If you change the direction of measurement, it will confuse an inattentive audience. Although we should mention that reversing the measurement is possible when used as a tactical maneuver, as in this example:

At first sight, it may seem that the number of murders committed using firearms has been decreasing over the years. In fact, it’s the opposite, as the scale starts from the top. Perhaps the author of the chart did this on purpose to decrease the negative response to the results shown.

A suitable scale also makes your chart clearer. If a report shows data points that are too close and you can’t see any movement, try to change the scale. Start the measurements not from zero or divide the scale into smaller parts and the picture will clear up.

Before giving a report to the stakeholder, make sure that the chart loads fast. Slow loading kills all your efforts.

For example, if you’re visualizing data in Google Sheets, most likely your data is stored on the same page or on the next page and doesn’t come from a third-party source.

But when you create a report in Looker Studio (ex. Data Studio) or Power BI, data will be imported from somewhere else. In this case, you have to pay close attention to the source accessibility and the data flow rate. Otherwise, you’ll see a sad looking picture when there’s a chart template but data hasn’t been loaded.

Remember, the golden rule when you're crafting your chart design is to keep it simple. 

When you're tasked with putting together a standard report, don’t fret about making it look fancy. You don't need to dress it up.

Avoid any extra elements that only clutter the chart: too many colors and structures, 3D volume, shadows, gradients, etc.

The simpler a chart is - the easier it is for the readers to understand the information you want to share.

Don’t make your visualizations too small, and don’t put all charts on the same dashboard page. It’s considered bad style to use more than three types of charts on one slide or the same dashboard page. If you really need so many chart types, put them on different pages, or make a clear separation, so it’s easy to understand them.

Don’t be afraid to experiment. If you have a task that's not typical, perhaps your solution should also be non-standard. In the infographic below, we can see the wing movement patterns of different animals. The dynamic visualization is totally relevant.

Let’s have a look at some data visualization tools examples and discuss how to choose the right one for your goals.

Visualizing data is an undeniable benefit in any niche, and it doesn’t matter if you’re building a career in marketing, design, retail or anything else.

Making information easy to consume and quickly make smart decisions is one of the keys to finding growth zones and developing your business.

When your colleagues would see the visual charts outlining the current state of the main metrics for you, it’s easier to make sure that all of the team members are  on the same page and everyone understands the strong and weak points of the current strategy.

While visualizing reports itself cannot fix the issues, it gives you the wheel to drive the car, to make the necessary changes and improve the KPIs.

Nowadays, there are lots of data visualization and reporting tools on the market. Some of them are paid, others are available for free. Some of them work fully on the web, others can be installed on a desktop but work online, and others are offline only. 

Best Reporting Tools

We’ve crafted a list of 10 most popular reporting and data visualization software:

1. Google Spreadsheets

Explore BigQuery Data in Google Sheets

Bridge the gap between corporate BigQuery data and business decisions. Simplify reporting in Google Sheets without manual data blending and relying on digital analyst resources availability

2.  Looker Studio (ex. Google Data Studio)

3.  Tableau

4.  Power BI

6. QlikView

7. R Studio

8. Visual.ly

First six tools and services are created by companies specializing in visualization. 

Numbers seven through ten are quite interesting tools, mostly free and online. They offer non-standard types of data visualization and may offer new ways of approaching your business information.

How to select a reporting tool

What to look for when choosing a reporting tool:

• Start from the goals and tasks you want to accomplish.  For example, a major trend on the market nowadays is dynamic reports. If a tool cannot work with dynamic reports, that’s a strike against it.
• Consider the amount of money you’re ready to pay.  If your team is big enough and every employee has to work with the visualization tool, then the cost per user may be a stop sign.
• Decide who will use the tool and how:
• Is there a possibility for group editing? 
• How simple is it to start working with the tool? 
• Is the interface user-friendly? 
• Is there a possibility to create a report without any knowledge of programming? 

For example, R Studio is a great service, especially for searching for trends and building attribution and correlation models. But if you are not familiar with coding, you won't be able to connect any specific libraries, and it would be difficult for you to start working with R Studio.

We'll dive deeper into a few services and guide you through their pros & cons, as well as the main features and advantages. But before we start, let us explain how  dynamic data visualization  and  dynamic reports  differs.

Dynamic reports   refer to the possibility to import data from different sources in real time. 

For example, Looker Studio (formerly Google Data Studio) doesn’t have dynamic reports in place. Let’s say we’ve connected a Looker Studio request from Google BigQuery and then changed something in this request. To record these changes in the report, we need to at least refresh the page. 

However, if we add or delete some fields in Google BigQuery (not just change the logic of the calculation but change the table structure), then Looker Studio would show an error. You’ll have to rebuild the dashboard to get the visualizations in place.

Dynamic visualization   concept refers to the possibility to look at summary statistics over different dates during one session. 

For example, in Google Analytics 4 you can change the time period and get statistics for the date range you need.

OWOX BI is a comprehensive analytics platform that covers everything from data collection and streaming to attribution modeling and reporting. With OWOX BI, companies get a complete view of their marketing activities across various channels, empowering advertising specialists to optimize their ad spending and achieve better ROI.

3 whales of data management and analysis

OWOX BI Pipelines   facilitates seamless  data collection  from various advertising platforms, CRMs, and website builders, enabling organizations to consolidate all their data in one place in order to have a  data source of truth  and gain better insights.

OWOX BI Streaming  is a  cookieless real-time user behavior tracking  system, ensuring privacy compliance with regulation and extending the lifespan of cookies. Marketers can accurately track the entire conversion journey, find the  true sources of conversions , and gain a deeper understanding of customer behavior.

OWOX BI Transformation   saves time on data preparation  (avg. of 70 hours per month). With pre-built  low- or no-code transformation templates  (based on 100’s delivered projects across multiple industries), businesses can quickly produce  trusted datasets for reporting , modeling, and operational workflows:

• Sessionization: Group on-site events into sessions to  find conversion sources
• Cost data blending: Merge ad cost data across channels to  compare campaign KPIs in a single report
• Attribute ad costs to sessions to  measure  cohorts   and pages' ROI;
• Create cross-device user profiles across  different devices
• Identify new and returning user types for  accurate analysis
• Apply a set of  attribution models : Choose from standard attribution models like First-Click, LNDC, Linear, U-shape, and Time Decay, or create a custom  Machine Learning Funnel-based attribution model
• Use modeled conversion for cookieless measurements and  conversion predictions
• Prepare data for  marketing reports  in minutes

Lastly, OWOX BI integrates with visualization tools like Looker Studio, Tableau, or Power BI, enhancing data-driven decision-making by building customizable reports & keeping the data always up-to-date.

OWOX BI Advantages & Benefits

• No technical background, coding experience or knowledge of SQL is required.
• Simple and user-friendly interface: you can collect all of the data and generate reports using our dashboard templates and customize what matters for you the most.
• If you want to working with your data in Google Sheets, you can easily export an aggregated dataset from BigQuery to Google Sheets with our reports add-on . 
• You can copy SQL queries generated by OWOX BI. 
• You can then modify those queries or use them, for example, to automate a data-based report in Google Sheets or BigQuery.
• You retain complete control over access to that data .
• You can merge digital marketing data with CRM/CMS data.
• Full transperancy.

Note: For enterprise customers, OWOX BI expert team will set up a data model tailored to your business. You’ll be able to evaluate the impact of all marketing efforts — both online and offline.

Types of data you can use

User actions on your site:

• You can set up the collection of raw data from the site in Google BigQuery using OWOX BI Streaming.
• Or you can use the native standard export from Google Analytics 360 or Google Analytics 4 to Google BigQuery.

Transactions Data:

• Google Analytics → Google BigQuery
• Google Sheets → Google BigQuery
• CRM → Google BigQuery

Advertising campaign costs:

• Advertising services → Google Analytics 4
• Advertising services → Google BigQuery
• Other marketing tools → Google BigQuery

Looker Studio, also  known as data studio  allows you to connect data sources, easily build charts, reports and add elements to visualize and share reports with colleagues in a way that’s similar to other Google products.

Advantages:

• Free​ (with paid version announced in 2023)
• More than 860 connectors to the data sources that are easy to integrate
• Allows to use data from several sources via one dashboard
• Convenient to share reports

Looker Studio is a free tool with  21 native connectors  provided by Google:

• Connect to your Looker semantic models. 
• Connect to Google Analytics 4 reporting views. 
• Connect to Google Ads performance report data.
• Connect Google Sheets .
• Connect to BigQuery tables and custom queries.
• Connect to AppSheet app data.
• File Upload - Use CSV ( comma-separated values ) files. 
• Connect to Amazon Redshift .
• Connect to Campaign Manager 360 data.
• Connect to MySQL databases.
• Connect to Display & Video 360 report data.
• Connect to Microsoft SQL Server databases.
• Connect to PostgreSQL databases.
• Connect to Search Console data.
• Connect to YouTube Analytics data. 

and more...

They’re checked, approbated, work well, and perfectly suit to the most common reporting tasks. 

There are also connectors provided by Google partners, though you have to understand that connectors can be presented by developers with different skill levels and there’s no guarantee they’ll perform correctly.

By the way, if you want to see any Facebook or Yahoo Gemini statistics in reports built in Looker Studio, you can  import ad cost data into Google BigQuery  with OWOX BI. While you may lose some of the important data with other data connectors, with our Facebook Ads to Google BigQuery pipeline you receive complete data ready for analysis and reporting from your Facebook account.

You can also merge your Facebook Ads data with the advertising cost data from Google Ads, Twitter Ads, and LinkedIn ads and get a helicopter view of your marketing performance and optimize your cross-channel budget easily.

Automate your digital marketing reporting

Manage and analyze all your data in one place! Access fresh & reliable data with OWOX BI — an all-in-one reporting and analytics tool

We also have a ready-to-use dashboard templates of our own that we want to share.

We've prepared a comprehensive Looker Studio dashboard template gallery with  ready-to-use templates  so that you can quickly create a guide to your business results, KPIs and performance.

The first is a  All-in-one Performance Dashboard . With this dashboard, you can find all of the basic metrics and metrics to stay on top of your advertising and marketing performance and achieve the desired ROI.

All-in-one Digital marketing Dashboard

Another dashboard template we'd like to share is the  Digital Marketing Paid Channels KPI dashboard , which is segmented by data sources (shown in detail). In other words, it shows filtered data on Facebook marketing campaigns, etc.

Those are the dashboard templates. Make a copy, change the data sources to your own, and use them to build beautiful reports based on your data. 

One of the recent Looker Studio updates adds the possibility to filter information by view. For example, you can compare data points over the current period and the previous year.

One more interesting update allows you to change the type of an already created chart, graph or element. Earlier, when changing a chart, you had to delete it and create a new one. 

Useful links:

• Webinar: Mastering Marketing KPIs
• Google Looker Studio dashboard template gallery

Mastering Marketing KPIs: How to Evaluate Your Marketing Performance

You'll learn how you can effectively evaluate your marketing performance to fuel your business growth.

Marketing Ninja @ OWOX

This is one of the two most popular data reporting tools (together with Microsoft Excel) that’s used by any marketing specialist at least once. The Google Sheets interface is quite simple and easy-to-use, especially for those who just starting analytics out.

• Flexible — supports dynamic parameters, vlookups, pivot tables, formulas, app scripts etc.
• Easy to integrate with data sources (but not so easy to automate updates)
• Convenient to share reports via links

The charts and reports types in Google Sheets are the same as that is in Looker Studio.

Conditional Formatting

Conditional formatting in Google Sheets allows users to apply formatting rules to cells based on specific conditions .

These conditions can be based on the cell's value, text, or even a formula . By using conditional formatting, users can visually highlight important data, identify trends , and make their spreadsheets more visually appealing and easier to understand.

For example, let's say you have a sales report in Google Sheets and you want to highlight all the cells that have sales numbers above a certain threshold . With conditional formatting, you can set a rule that applies a different background color to those cells automatically. This makes it easier to quickly identify the high-performing sales figures without manually scanning through the entire spreadsheet.

Pivot Tables

Perhaps the main advantage of Google Sheets as your go-to reporting tool is pivot tables.

Pivots allow users to summarize and analyze significant amounts of data. They are used to transform raw flat table data or data sets into insights by organizing and summarizing it in an easy and structured relatively small table.

With Pivot tables you can quickly explore data from different perspectives, change columns and rows, sort values, identify patterns , and uncover trends or anomalies . They are particularly useful for data aggregation tasks.

For example , let's say you have a spreadsheet with sales data for a company. The data includes columns for product names, sales dates, sales quantities, and sales amounts . By creating a pivot table, you can easily summarize this data to answer questions like: ' What are the total sales amounts for each product? ' or ' What are the average sales quantities by month? '

Pivot tables allow you to group and aggregate data based on different criteria , such as product , sales date , or any other relevant attribute.

VLOOKUP formula

VLOOKUP is a function in Google Sheets that stands for vertical lookup . It is used to search for a specific value in the leftmost column of a range of cells, and then return a corresponding value from a different column in the same row . Vlookup is commonly used to find and retrieve data from large datasets or tables.

Imagine having a list of products and their corresponding prices. You can use VLOOKUP to search for a specific product name in the leftmost column, and then retrieve the price of that product from a different column in the same row.

This can be useful for tasks such as pricing analysis.

The syntax of the VLOOKUP function in Google Sheets is as follows: =VLOOKUP(search_key, range, index, is_sorted).

The search_key is the value you want to search for, the range is the range of cells where the search will be performed, the index is the column number from which the corresponding value should be returned, and the is_sorted is an optional parameter that specifies whether the range is sorted in ascending order or not.

Everything about VLOOKUP in Google Sheets

BigQuery <> Google Sheets

If you want to get data from BigQuery to visualized in Google Sheets, there is a Google sheets extension that allows you to query data directly from Google BigQuery and build reports based on the imported data.

Basically, you can request any data stored in your Google BigQuery project directly from the Sheets interface. 

Simplify BigQuery Reporting in Sheets

Easily analyze corporate data directly into Google Sheets. Query, run, and automatically update reports aligned with your business needs

Cohort Analysys

Last but not least, we wanted to share with you one of our favorite reports for Google Sheets — the cohort analysis report .

Cohort analysis is a powerful analytical technique used to understand the behavior and common details of a specific group of individuals over time. 

It involves dividing a larger audience into smaller groups, or cohorts , based on a common characteristic or event. These cohorts are then analyzed to identify patterns and trends  that can help businesses make informed decisions and improve their strategies.

The most common case of cohort analysis in marketing is to track the behavior of customers who made their first purchase in a particular month . By analyzing this cohort, businesses can determine the retention rate, average purchase value, and lifetime value of these customers. This information can be valuable in identifying the most effective marketing channels, optimizing customer acquisition strategies, and improving customer loyalty.

Additionally, you can read our  detailed guide to cohort analysis in Google Analytics 4  and Google Sheets, where we provide very detailed instructions. We’ve also hosted a webinar on cohort analysis .

Finally, we want to share some useful links and books on data visualization:

• Edward Tufte,  The Visual Display of Quantitative Information
• Stephen Few,  Big Data, Big Dupe
• «The Joy of Stats»  (documentary film)

Visualization services can help you make your reports visually appealing and comprehensive, you can highlight valuable insights in your data easily. 

If you want to keep up with the pace of modern business, adding visual storytelling and data exploration to your reports will allow you to accelerate the process of decision-making.

If you still don’t know which of all data visualization tools would fit your business needs, book a free demo to discuss your specific situation with our data experts and discover the ideal solution designed for you. 

Gain clarity for better decisions without chaos

No switching between platforms. Get the reports you need to focus on campaign optimization

How can I create effective data visualizations?

What are some popular data visualization tools, what are the benefits of data visualization.

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Data visualization definition

Data visualization is the presentation of data in a graphical format such as a plot, graph, or map to make it easier for decision makers to see and understand trends, outliers, and patterns in data.

Maps and charts were among the earliest forms of data visualization. One of the most well-known early examples of data visualization was a flow map created by French civil engineer Charles Joseph Minard in 1869 to help understand what Napoleon’s troops suffered in the disastrous Russian campaign of 1812. The map used two dimensions to depict the number of troops, distance, temperature, latitude and longitude, direction of travel, and location relative to specific dates.

Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more.

What is the business value of data visualization?

Data visualization helps people analyze data, especially large volumes of data, quickly and efficiently.

By providing easy-to-understand visual representations of data, it helps employees make more informed decisions based on that data. Presenting data in visual form can make it easier to comprehend, enable people to obtain insights more quickly. Visualizations can also make it easier to communicate those insights and to see how independent variables relate to one another. This can help you see trends, understand the frequency of events, and track connections between operations and performance, for example.

Key data visualization benefits include:

• Unlocking the value big data by enabling people to absorb vast amounts of data at a glance
• Increasing the speed of decision-making by providing access to real-time and on-demand information
• Identifying errors and inaccuracies in data quickly

What are the types of data visualization?

There are myriad ways of visualizing data, but data design agency The Datalabs Agency breaks data visualization into two basic categories:

• Exploration: Exploration visualizations help you understand what the data is telling you.
• Explanation: Explanation visualizations tell a story to an audience using data .

It is essential to understand which of those two ends a given visualization is intended to achieve. The Data Visualisation Catalogue , a project developed by freelance designer Severino Ribecca, is a library of different information visualization types.

Some of the most common specific types of visualizations include:

2D area: These are typically geospatial visualizations. For example, cartograms use distortions of maps to convey information such as population or travel time. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state.

Temporal: These are one-dimensional linear visualizations that have a start and finish time. Examples include a time series, which presents data like website visits by day or month, and Gantt charts, which illustrate project schedules.

Multidimensional: These common visualizations present data with two or more dimensions. Examples include pie charts, histograms, and scatter plots.

Hierarchical: These visualizations show how groups relate to one another. Tree diagrams are an example of a hierarchical visualization that shows how larger groups encompass sets of smaller groups.

Network: Network visualizations show how data sets are related to one another in a network. An example is a node-link diagram, also known as a network graph , which uses nodes and link lines to show how things are interconnected.

What are some data visualization examples?

Tableau has collected what it considers to be 10 of the best data visualization examples . Number one on Tableau’s list is Minard’s map of Napoleon’s march to Moscow, mentioned above. Other prominent examples include:

• A dot map created by English physician John Snow in 1854 to understand the cholera outbreak in London that year. The map used bar graphs on city blocks to indicate cholera deaths at each household in a London neighborhood. The map showed that the worst-affected households were all drawing water from the same well, which eventually led to the insight that wells contaminated by sewage had caused the outbreak.
• An animated age and gender demographic breakdown pyramid created by Pew Research Center as part of its The Next America project , published in 2014. The project is filled with innovative data visualizations. This one shows how population demographics have shifted since the 1950s, with a pyramid of many young people at the bottom and very few older people at the top in the 1950s to a rectangular shape in 2060.
• A collection of four visualizations by Hanah Anderson and Matt Daniels of The Pudding that illustrate gender disparity in pop culture by breaking down the scripts of 2,000 movies and tallying spoken lines of dialogue for male and female characters. The visualizations include a breakdown of Disney movies, the overview of 2,000 scripts, a gradient bar with which users can search for specific movies, and a representation of age biases shown toward male and female roles.

Data visualization tools

Data visualization software encompasses many applications, tools, and scripts. They provide designers with the tools they need to create visual representations of large data sets. Some of the most popular include the following:

Domo: Domo is a cloud software company that specializes in business intelligence tools and data visualization. It focuses on business-user deployed dashboards and ease of use, making it a good choice for small businesses seeking to create custom apps.

Dundas BI: Dundas BI is a BI platform for visualizing data, building and sharing dashboards and reports, and embedding analytics.

Infogram: Infogram is a drag-and-drop visualization tool for creating visualizations for marketing reports, infographics, social media posts, dashboards, and more. Its ease-of-use makes it a good option for non-designers as well.

Klipfolio: Klipfolio is designed to enable users to access and combine data from hundreds of services without writing any code. It leverages pre-built, curated instant metrics and a powerful data modeler, making it a good tool for building custom dashboards.

Looker: Now part of Google Cloud, Looker has a plug-in marketplace with a directory of different types of visualizations and pre-made analytical blocks. It also features a drag-and-drop interface.

Microsoft Power BI: Microsoft Power BI is a business intelligence platform integrated with Microsoft Office. It has an easy-to-use interface for making dashboards and reports. It’s very similar to Excel so Excel skills transfer well. It also has a mobile app.

Qlik: Qlik’s Qlik Sense features an “associative” data engine for investigating data and AI-powered recommendations for visualizations. It is continuing to build out its open architecture and multicloud capabilities.

Sisense: Sisense is an end-to-end analytics platform best known for embedded analytics. Many customers use it in an OEM form.

Tableau: One of the most popular data visualization platforms on the market, Tableau is a platform that supports accessing, preparing, analyzing, and presenting data. It’s available in a variety of options, including a desktop app, server, and hosted online versions, and a free, public version. Tableau has a steep learning curve but is excellent for creating interactive charts.

Data visualization certifications

Data visualization skills are in high demand. Individuals with the right mix of experience and skills can demand high salaries. Certifications can help.

Some of the popular certifications include the following:

• Data Visualization Nanodegree (Udacity)
• Professional Certificate in IBM Data Science (IBM)
• Data Visualization with Python (DataCamp)
• Data Analysis and Visualization with Power BI (Udacity)
• Data Visualization with R (Dataquest)
• Visualize Data with Python (Codecademy)
• Professional Certificate in Data Analytics and Visualization with Excel and R (IBM)
• Data Visualization with Tableau Specialization (UCDavis)
• Data Visualization with R (DataCamp)
• Excel Skills for Data Analytics and Visualization Specialization (Macquarie University)

Data visualization jobs and salaries

Here are some of the most popular job titles related to data visualization and the average salary for each position, according to data from PayScale .

• Data analyst: $64K
• Data scientist: $98K
• Data visualization specialist: $76K
• Senior data analyst: $88K
• Senior data scientist: $112K
• BI analyst: $65K
• Analytics specialist: $71K
• Marketing data analyst: $61K

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Data & Finance for Work & Life

What is a data display? Definition, Types, & Examples

You may have heard that “data is the new oil” — the most valuable commodity of the 21st century. But just like oil is useless until refined, data is useless until simplified and communicated. Data displays are a tool to help analysts do just that.

Because they’re so important to data, data displays can be found in virtually every discipline that deals with large amounts of information. Consequently, the precise meaning behind data displays has become blurred, resulting in a lot of unanswered questions — many of which you may have already asked yourself.

The purpose of this article is to clear things up. I will briefly define data displays, show examples of 17 popular displays, answer some common questions, compare data displays and data visualization, cover data displays in the context of qualitative data, and explore common data visualization tools.

Don’t forget, you can get the free Intro to Data Analysis eBook to get a strong start.

Data Display Definition

Also known as data visualization, a data display is a visual representation of raw or processed data that aims to communicate a small number of insights about the behavior of an underlying table, which is otherwise difficult or impossible to understand to the naked eye. Common examples include graphs and charts, but any visual depiction of information, even maps, can be considered data displays.

Additionally, the term “data display” can refer to a legal agreement in which a publishing entity, often a stock exchange, obtains the rights from a partner to publicly display the partner’s data. This is important to know, but it’s outside the scope of this article.

Types of Data Display: 17 Actionable Visualizations with Examples

The most common types of data displays are the 17 that follow:

• bar charts,
• column charts,
• stacked bar charts,
• line graphs,
• area charts,
• stacked area charts,
• unstacked area charts,
• combo bar-and-line charts,
• waterfall charts,
• tree diagrams,
• bullet graphs,
• scatter plots,
• histograms,
• packed bubble charts, and
• box & whisker plots.

Let’s look at each of these with an example. I’ll be using Tableau software to show these, but many of them are available in Excel.

Bar charts show the value of dimensions as horizontal rectangles. They’re useful for comparing simple items side-by-side. This image shows total checkouts for two book IDs.

Column Charts

Column charts show the value of dimensions as a vertical rectangle. Like bar charts, they’re useful for comparing simple items side-by-side. This image shows total checkouts for two book IDs.

Stacked Bar/Stacked Columns Charts

Stacked bar or column charts show the value of dimensions with more granular dimensions “inside.” They’re useful for comparing dimensions with additional breakdown. In this image, the columns represent total checkouts by book ID, and the colors represent month of checkout.

Tree maps show the value of multiple dimensions by their relative size and splits them into rectangles in a “spiral” fashion. As you can see here, book IDs are shows in size by the number of checkouts they had.

Line Graphs

Line graphs show the value of two dimensions that are continuous, most often wherein one of the dimensions is time. This image shows five book IDs by number of checkouts over time.

Area Charts

Area charts show the value of a dimension as all the space under a line (often over time).

Stacked Area Charts

Stacked are charts show the value of two dimension values as areas stacked on top of each other, such that one starts where the other ends on the vertical axis.

Unstacked Area Charts

Unstacked area charts show two area charts layered on top of each other such that both start from zero. As you can see below, this view is useful for comparing the sum of two values over time.

Combo Bar-and-Line Charts

A bar-and-line chart shows two different measures — one as a line and the other as bars. These are particularly useful when showing a running total as a line and the individual values of the total as bars.

Waterfall Charts

Waterfall charts show a beginning balance, additions, subtractions, and an ending balance, all as a sequence of connected bars. These are useful for showing additions and subtractions, or a corkscrew calculations, around a project or account.

Tree Diagrams

Tree diagrams show the hierarchical relationship between elements of a system.

Bullet Graphs

Bullet graphs show a column value of actual real numbers (blue bars), a marker for a target number (the small black vertical lines), and shading at different intervals to indicate quality of performance such as bad, acceptable, and good.

Scatter Plots

Scatter plots show points on a plane where two variables meet — very similar to a line graph but used to compare any kind of variables, not just a value over time.

Histograms show bars representing groupings of a given dimension. This is easier to understand in the picture — each column represents a number of entries that fall into a range, i.e. 10 values fall into a bin ranging from 1-4, 29 values fall into a bin of 5-8, etc.

Heat maps show the intensity of a grid of values through the use of color shading and size intensities.

Packed Bubble Charts

Packed bubble charts show the intensity of dimension values based on relative size of “bubbles,” which are nothing more than circles.

Box & Whisker Plots

Box & whisker plots show values of a series based on 4 markers: max, min, lower 25% quartile, upper 75% quartile, and the average.

Don’t Use Pie Charts!

One of the most common chart types is a pie chart, and I’m asking you to never use one. Why? Because pie charts don’t provide any value to the viewer.

A pie chart shows the percent that parts of a total represent. But what does that mean for the viewer? Visually, it’s difficult to distinguish which slices are largest, unless you have one slice that dominates 80% or more of the pie — or you use labels on each slice.

If you want to show percent of total, use a percent of total bar chart. Or better yet, use a waterfall chart! These will be much more informative to the viewer.

Packed Bubble Charts: the New Cool Thing

I’m not totally against bubble charts, but they’re not the most insightful visual we can provide. A bubble chart has no structure, so it’s not possible to compare different values. They’re similar to pie charts in that it’s difficult to draw insight.

That said, there is some creative value to the viewer. Bubble charts grab attention, which means you can use them to draw in users and show them more insightful charts.

Why display data in different ways?

In all of the example charts above, I used the same two data tables. What this means is that any given data set can be represented in many different data displays . So why would we represent data in different ways?

The simple answer is that it helps the viewer think differently about information . When I showed the stacked area chart of number of checkouts for two different books, it appeared as though the books followed the same trend.

However, when I showed them in an unstacked view, we clearly see that the book colored orange performed slightly better in Q1 – Q3, whereas the book colored blue performed better in Q4.

Displaying data in different ways allows us to think differently about it — to gain insights and understand it in new and creative ways.

Another reason for using multiple data displays is for an analyst to cater to his/her audience . For example, take another look at the bullet graph and scatter plot above.

Managers in a book selling firm are likely very interested in the performance of sales in Q1 vs Q2, so the bullet graph is better for them . However, a writer looking to better understand the relationship between sales of individual books in Q1 vs Q2 will prefer the scatter plot .

Which data display shows the number of observations?

I’m not sure where this question comes from, but it’s asked a lot. An observation is nothing more than one line in a data table, and many wonder what data display shows the total number of these lines.

In short, any data display can show the number of observations in the underlying data set — it’s only a question of granularity of dimensions. However, the most common data display showing number of observations is a scatter plot. As long as you include a measure at the observation level of detail, the scatter will show the number of observations.

If the goal, however, is simply to count the number of observations, most data table software have a simple count function . In Excel, it’s COUNTA(array of one column). In Tableau, it’s COUNT([observation metric]).

What data display uses intervals and frequency?

Another common question, and this one is easy. Take another look at the histogram above. It pinpoints intervals and counts the number of records within that interval. The number of records is also know as frequency. In short, the data display showing intervals and frequency is a histogram.

Which data display is misleading?

You may have heard the term “misleading data.” Unfortunately, misleading data is a necessary evil in the world of informatics. While any data display can be misleading, the most common examples are bar charts in which an axis is made non-zero and line charts in which the data axis (x-axis) is reversed. The first results in an inflated visual value of bars, and the second results in the reverse interpretation of a trend over time.

Misleading data is a huge topic and is outside the scope of this article. If you’re interested in it, check out these articles:

• Data Distortion: What is it? And how is it misleading?
• Pros & Cons of Data Visualization: the Good, Bad, & Ugly

Data Visualization vs Data Display

Alas, we arrive at what is likely the most common source of confusion surrounding data displays: the difference between a data display and a data visualization.

In most cases, there is no difference between a data visualization and a data display — they are synonyms. However, the term “visualization” is a buzzword that invites the image of aesthetically-pleasing data displays, whereas “data display” can refer to visualizations OR aesthetically-simple charts and graphs like those used in academic papers.

What is a data display in qualitative research?

Since data is quantitative, applying data displays to qualitative research can be challenging — but it’s 100% possible. It requires converting qualitative data into quantitative data . In most cases qualitative data consists of words, so “conversion” involves counting words. In practice, counting manifests as (1) idea coding and/or (2) determining word frequency .

Idea coding consists of reading through text and assigning designated phrases per idea covered, then counting the number of times these phrases appear . Word frequency consists of passing a text through a word analyzer software and counting the most common combinations . The details around these techniques are outside the scope of this article, but you can learn more in the article Qualitative Content Analysis: a Simple Guide with Examples .

Once converted into numbers, we can display qualitative data just like we display quantitative data in the 17 Actionable Visualizations . So, how do we answer the question “what is a data display in qualitative research?”

In short, a data display in qualitative research is the visualization of words after they have been quantified through idea coding, word frequency, or both.

Data Display Tools and Products (5 Examples)

Any article on data displays worth its salt shows data tools. Here are five free data visualization tools you can get started with today.

Admittedly, Excel is not a data display tool in the strict sense of the term. However, it offers several user-friendly visualization options. You can navigate to them via the Insert ribbon. The options include:

• Column charts,
• Bar charts,
• Line graphs,
• Histograms,
• Box & Whisker Plots,
• Waterfall charts,
• Pie charts (but don’t use them!),
• Scatter plots, and
• Combo charts

They’re displayed in the icons as shown in the below picture:

Tableau is the leading data visualization software, and for good reason. It’s what I used to build all of the data displays earlier in this article. Tableau interacts directly with data stored in Excel, on a local server, in Google Sheets, and many other sources.

It provides one of the most flexible interfaces available, allowing you to rapidly “slice and dice” different dimensions and measures and switch between visualizations with the click of a button.

The one downside is that Tableau takes some time to learn . Its flexibility requires the use of many functional buttons, and you’ll need some time to understand them.

You can download the free version of the paid product called Tableau Public .

I only recently learned about Flourish. It’s a pre-set data display tool that’s much less flexible than Tableau, but much easier to get started on. Given a set of static and dynamic charts to choose from, Flourish prompts you to fill in data in a format compatible with the chart.

Have you seen those “rat race” videos where GDP per country or market cap by company is shown over time? With the leaders moving to the top over the years? You can build that in Flourish .

Infogram allows the creation of a fixed number of data displays, similar to those available in Excel. It’s added value, however, is that Infogram is aesthetically pleasing and it’s a browser-based tool. This means you won’t bore an audience with classic Excel charts, and it means you can access your work anywhere you have an internet connection. Check out Infogram here .

Datawrapper

Datawrapper is similar to Flourish and Infogram. The key difference is that you have a wide variety of displays to choose from like Flourish, but it requires a standard input format like Infogram.

At the end of the day, Tableau is by far the best visualization software in terms of flexibility and power. But if you’re looking for a simple, accessible solution, Flourish, Infogram and Datawrapper will do the trick. Try them out to see which is best for you!

Data Display in Excel

A quick note on data display in Excel: in addition to using the visualizations discussed above on a normal range, you can use them on a pivot table.

What are the steps to display data in a pivot table?

Imagine you have a normal range in Excel that you want to convert to a pivot table. You can do so by highlighting the range and navigating to Insert > Tables > Pivot Table. Once the field appears, drag the dimensions and measures you want into the fields.

From there, you can create a pivot table data display by placing your cursor anywhere in the pivot table and navigating to Insert and clicking a visualization. The data display is now connected to the pivot table and will change with it.

Data Display from Database

So far we’ve discussed the data display definition, types of displays, answered some common questions, compared data displays and data visualization, covered data displays with qualitative data, and explored common tools.

All of these items can be considered “front-end” topics, meaning they don’t require you to work with programming languages and underlying datasets. However, it’s worth addressing how to create a data display from a database.

At its core, a database is a storage location with 2 or more joinable tables. While IT professionals would laugh at me saying this, two tabs in Excel with a data table in each could technically be considered a database . This means that any time you create a data display or visualization using data from a structure of this nature, you’re displaying from a database.

But this would be an oversimplification !

In reality, serious databases are stored on servers accessible with SQL. Displaying data from those databases requires a tool, such as Tableau, capable of accessing those servers directly. If not, you would need to export them into Excel first, then display the data with a tool.

In short, displaying data from a database requires either a powerful visualization tool or preparatory export from the database into Excel using SQL.

What is a data display in math?

Because we’re talking about data, the numeric affiliation with math comes up often. Data displays are used in math insofar as math is used in almost every discipline. This means we don’t need to explore it extensively. However, the specific use of data displays in statistics is important.

Data Displays and Statistics

Statistics is the specific discipline of math that deals with datasets. More specifically, it deals with descriptive and inferential analytics . In short, descriptive analysis tries to understand distribution. Distribution can be broken down into central tendency and dispersion.

Inferential analysis, on the other hand, uses descriptive statistics on known data to make assumptions about a broader population. If a penny is copper (descriptive), and all pennies are the same, then all pennies are copper (inferential).

Data displays in statistics can be used for both descriptive and inferential analysis. They help the analyst understand how well their models represent the data.

Much of statistics is polluted with discipline-specific jargon, and it’s not the goal of this article to deep-dive into that world. Instead, I encourage you to get ahold of one of the data display tools we discussed and start playing with them. This is the best way to learn data display skills .

At AnalystAnswers.com, I’m working to build task-based packets to help you improve your skills. So stay tuned for those!

If you found this article helpful, check out more free content on data, finance, and business analytics at the AnalystAnswers.com homepage !

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

This practice is crucial in the data science process, as it helps to make data more understandable and actionable for a wide range of users, from business professionals to data scientists.

Table of Content

What is Data Visualization?

Why is data visualization important, 1. data visualization discovers the trends in data, 2. data visualization provides a perspective on the data, 3. data visualization puts the data into the correct context, 4. data visualization saves time, 5. data visualization tells a data story, types of data visualization techniques, tools for visualization of data, advantages and disadvantages of data visualization, best practices for visualization data, use-cases and applications of data visualization.

Data visualization translates complex data sets into visual formats that are easier for the human brain to comprehend. This can include a variety of visual tools such as:

• Charts : Bar charts, line charts, pie charts, etc.
• Graphs : Scatter plots, histograms, etc.
• Maps : Geographic maps, heat maps, etc.
• Dashboards : Interactive platforms that combine multiple visualizations.

The primary goal of data visualization is to make data more accessible and easier to interpret, allowing users to identify patterns, trends, and outliers quickly. This is particularly important in the context of big data, where the sheer volume of information can be overwhelming without effective visualization techniques.

Types of Data for Visualization

Performing accurate visualization of data is very critical to market research where both numerical and categorical data can be visualized, which helps increase the impact of insights and also helps in reducing the risk of analysis paralysis. So, data visualization is categorized into the following categories:

• Numerical Data 
• Categorical Data

Let’s understand the visualization of data via a diagram with its all categories.

To read more on this refer to: Categories of Data Visualization

Let’s take an example. Suppose you compile visualization data of the company’s profits from 2013 to 2023 and create a line chart. It would be very easy to see the line going constantly up with a drop in just 2018. So you can observe in a second that the company has had continuous profits in all the years except a loss in 2018.

It would not be that easy to get this information so fast from a data table. This is just one demonstration of the usefulness of data visualization. Let’s see some more reasons why visualization of data is so important.

The most important thing that data visualization does is discover the trends in data. After all, it is much easier to observe data trends when all the data is laid out in front of you in a visual form as compared to data in a table. For example, the screenshot below on visualization on Tableau demonstrates the sum of sales made by each customer in descending order. However, the color red denotes loss while grey denotes profits. So it is very easy to observe from this visualization that even though some customers may have huge sales, they are still at a loss. This would be very difficult to observe from a table.

Visualizing Data provides a perspective on data by showing its meaning in the larger scheme of things. It demonstrates how particular data references stand concerning the overall data picture. In the data visualization below, the data between sales and profit provides a data perspective concerning these two measures. It also demonstrates that there are very few sales above 12K and higher sales do not necessarily mean a higher profit.

It isn’t easy to understand the context of the data with data visualization. Since context provides the whole circumstances of the data, it is very difficult to grasp by just reading numbers in a table. In the below data visualization on Tableau , a TreeMap is used to demonstrate the number of sales in each region of the United States. It is very easy to understand from this data visualization that California has the largest number of sales out of the total number since the rectangle for California is the largest. But this information is not easy to understand outside of context without visualizing data.

It is definitely faster to gather some insights from the data using data visualization rather than just studying a chart. In the screenshot below on Tableau, it is very easy to identify the states that have suffered a net loss rather than a profit. This is because all the cells with a loss are coloured red using a heat map, so it is obvious states have suffered a loss. Compare this to a normal table where you would need to check each cell to see if it has a negative value to determine a loss. Visualizing Data can save a lot of time in this situation!

Data visualization is also a medium to tell a data story to the viewers. The visualization can be used to present the data facts in an easy-to-understand form while telling a story and leading the viewers to an inevitable conclusion. This data story, like any other type of story, should have a good beginning, a basic plot, and an ending that it is leading towards. For example, if a data analyst has to craft a data visualization for company executives detailing the profits of various products, then the data story can start with the profits and losses of multiple products and move on to recommendations on how to tackle the losses.

To find out more points please refer to this article: Why is Data Visualization so Important?

Now, that we have understood the basics of Data Visualization, along with its importance, now will be discussing the Advantages, Disadvantages and Data Science Pipeline (along with the diagram) which will help you to understand how data is compiled through various checkpoints.

Various types of visualizations cater to diverse data sets and analytical goals.

• Bar Charts: Ideal for comparing categorical data or displaying frequencies, bar charts offer a clear visual representation of values.
• Line Charts: Perfect for illustrating trends over time, line charts connect data points to reveal patterns and fluctuations.
• Pie Charts: Efficient for displaying parts of a whole, pie charts offer a simple way to understand proportions and percentages.
• Scatter Plots: Showcase relationships between two variables, identifying patterns and outliers through scattered data points.
• Histograms: Depict the distribution of a continuous variable, providing insights into the underlying data patterns.
• Heatmaps: Visualize complex data sets through color-coding, emphasizing variations and correlations in a matrix.
• Box Plots: Unveil statistical summaries such as median, quartiles, and outliers, aiding in data distribution analysis.
• Area Charts: Similar to line charts but with the area under the line filled, these charts accentuate cumulative data patterns.
• Bubble Charts: Enhance scatter plots by introducing a third dimension through varying bubble sizes, revealing additional insights.
• Treemaps: Efficiently represent hierarchical data structures, breaking down categories into nested rectangles.
• Violin Plots : Violin plots combine aspects of box plots and kernel density plots, providing a detailed representation of the distribution of data.
• Word Clouds : Word clouds are visual representations of text data where words are sized based on their frequency.
• 3D Surface Plots : 3D surface plots visualize three-dimensional data, illustrating how a response variable changes in relation to two predictor variables.
• Network Graphs : Network graphs represent relationships between entities using nodes and edges. They are useful for visualizing connections in complex systems, such as social networks, transportation networks, or organizational structures.
• Sankey Diagrams : Sankey diagrams visualize flow and quantity relationships between multiple entities. Often used in process engineering or energy flow analysis.

Visualization of data not only simplifies complex information but also enhances decision-making processes. Choosing the right type of visualization helps to unveil hidden patterns and trends within the data, making informed and impactful conclusions.

The following are the 10 best Data Visualization Tools

• Zoho Analytics
• IBM Cognos Analytics
• Microsoft Power BI
• SAP Analytics Cloud

To  find out more about these tools please refer to this article: Best Data Visualization Tools

Advantages of Data Visualization:

• Enhanced Comparison: Visualizing performances of two elements or scenarios streamlines analysis, saving time compared to traditional data examination.
• Improved Methodology: Representing data graphically offers a superior understanding of situations, exemplified by tools like Google Trends illustrating industry trends in graphical forms.
• Efficient Data Sharing: Visual data presentation facilitates effective communication, making information more digestible and engaging compared to sharing raw data.
• Sales Analysis: Data visualization aids sales professionals in comprehending product sales trends, identifying influencing factors through tools like heat maps, and understanding customer types, geography impacts, and repeat customer behaviors.
• Identifying Event Relations: Discovering correlations between events helps businesses understand external factors affecting their performance, such as online sales surges during festive seasons.
• Exploring Opportunities and Trends: Data visualization empowers business leaders to uncover patterns and opportunities within vast datasets, enabling a deeper understanding of customer behaviors and insights into emerging business trends.

Disadvantages of Data Visualization:

• Can be time-consuming: Creating visualizations can be a time-consuming process, especially when dealing with large and complex datasets.
• Can be misleading: While data visualization can help identify patterns and relationships in data, it can also be misleading if not done correctly. Visualizations can create the impression of patterns or trends that may not exist, leading to incorrect conclusions and poor decision-making.
• Can be difficult to interpret: Some types of visualizations, such as those that involve 3D or interactive elements, can be difficult to interpret and understand.
• May not be suitable for all types of data: Certain types of data, such as text or audio data, may not lend themselves well to visualization. In these cases, alternative methods of analysis may be more appropriate.
• May not be accessible to all users: Some users may have visual impairments or other disabilities that make it difficult or impossible for them to interpret visualizations. In these cases, alternative methods of presenting data may be necessary to ensure accessibility.

Effective data visualization is crucial for conveying insights accurately. Follow these best practices to create compelling and understandable visualizations:

• Audience-Centric Approach: Tailor visualizations to your audience’s knowledge level, ensuring clarity and relevance. Consider their familiarity with data interpretation and adjust the complexity of visual elements accordingly.
• Design Clarity and Consistency: Choose appropriate chart types, simplify visual elements, and maintain a consistent color scheme and legible fonts. This ensures a clear, cohesive, and easily interpretable visualization.
• Contextual Communication: Provide context through clear labels, titles, annotations, and acknowledgments of data sources. This helps viewers understand the significance of the information presented and builds transparency and credibility.
• Engaging and Accessible Design: Design interactive features thoughtfully, ensuring they enhance comprehension. Additionally, prioritize accessibility by testing visualizations for responsiveness and accommodating various audience needs, fostering an inclusive and engaging experience.

1. Business Intelligence and Reporting

In the realm of Business Intelligence and Reporting, organizations leverage sophisticated tools to enhance decision-making processes. This involves the implementation of comprehensive dashboards designed for tracking key performance indicators (KPIs) and essential business metrics. Additionally, businesses engage in thorough trend analysis to discern patterns and anomalies within sales, revenue, and other critical datasets. These visual insights play a pivotal role in facilitating strategic decision-making, empowering stakeholders to respond promptly to market dynamics.

2. Financial Analysis

Financial Analysis in the corporate landscape involves the utilization of visual representations to aid in investment decision-making. Visualizing stock prices and market trends provides valuable insights for investors. Furthermore, organizations conduct comparative analyses of budgeted versus actual expenditures, gaining a comprehensive understanding of financial performance. Visualizations of cash flow and financial statements contribute to a clearer assessment of overall financial health, aiding in the formulation of robust financial strategies.

3. Healthcare

Within the Healthcare sector, the adoption of visualizations is instrumental in conveying complex information. Visual representations are employed to communicate patient outcomes and assess treatment efficacy, fostering a more accessible understanding for healthcare professionals and stakeholders. Moreover, visual depictions of disease spread and epidemiological data are critical in supporting public health efforts. Through visual analytics, healthcare organizations achieve efficient allocation and utilization of resources, ensuring optimal delivery of healthcare services.

4. Marketing and Sales

In the domain of Marketing and Sales, data visualization becomes a powerful tool for understanding customer behavior. Segmentation and behavior analysis are facilitated through visually intuitive charts, providing insights that inform targeted marketing strategies. Conversion funnel visualizations offer a comprehensive view of the customer journey, enabling organizations to optimize their sales processes. Visual analytics of social media engagement and campaign performance further enhance marketing strategies, allowing for more effective and targeted outreach.

5. Human Resources

Human Resources departments leverage data visualization to streamline processes and enhance workforce management. The development of employee performance dashboards facilitates efficient HR operations. Workforce demographics and diversity metrics are visually represented, supporting inclusive practices within organizations. Additionally, analytics for recruitment and retention strategies are enhanced through visual insights, contributing to more effective talent management.

Data Visualization in Big Data

In the contemporary landscape of information management, the synergy between data visualization and big data has become increasingly crucial for organizations seeking actionable insights from vast and complex datasets. Data visualization, through graphical representation techniques such as charts, graphs, and heatmaps, plays a pivotal role in distilling intricate patterns and trends inherent in massive datasets.

• It acts as a transformative bridge between raw data and meaningful insights, enabling stakeholders to comprehend complex relationships and make informed decisions.
• In tandem, big data, characterized by the exponential growth and diversity of information, provides the substantive foundation for these visualizations.

As organizations grapple with the challenges and opportunities presented by the sheer volume, velocity, and variety of data, the integration of data visualization becomes an indispensable strategy for extracting value and fostering a deeper understanding of complex information. The marriage of data visualization and big data not only enhances interpretability but also empowers decision-makers to derive actionable intelligence from the vast reservoirs of information available in today’s data-driven landscape.

Data visualization serves as a cornerstone in the modern landscape of information interpretation. Its ability to transform complex data into comprehensible visual formats, such as charts and graphs, is instrumental in facilitating better decision-making processes across various sectors.

Visualization of Data -FAQs

What are the 4 main visualization types.

Various forms of data visualization exist, including but not limited to bar charts, line charts, scatter plots, pie charts, and heat maps. These represent commonly used methods for presenting and interpreting data.

What is an example of data visualization?

A pie chart depicting the market share of different smartphone brands in a region, allowing a quick visual comparison of their respective contributions to the overall market.

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Resource Details

June 5, 2020

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram, or map.

The field of data visualization combines both art and data science. While data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data.

This resource explains the fundamentals of data visualization, including examples of different types of data visualizations and when and how to use them to illustrate findings and insights.

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

35 Data Visualization Types to Master the Art of Data

Ready to unlock the power of your data? Brush up on data visualization types that will level-up the information you’re sharing!

Data visualization is all about figuring out how to present data in a way that’s not only visually appealing but also, and more importantly, gets a point across in the most effective way possible.

The problem with relying solely on raw data or basic tables is that they can be confusing, overwhelming, and lack context. Data without clear visualization can miscommunicate information and lead to poor decision-making.

It was business altering when I discovered the various tools and resources for effective data visualization. I especially appreciate how they help transform abstract numbers into tangible visuals, making it easier for everyone – from analysts to stakeholders – to understand complex datasets.

In this post, we’re going to look at the most popular yet effective data visualization types. We’re going to dive deep into each type, illustrating their uses, strengths, and limitations, and offering you a roadmap to transform your data into compelling stories.

So, grab your favorite drink (coffee, I’m thinking), and let’s dive into the many data visualization types!

Categorical Data Visualizations

Categorical data visualizations are an excellent tool for comparing different categories or segments within your dataset. These data visualization types are easy to understand, making them a popular choice for many data analysts.

Think of them as dealing with non-numerical or grouped data, where values fall into a specific category. These are often used to showcase comparisons, distributions, and relationships in a dataset, giving you the power to reveal patterns, trends, and insights that may be otherwise obscured in raw data.

If you’re the type of person who struggles to make sense of seemingly random data points, or if you’re a data enthusiast who loves uncovering hidden trends and insights, you may find this category of data visualization extremely beneficial.

#1: Bar Chart – Making Comparisons Effortless

The bar chart, often also known as a column chart, is a staple in the toolbox of data visualization types. Serving as a simple and effective tool, bar charts facilitate the comparison of data across categories. With one axis dedicated to numerical values and the other representing various categories or subjects under scrutiny, the bar chart brings your data to life.

Whether you choose to orient your bars vertically or horizontally depends largely on the nature of your data. Vertical bar charts place numerical values on the y-axis, offering a quick glance at the size differences, while horizontal bar charts, with numerical values on the x-axis, provide more space for lengthy category labels.

With a bar chart, you can:

• Transform complex datasets into easily understandable visuals.
• Visualize comparisons between different categories.
• Communicate detailed data trends effectively.

An essential tip for leveraging the power of bar charts is considering the complexity of your category labels. If your qualitative data features long or descriptive names, opt for a horizontal bar chart. For an extra layer of creativity, consider using color-coding systems, 3D bars, animated effects, or even photographic backgrounds. Alternatively, a stacked bar chart can illustrate part-to-whole relationships within your categories.

#2: Pie Chart – Showcasing Parts of a Whole

The pie chart ranks high among commonly used data visualizations types, given its simplicity and clarity when demonstrating parts of a whole. The entire circle represents the total, while each individual slice corresponds to a proportion of this total.

Pie charts are ideal for datasets with no more than five or six parts, as this keeps each slice visible and distinguishable. With more than this, slices may become too thin, and with similar values, discerning differences can become challenging. Successful pie charts use contrasting yet harmonious colors, ensuring each slice is visually distinct.

With a pie chart, you can:

• Transform intricate data into easily digestible reports.
• Create clear visualizations of proportional relationships.
• Enhance communication through visual aids.

If your data contains more than six segments, a bar chart could be a more suitable alternative, maintaining the clarity and simplicity of a pie chart while accommodating larger datasets.

#3: Bullet Graph – Compact Data Storytelling

While not as widely recognized as bar or pie charts, bullet graphs pack a punch when it comes to presenting a wealth of information in a compact space. Bullet graphs excel in demonstrating performance against a goal or comparable metric, offering a rich, concise display of key metrics without overwhelming your audience.

Bullet graphs can help you:

• Present performance data relative to a set target or benchmark.
• Highlight measures, drawing attention to whether they fall within an acceptable range.
• Display multiple measures in a confined space, perfect for dashboard presentations.

Remember, the key to successfully using bullet graphs is to provide clear context. A bullet graph comparing current sales to a set target, with color-coded ranges indicating performance levels, can effectively convey a lot of information at a glance. However, they may not be suitable when your data demands a different context or when illustrating data over time.

Ready to get hands-on with these data visualization types? Check out our A to Z list of data visualization tools .

Hierarchical Data Visualizations Types: Revealing Order and Structure

Hierarchical data visualization techniques are invaluable when you’re dealing with data that’s organized into some sort of hierarchy, whether that be nested categories, familial relationships, organizational structures, or rankings. They help to bring out the order and structure inherent in the data, making it easier to understand and interpret. Here, we will delve into three common types: Tree Diagrams, Treemaps, and Sunburst Charts.

#4: Tree Diagrams – Simplifying Complex Structures

The tree diagram, also known as a hierarchical tree, is a visualization tool that clearly delineates hierarchical relationships within your data. This structure comprises ‘nodes’ and ‘edges’, with each node representing a data point and each edge representing the connection between these points.

Using a tree diagram , you can:

• Visualize intricate hierarchical data in a straightforward, logical manner.
• Clarify relationships and connections between various data points.
• Create an easy-to-follow map of data lineage or processes.

One crucial point to note when using tree diagrams is to maintain a logical and straightforward layout. Overcomplication can quickly lead to confusion. Remember that the main aim is to present a hierarchical relationship in the most comprehensible way.

#5: Treemaps – Depicting Hierarchies and Proportions

Treemaps take a slightly different approach to representing hierarchical data. Instead of focusing solely on the hierarchy, they simultaneously demonstrate proportions within the hierarchy through varying sizes of rectangles. Each rectangle represents a data point, with its size proportional to a particular dimension of the data.

Treemaps allow you to:

• Represent hierarchical relationships and proportions simultaneously.
• Accommodate large amounts of data within a confined space.
• Highlight significant data points through size and color variation.

While treemaps can be incredibly insightful, they may not be suitable if your data set involves too many small, similarly sized categories, which may make the map hard to read and interpret.

#6: Sunburst Charts – Circular Representation of Hierarchies

Sunburst charts, also known as radial treemaps, present hierarchical data in a circular format, making them particularly useful for displaying data that wraps around at the end-points (like hours in a day or months in a year). Each layer of the circle represents a level in the hierarchy, with the innermost layer being the top of the hierarchy.

With a sunburst chart , you can:

• Visualize complex hierarchical structures in a unique, engaging manner.
• Demonstrate a full cycle of data effectively.
• Highlight the proportion of different elements at each hierarchical level.

Keep in mind that while sunburst charts can provide a visually appealing way to present hierarchies, they might not be the best choice for data with many hierarchical levels, as the chart may become crowded and difficult to interpret.

Interested in strategies to enhance your data visualizations? We cover this and more in our in-depth guide on Data Visualization Basics .

Multidimensional Data Visualization Types

Sometimes, your data isn’t as simple as comparing two variables, or understanding hierarchical structures. You may be dealing with complex datasets where you need to understand relationships across multiple dimensions. For these situations, you can leverage multidimensional data visualizations such as Scatter Plots, Bubble Charts, and Radar/Spider Charts.

#7: Scatter Plots – Uncovering Correlations

A scatter plot, also known as a scatter chart or scattergram, is a type of visualization that uses dots to represent the values obtained for two different variables – one plotted along the x-axis and the other along the y-axis. This type of chart can be used to display and compare numeric values, such as scientific, statistical, and engineering data.

By using a scatter plot, you can:

• Identify types of correlation between variables, if any.
• Spot any unusual observations in your dataset.
• Forecast trends by using lines of best fit.

While scatter plots can be effective at demonstrating relationships, it’s important to remember that correlation doesn’t always mean causation. Also, scatter plots may not be as effective when dealing with categorical data visualization types.

#8: Bubble Charts – Adding a Third Dimension

A bubble chart is a variation of a scatter plot. Like scatter plots, they display data across two axes, but they add a third dimension, represented by the size of the dots or ‘bubbles’. This third dimension allows you to incorporate even more data into your analysis.

With a bubble chart , you can:

• Display three dimensions of data effectively.
• Show connections and differences in a dataset that would be difficult to express otherwise.
• Highlight significant data points through size variation.

Remember that while bubble charts can be visually engaging and informative, too many bubbles or bubbles that are too similar in size can lead to confusion, so it might not make the best data visualization types. Be careful about the scale of your bubbles – disproportionate sizes can distort data interpretation.

#9: Radar/Spider Charts – Comparing Multivariate Data

Radar or spider charts are a unique way of showing multiple data points in a two-dimensional chart, making them useful for comparing multivariate data to and can really pique interest when thinking about data visualization types.

Each variable is given its own axis, all of which are radially distributed around a central point. Data points are plotted along these axes and connected to form a polygon.

Radar/Spider charts allow you to:

• Compare multiple quantitative variables.
• Understand the strengths and weaknesses of different variables.
• Visualize multivariate data in a compact format.

However, these charts can become messy and hard to read when there are too many variables, or the values are too similar. Also, the area covered by the polygon can sometimes give a misleading impression if the values are not evenly distributed.

Ready to step up your data visualization game? Discover how to take your skills to the next level in our comprehensive guide on Data Visualization Basics .

Sequential Data Visualizations: Tracking Change Over Time

Data that is collected over time holds a unique place in data analysis. Time-series data, or sequential data, has its own set of visualization tools which are effective in showing trends, fluctuations, and patterns over a period.

Tracking metrics and KPIs over time is an excellent way to see trends.

It helps to be able to look at the same data from different perspectives at the same time and see how they fit together. Stephen Few via Tableau Blog

#10: Line Graphs – Highlighting Trends and Fluctuations

A line graph, or line chart, is a powerful tool for showing continuous data, typically over time. It comprises points connected by line segments, with the x-axis often representing time and the y-axis the quantitative variable.

Line graphs enable you to:

• Visualize trends and fluctuations in data over time.
• Compare changes in the same variable across different groups.
• Forecast future trends using historical data.

Line graphs are flexible and straightforward, but they can become cluttered if there are too many lines or time points. Also, they may not effectively represent data where values fluctuate drastically.

#11: Area Charts – Quantifying Changes Over Time

Area charts are similar to line graphs, but with the area below the line filled in. This can be beneficial when you want to demonstrate how a quantity has changed over time, particularly when you want to show the contribution of different components to a total.

With an area chart, you can:

• Visualize the magnitude of trends over time.
• Display the part-to-whole relationships.
• Highlight the total across a trend.

Despite their advantages, area charts can be hard to read if there are too many categories or if the categories overlap significantly.

#12: Stream Graphs – Displaying Density Over Time

A stream graph, also known as a theme river, is a type of stacked area graph which is displaced around a central axis, resulting in a flowing, organic shape. Stream graphs are used to display high-volume datasets, showing the changes in data over time.

Stream graphs allow you to:

• Visualize large sets of sequential data.
• Display the density of data flow over time.
• Highlight anomalies and major events within a dataset.

Stream graphs can be very visually appealing, but they might not be the best choice when precision is key, as it can be difficult to discern the exact values represented.

#13: Gantt Charts – Visualizing Project Timelines

Gantt Charts are an essential tool in project management and are used to illustrate a project schedule. It allows for the representation of the duration of tasks against the progression of time. A Gantt chart is a type of bar chart that shows both the scheduled and completed work over a period.

Using a Gantt chart , you can:

• Plan and schedule projects of all sizes.
• Set realistic timeframes for project completion.
• Monitor progress and stay on track with your plan.

While Gantt Charts are excellent for planning and tracking progress, they can become overly complex for large projects with many tasks or dependencies. In such cases, it’s crucial to maintain and update the chart regularly to reflect the true status of the project.

Geospatial Data Visualizations: Mapping Your Data

When your data is tied to specific geographical locations, traditional graphs and charts may not suffice. This is where geospatial visualizations come in. These data visualization types, such as Maps, Choropleth Maps, and Cartograms, allow you to represent data in relation to real-world locations.

Plus. Who doesn’t love a good map for context?

#14: Maps – Plotting Geographical Data

Maps are one of the most traditional forms of data visualization, providing a straightforward method of representing geographical data. This could be as simple as plotting the location of specific events or as complex as showing data variations across different regions.

With a map , you can:

• Display the geographic distribution of data.
• Identify regional patterns and trends.
• Highlight areas of interest or concern.

While maps are a powerful tool for geospatial data visualization, they may not be as effective when comparing quantities across regions, due to size and proximity variations.

#15: Choropleth Maps – Showing Regional Variations

A Choropleth map uses differing shades or colors to represent statistical data on a predefined geographic area, such as countries, states, or counties. The color intensity represents the quantity of the variable of interest, helping to visualize how this variable changes across the map.

Choropleth maps allow you to:

• Display divided geographic areas that are colored or patterned in relation to a data variable.
• Visualize how a measurement varies across a geographic area.
• Identify regional patterns and variations.

Keep in mind that choropleth maps can sometimes be misleading, as they give equal visual weight to each region, regardless of their size or the number of data points in each region.

#16: Cartograms – Distorting Reality for Clarity

Cartograms are a type of map in which some variable (like population or GDP) is substituted for land area or distance. The geometry or space of the map is distorted to convey the information of this alternate variable.

Cartograms help you to:

• Represent a specific variable more effectively by sizing regions accordingly.
• Compare variables independently from the geographical size of regions.
• Highlight discrepancies in data relative to geographic size.

Remember, though cartograms can provide a powerful representation of data, they can also distort the perception of geographical space, potentially causing confusion.

#17: Heat Maps – Showcasing Density and Intensity

Heat Maps is one of the powerful data visualization types used to represent complex data sets through color gradations. They’re often used to display how a particular quantity or frequency varies across different areas of the map.

For instance, a heat map can show the concentration of population in a region or the intensity of traffic at different times of the day.

With a heat map, you can:

• Represent complex data in an understandable way.
• Identify hotspots or areas with high concentration or activity.
• Spot correlations and patterns in large data sets.

However, heat maps may not be effective when used with data sets with few variations or when individual data points need to be distinct.

#18: Dot Distribution Maps – Representing Location and Frequency

Dot Distribution Maps, also known as dot density maps, are a type of thematic map that uses a dot symbol to show the presence of a feature or phenomenon. They’re used to visualize the geographical distribution of a particular attribute, such as population density in different regions.

Using a dot distribution map, you can:

• Depict spatial patterns or the geographical distribution of a particular phenomenon.
• Indicate the presence or frequency of an occurrence.
• Provide a visual representation of raw data.

Remember, the interpretation of dot distribution maps can be somewhat subjective, and they may not provide a clear picture of the data if the dots are too close together, overlapping, or too spread out.

#19: Parallel Coordinates – Multidimensional Analysis

Parallel Coordinates are an exceptional type of visualization used to plot individual data elements across multiple dimensions. Each data attribute has its parallel vertical axis, and values are plotted as points on each axis, connected by line segments. This visualization type is particularly useful when dealing with multivariate data.

When you use parallel coordinates, you can:

• Explore and analyze multidimensional numerical data.
• Detect correlations, outliers, and trends across multiple dimensions.
• Compare multiple variables without losing sight of individual data points.

However, parallel coordinates may not be as effective when dealing with large data sets due to overplotting. They also require a bit of learning to interpret accurately.

#20: Matrix Plots – Complex Comparisons Simplified

Matrix Plots or Matrix Charts provide a grid-like visual representation of data. Each cell in the grid represents a specific value, often using color to denote this value. It’s a great way to visualize large amounts of data and understand the correlation between different variables.

With a matrix plot, you can:

• Represent complex and large data in a simplified and concise manner.
• Compare multiple variables at once.
• Spot patterns and correlations quickly.

Keep in mind that matrix plots can be less intuitive to understand at first glance and may not be suitable when you want to emphasize individual data points.

#21: Radar Charts – Multivariate Observations

Radar Charts, also known as Spider Charts, use a circular display with several different quantitative axes starting from the same point for a detailed view of data. Each variable has its axis, and the data points are connected, forming a polygon. Radar charts are best used when you want to observe which variables have similar values or if there are any outliers amongst them.

Using radar charts, you can:

• Understand the pattern of each individual data series.
• Highlight similarities or differences between different groups.

Remember, radar charts can become cluttered and hard to read when used with many variables or categories. Additionally, they can distort data perception when the axes aren’t uniformly scaled.

#22: Word Clouds – Textual Emphasis

Word Clouds, also known as tag clouds, depict textual data where the size of each word represents its frequency or importance in a body of text. They are a fun and visually appealing way to highlight popular or high-impact words, with larger-sized words indicating higher frequency or importance.

With Word Clouds, you can:

• Visualize textual data, emphasizing popular or recurring themes.
• Analyze and present customer feedback, social media sentiment, or keyword research.
• Create visually engaging presentations of textual content.

However, keep in mind that Word Clouds are best used for illustrative purposes rather than deep analysis, as they lack precise quantitative values.

#23: Highlight Tables – Focus on Categories

Highlight Tables take data tables a step further by adding color to represent values, helping you focus on specific categories. The color intensity reflects the value in the cell, offering an at-a-glance overview of the data.

Using Highlight Tables, you can:

• Add an extra layer of detail to a basic table.
• Bring focus to high or low values in a large dataset.
• Easily compare categorical data.

Remember that while highlight tables are useful for bringing attention to specific data points, they can become overwhelming and difficult to interpret if they’re too complex or have too many categories.

#24: Bubble Clouds – Multidimensional Textual Visualization

Bubble Clouds, sometimes called Circle Packing or Bubble Charts, visualize data hierarchically as a cluster of circles. The size and color of each circle can represent additional variables. Bubble Clouds can present numeric, categorical, or textual data and are helpful when the data has many layers of categorization.

With Bubble Clouds, you can:

• Represent multilayered or hierarchical data.
• Compare and contrast different categories and subcategories.
• Add visual interest to complex datasets.

Keep in mind, however, that like with many other visually intense plots, Bubble Clouds can be challenging to understand and interpret if overused or if they include too many categories or subcategories.

Unique and Complex Data Visualizations

These data visualization types are less common but can provide unique insights when used correctly. They often display more complex data structures or more specific types of data and are best used when simpler visualizations fall short.

#25: Streamgraphs – Show Volume Over Time

Streamgraphs are stacked area charts with smooth, flowing shapes, used to visualize changes in data over time. The aesthetic appeal of streamgraphs often makes them a popular choice for public data visualizations.

With Streamgraphs, you can:

• Display high-volume data over time in a visually engaging way.
• Showcase patterns and trends in large datasets.
• Highlight the magnitude of change between different categories over time.

However, Streamgraphs can be harder to read and interpret than basic line or bar charts due to their flowing shapes, so it’s essential to consider your audience’s data literacy.

#26: Waterfall Charts – Bridge the Gap

Waterfall charts are a form of data visualization that helps demonstrate how an initial value is affected by subsequent positive and negative values. It effectively showcases the cumulative effect of sequential data, providing a ‘bridge’ from one data point to the next, hence the name “waterfall.”

With Waterfall Charts, you can:

• Visualize the cumulative effect of sequential positive and negative values.
• Show how an initial value is adjusted to a final value.
• Depict the incremental changes in a metric over time or between categories.

Keep in mind that Waterfall Charts can become complex and hard to interpret if they contain too many categories or steps.

#27: Chord Diagrams – Visualizing Inter-Relationships

Chord Diagrams are circular charts used to display the inter-relationships between data in a matrix. The data points are arranged around a circle with the relationships depicted as arcs connecting the data points.

With Chord Diagrams, you can:

• Represent complex inter-relationships between different data points.
• Visualize network structures or flow data.
• Present multidimensional data in a single plot.

Chord Diagrams are complex and require a higher degree of data literacy to interpret correctly. Therefore, it’s advisable to use them when your audience has a good understanding of the data and the relationships being represented.

#33: Heatmaps – Visualize Magnitude of Phenomena

Heatmaps are data visualizations that use color-coding to represent different values of data. Heatmaps are excellent tools for displaying large amounts of data and showing variance across multiple variables, helping to visualize complex data sets.

With Heatmaps, you can:

• Visualize large amounts of data in a compact space.
• Display variations across multiple variables.
• Understand complex data sets intuitively through color differentiation.

However, Heatmaps can become hard to interpret when there are too many categories or if the color differentiation isn’t clear.

#34: Dot Distribution Maps – Geographical Representation of Data

Dot Distribution Maps are used to show the geographical distribution of phenomena. Each dot represents a specific quantity of the phenomena at a particular location. They are most effective when you want to show density or distribution over a geographic area.

With Dot Distribution Maps, you can:

• Show geographic distribution of a single category or multiple categories.
• Highlight density or concentration in specific areas.
• Represent large datasets on a geographical layout.

Dot Distribution Maps can become confusing when there are too many dots or categories, so it’s essential to use them judiciously.

#35: Bubble Clouds – Multi-Dimensional Visualizations

Bubble clouds are similar to scatter plots but with an additional dimension represented by the size of the bubbles. The X and Y axes represent two dimensions, while the size (and sometimes color) of the bubbles represent additional dimensions.

• Visualize multi-dimensional data in a single plot.
• Show relationships and disparities between data points.
• Highlight the significance of specific data points using the bubble size.

Bubble Clouds can become complex if there are too many bubbles or if the bubbles overlap, making it hard to interpret the data accurately.

Remember, while adding more types of visualizations to your list can make it comprehensive, the key is to help your reader understand when and how to use each type effectively.

The Art of Choosing the Right Visualization: Concluding Thoughts

Navigating the vast landscape of data visualizations can initially seem like a daunting task, but with the right understanding and tools, it transforms into an exciting journey. Remember, data visualizations are a powerful medium to convey complex information in an easily digestible and engaging way. However, the effectiveness of your visualization hinges on choosing the right type.

When deciding which visualization to use, here are some fundamental aspects to consider:

1. The Nature of Your Data: The type and structure of your data are key determinants in your choice of visualization. Numerical data might be best served by bar or line charts, while geographical data can be presented as a map. Categorical data, on the other hand, might warrant a pie chart or a treemap.

2. The Message You Want to Convey: What’s the story you want to tell with your data? Are you highlighting a trend, comparing items, or showing a relationship? The goal of your communication heavily influences your choice.

3. The Audience: Consider who will be interpreting your visualization. What’s their level of data literacy? Are they familiar with more complex visualizations or should you stick to the basics? Tailoring your visualization to your audience ensures your data story is received as intended.

4. Simplicity vs. Complexity: While some visualizations can depict complex, multi-dimensional data, simplicity often leads to better understanding. If a simpler visualization can tell the same story, it might be the better choice.

5. Trial and Experimentation: Don’t be afraid to experiment with different visualizations. Often, it’s not until you see your data in several visual forms that the most effective one becomes apparent.

In conclusion, the art of data visualization lies in striking the balance between aesthetic appeal and functional communication. The right visualization accentuates your data’s story, driving insight and aiding decision-making. Each type of data visualization has its strengths and appropriate uses, so choose wisely and let your data shine. And always remember, the ultimate aim of data visualization is not just to make data look pretty, but to make it meaningful and accessible for everyone.

If you’re intrigued by the possibilities of data visualization, learn about the key skills you need to master in our essential guide on Data Visualization Basics .

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I'm Renee, the creator behind Coffee Break Data, where my MS in Data Analytics fuels my drive to simplify the complex. I'm here to boost your data confidence, one insightful—and caffeinated—moment at a time.

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What is Data Visualization

Definition:

Data visualization is a general term that describes any effort to help people understand the meaning of data by placing it in a visual context. For example, patterns, trends, and correlations that might not be detected in text-based data can be exposed and recognized more easily with data visualization software.

Data visualization has its origin in the birth of Web 2.0 as a result of the abundance of interpretable data and the need to create methods for them, since then more and more types of data visualizations are available.

• 1 Types of data visualizations
• 2 Importance of data visualization

Types of data visualizations

There are many ways in which we can arrange the data for interpretation, as many as we want to give them. Some of the most important would be the following:

• Tables: The data are presented by tables formed by rows and columns so that we can associate contents quickly, it is also a very useful tool to perform mathematical calculations and that has become enormously popular thanks to the success of programs such as Microsoft Excel.
• Graphs: Graphs or graphs is a type of visual representation of very visual data with the aim of expressing the numerical relationship that the data keeps with each other at a glance. The most used visual resources are lines, vectors, surfaces, bars, etc …
• Infographics: infographics are combinations of images and texts of an explanatory nature and with a careful aesthetic with the aim of communicating information in a visual way.
• Treemap : Treemaps represent hierarchical data using nested figures by means of rectangles.
• Word clouds are visual representations of the words that make up a website or a text, being the largest those that appear more times.

Images can include interactive capabilities, allowing users to manipulate or inquire into the data for query and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur can also be included.

Importance of data visualization

Because of the way the human brain processes information, data visualization is very important. Using tables or graphs to view large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick and easy way to convey concepts in a universal way, and you can experiment with different scenarios, simply by making small adjustments.

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Painting Pictures with Data: The Power of Visual Representations

Picture this. A chaotic world of abstract concepts and complex data, like a thousand-piece jigsaw puzzle. Each piece, a different variable, a unique detail.

Alone, they’re baffling, nearly indecipherable.

But together? They’re a masterpiece of visual information, a detailed illustration.

American data pioneer Edward Tufte , a notable figure in the graphics press, believed that the art of seeing is not limited to the physical objects around us. He stated, “The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing.”

It’s in this context that we delve into the world of data visualization. This is a process where you create visual representations that foster understanding and enhance decision making.

It’s the transformation of data into visual formats. The information could be anything from theoretical frameworks and research findings to word problems. Or anything in-between. And it has the power to change the way you learn, work, and more.

And with the help of modern technology, you can take advantage of data visualization easier than ever today.

What are Visual Representations?

Think of visuals, a smorgasbord of graphical representation, images, pictures, and drawings. Now blend these with ideas, abstract concepts, and data.

You get visual representations . A powerful, potent blend of communication and learning.

As a more formal definition, visual representation is the use of images to represent different types of data and ideas.

They’re more than simply a picture. Visual representations organize information visually , creating a deeper understanding and fostering conceptual understanding. These can be concrete objects or abstract symbols or forms, each telling a unique story. And they can be used to improve understanding everywhere, from a job site to an online article. University professors can even use them to improve their teaching.

But this only scratches the surface of what can be created via visual representation.

Types of Visual Representation for Improving Conceptual Understanding

Graphs, spider diagrams, cluster diagrams – the list is endless!

Each type of visual representation has its specific uses. A mind map template can help you create a detailed illustration of your thought process. It illustrates your ideas or data in an engaging way and reveals how they connect.

Here are a handful of different types of data visualization tools that you can begin using right now.

1. Spider Diagrams

Spider diagrams , or mind maps, are the master web-weavers of visual representation.

They originate from a central concept and extend outwards like a spider’s web. Different ideas or concepts branch out from the center area, providing a holistic view of the topic.

This form of representation is brilliant for showcasing relationships between concepts, fostering a deeper understanding of the subject at hand.

2. Cluster Diagrams

As champions of grouping and classifying information, cluster diagrams are your go-to tools for usability testing or decision making. They help you group similar ideas together, making it easier to digest and understand information.

They’re great for exploring product features, brainstorming solutions, or sorting out ideas.

3. Pie Charts

Pie charts are the quintessential representatives of quantitative information.

They are a type of visual diagrams that transform complex data and word problems into simple symbols. Each slice of the pie is a story, a visual display of the part-to-whole relationship.

Whether you’re presenting survey results, market share data, or budget allocation, a pie chart offers a straightforward, easily digestible visual representation.

4. Bar Charts

If you’re dealing with comparative data or need a visual for data analysis, bar charts or graphs come to the rescue.

Bar graphs represent different variables or categories against a quantity, making them perfect for representing quantitative information. The vertical or horizontal bars bring the data to life, translating numbers into visual elements that provide context and insights at a glance.

Visual Representations Benefits

1. deeper understanding via visual perception.

Visual representations aren’t just a feast for the eyes; they’re food for thought. They offer a quick way to dig down into more detail when examining an issue.

They mold abstract concepts into concrete objects, breathing life into the raw, quantitative information. As you glimpse into the world of data through these visualization techniques , your perception deepens.

You no longer just see the data; you comprehend it, you understand its story. Complex data sheds its mystifying cloak, revealing itself in a visual format that your mind grasps instantly. It’s like going from a two dimensional to a three dimensional picture of the world.

2. Enhanced Decision Making

Navigating through different variables and relationships can feel like walking through a labyrinth. But visualize these with a spider diagram or cluster diagram, and the path becomes clear. Visual representation is one of the most efficient decision making techniques .

Visual representations illuminate the links and connections, presenting a fuller picture. It’s like having a compass in your decision-making journey, guiding you toward the correct answer.

3. Professional Development

Whether you’re presenting research findings, sharing theoretical frameworks, or revealing historical examples, visual representations are your ace. They equip you with a new language, empowering you to convey your message compellingly.

From the conference room to the university lecture hall, they enhance your communication and teaching skills, propelling your professional development. Try to create a research mind map and compare it to a plain text document full of research documentation and see the difference.

4. Bridging the Gap in Data Analysis

What is data visualization if not the mediator between data analysis and understanding? It’s more than an actual process; it’s a bridge.

It takes you from the shores of raw, complex data to the lands of comprehension and insights. With visualization techniques, such as the use of simple symbols or detailed illustrations, you can navigate through this bridge effortlessly.

5. Enriching Learning Environments

Imagine a teaching setting where concepts are not just told but shown. Where students don’t just listen to word problems but see them represented in charts and graphs. This is what visual representations bring to learning environments.

They transform traditional methods into interactive learning experiences, enabling students to grasp complex ideas and understand relationships more clearly. The result? An enriched learning experience that fosters conceptual understanding.

6. Making Abstract Concepts Understandable

In a world brimming with abstract concepts, visual representations are our saving grace. They serve as translators, decoding these concepts into a language we can understand.

Let’s say you’re trying to grasp a theoretical framework. Reading about it might leave you puzzled. But see it laid out in a spider diagram or a concept map, and the fog lifts. With its different variables clearly represented, the concept becomes tangible.

Visual representations simplify the complex, convert the abstract into concrete, making the inscrutable suddenly crystal clear. It’s the power of transforming word problems into visual displays, a method that doesn’t just provide the correct answer. It also offers a deeper understanding.

How to Make a Cluster Diagram?

Ready to get creative? Let’s make a cluster diagram.

First, choose your central idea or problem. This goes in the center area of your diagram. Next, think about related topics or subtopics. Draw lines from the central idea to these topics. Each line represents a relationship.

While you can create a picture like this by drawing, there’s a better way.

Mindomo is a mind mapping tool that will enable you to create visuals that represent data quickly and easily. It provides a wide range of templates to kick-start your diagramming process. And since it’s an online site, you can access it from anywhere.

With a mind map template, creating a cluster diagram becomes an effortless process. This is especially the case since you can edit its style, colors, and more to your heart’s content. And when you’re done, sharing is as simple as clicking a button.

A Few Final Words About Information Visualization

To wrap it up, visual representations are not just about presenting data or information. They are about creating a shared understanding, facilitating learning, and promoting effective communication. Whether it’s about defining a complex process or representing an abstract concept, visual representations have it all covered. And with tools like Mindomo , creating these visuals is as easy as pie.

In the end, visual representation isn’t just about viewing data, it’s about seeing, understanding, and interacting with it. It’s about immersing yourself in the world of abstract concepts, transforming them into tangible visual elements. It’s about seeing relationships between ideas in full color. It’s a whole new language that opens doors to a world of possibilities.

The correct answer to ‘what is data visualization?’ is simple. It’s the future of learning, teaching, and decision-making.

Keep it smart, simple, and creative! The Mindomo Team

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Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

What Is Data Visualization?

Since I founded Perceptual Edge in 2003, data visualization has transitioned from an obscure area of interest to a popular field of endeavor. As with many fields that experience rapid growth, the meaning and practice of data visualization have become muddled. Everyone has their own idea of its purpose and how it should be done. For me, data visualization has remained fairly clear and consistent in meaning and purpose. Here’s a simple definition:

Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data.

You might bristle at the fact that this definition narrows the scope of data visualization to quantitative data. It is certainly true that non-quantitative data may be visualized, but charts, diagrams, and illustrations of this type are not typically categorized as data visualizations. For example, neither a flow chart, nor an organization chart, nor an ER (entity relationship) diagram qualifies as a data visualization unless it includes quantitative information.

The immediate purpose of data visualization is to improve understanding. When data visualization is done in ways that do not improve understanding, it is done poorly. The ultimate purpose of data visualization, beyond understanding, is to enable better decisions and actions.

Understanding the meaning and purpose of data visualization isn’t difficult, but doing the work well requires skill, augmented by good technologies. Data visualization is primarily enabled by skills—the human part of the equation—and these skills are augmented by technologies. The human component is primary, but sadly it receives much less attention than the technological component. For this reason data visualization is usually done poorly. The path to effective data visualization begins with the development of relevant skills through learning and a great deal of practice. Tools are used during this process; they do not drive it.

Data visualization technologies only work when they are designed by people who understand how humans interact with data to make sense of it. This requires an understanding of human perception and cognition. It also requires an understanding of what we humans need from data. Interacting with data is not useful unless it leads to an understanding of things that matter. Few data visualization technology vendors have provided tools that work effectively because their knowledge of the domain is superficial and often erroneous. You can only design good data visualization tools if you’ve engaged in the practice of data visualization yourself at an expert level. Poor tools exist, in part, because vendors care primarily about sales, and most consumers of data visualization products lack the skills that are needed to differentiate useful from useless tools, so they clamor for silly, dysfunctional features. Vendors justify the development of dumb tools by arguing that it is their job to give consumers what they want. I understand their responsibility differently. As parents, we don’t give our children what they want when it conflicts with what they need. Vendors should be good providers.

Data visualization can contribute a great deal to the world, but only if it is done well. We’ll get there eventually. We’ll get there faster if we have a clear understanding of what data visualization is and what it’s for.

20 Comments on “What Is Data Visualization?”

I agree with your basic thesis, and have a couple of questions.

Does your definition accommodate the use of this presentation of the quantity ‘dozen’: 12 How about 1,276? I believe it does. The decimal system was created in order to provide a compact, precise system of encoding quantities. It may be seem too obvious to mention, or an uncommon way of thinking, or but limiting data visualization to geometric forms seems to be a common position.

What about the presence of non-numeric categorical informational elements that provide the context for quantitative visual forms? A bar chart of Sales per Department is likely to be of very little value without the presence of the individual Departments’ names (some other identifier) labeling the bars, or is absent a notice that it’s about Sales.

I’m sympathetic to and agree with your position that “data visualization” has become muddied, often to the point of uselessness, but I can’t help thinking that data visualization also includes those labels/things that provide the identity and context for the quantitative bits.

The fact that data visualizations include categorical labels, in my mind, goes without saying. Displaying quantities without identifying what they represent is meaningless.

If your questions about 12 and 1,276 are asking if numbers qualify as data visualization when they are presented textually (i.e., as alphanumeric characters) rather than graphically (i.e., as geometrical objects), my answer is “No.” Expressing numbers textually qualifies as linguistic communication, not visual communication. Clearly, expressing numbers textually is useful, but they are processed differently than graphics by the brain and therefore serve different purposes. When you arrange numbers tabularly in columns and rows, however, the graphical arrangement does qualify as a form of data visualization, at least in part. For this reason, tables are classified as a type of chart.

Without a doubt, the need for data visualization is needed more than ever, especially with the volume of data that is being generated.

To tell the story that brings insight into the massive data in a concise visual format surely requires hard work.

I also think the tools have improved over the years. While I will not want to favor one tool over the other, my experience in the industry has always favored Tableau as the game changer. Of course, other companies like Microsoft and Qlik woke up to the challenge with tools like Power BI and QlikView.

As the saying goes – “a fool with a tool is still a fool”. I agree with you that the human factor is primary, the improvement with these tools has also contributed to the development of the data visualization field. There are lots of good open-source and commercial tools adopting the freemium model which make the tools accessible by being affordable and available.

This accessibility is needed globally to “contribute a great deal to the world” and make the world better.

The best is yet to come.

As you’ve stated the meaning as remained clear and consistent, I’ll again point to your own previous definition and non-quantitative examples which you described as data visualizations. At the time you made no qualifiers about in requiring quantitative data to be considered a “data visualization”; only that quantitative data could further enrich visualizations of this type.

“Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.” -Stephen Few

“Although data visualization usually features relationships between quantitative values, it can also display relationships that are not quantitative in nature. For instance, the connections between people on a social networking site such as Facebook or between suspected terrorists can be displayed using a node and link visualization. In the following example, people are the nodes, represented as circles, and their relationships are the links, represented as lines that connect them.

Visualizations that feature relationships between entities, such as the people in the example above, can be enriched with the addition of quantitative information as well. For example, the number of times that any two people have interacted could be represented by the thickness of the line that connects them.” – Stephen Few

Your current statement that non-quantitative visualizations are somehow disqualified is reflective of your current opinion and preference.

The purpose of data visualization is to enhance our understanding of a set of data … agreed. This type of enhanced understanding via data visualization can be facilitated for data sets both quantitative and non-quantitative, and yes there are wise and unwise approaches for both.

While I’ve long been supportive of your contributions to the field and share many of the same sentiments you have for so long championed, I still find your definition overly specific to the area of your greatest focus.

As to the rest of your article, I wholeheartedly agree.

Respectfully,

Jonathon Carrell

You appear to be more familiar with my work than I am. The first quote assumed abstract information of a quantitative nature, even though this wasn’t explicitly stated, but the second quote suggests that my thinking has not been as consistent as I thought. I’m curious — where does the second quote appear in my work? Offhand, I don’t remember saying this, but I don’t doubt that I did. The vast majority of work that is classified as data visualization is quantitative in nature. It’s difficult the draw clear boundaries without some exceptions because some types of charts that usually include a quantitative component, such as network diagrams, sometimes display relationships only without anything quantitative.

Both quotes (the definition and non-quantitative example) appear in the chapter you contributed to “The Encyclopedia of Human Computer Interaction” 2nd edition. The chapter title is “Data Visualization for Human Perception”. The online book along with your chapter are available at https://www.interaction-design.org .

In the words of Sherlock Holmes, “I never make exceptions. An exceptions disproves the rule.”

Joking aside, surely we can agree that the world is not so black and white. In my opinion, I’m convinced the inclusion of the word “quantitative” in a working definition is unnecessary. More so, is the statement that any visualization that fails to include quantitative data is somehow disqualified. This comes across as your current personal sentiment stated as fact.

I would also note, your article http://www.perceptualedge.com/articles/visual_business_intelligence/our_fascination_with_all_things_circular.pdf . In it you redesigned a David McCandless chart titled “Colours in Culture”. Both the original and your redesign were purely non-quantitative. No qualifiers about it not being a data visualization are raised in the article. Although, you do point out (and rightly so) the shortcomings of the original design.

My recreation of McCandless’ “Colours in Culture” chart is not a data visualization. I refered to it as an infographic, which is not synonymous with the term data visualization.

What I’m arguing is that a field of study and endeavor should have clear definitions. Even though it is difficult to define data visualization precisely, it is worthwhile to make the attempt. It is absolutely true that data visualization, as I understand and practice it, displays quantitative data. Many visualizations that don’t include quantitative data are closely related to data visualization, so the boundaries can be a little fuzzy at times, but that isn’t a reason to abandon the emphasis on quantitative data.

(By the way, thanks to your comments, I’ve revised my original blog post to say that data visualization, for me, has remained “fairly” clear and consistent in meaning and purpose.)

I’m not disagreeing that a working definition would have value or purpose. I’m stating that in my opinion making overcritical designations for which we may later make exceptions or allow for fuzzy boundaries is wholly unnecessary.

While the majority of useful visualizations are typically quantitative in nature, it isn’t always the case. Typically is the key word here. Adding a numeric value to an otherwise qualitative diagram (such as the example you provided) doesn’t in itself change its form, but rather enhances the context of its content. Even without numbers, it is still data and it is still a visualization.

This is a fruitless debate of semantics.

I leave you to consider this as an alternative:

Data visualization is the graphical representation of data to facilitate understanding and communication.

Instead of focusing on whether the data is quantitative, why not use a different adjective … such as “useful representation”? Then we could further disavow pies, bubble charts, word clouds, and radial gauges. [insert maniacal laughter here]

Semantics actually matter a great deal. The person who came up with the expression “It’s only semantics” deserves his own special place in hell. When we define a field, we need to draw boundaries somewhere. If we define data visualization as you’ve suggested, we open the door to an array of graphical displays that we don’t currently think of as data visualizations, including ER data diagrams, illustrations of all types, comic books, and all representational forms of visual art. The point of a definition is to enable shared meaning between people and over time. Despite the edge cases, drawing the boundaries of data visualization as quantitative displays bases the definition on a distinction in brain function, and it also fits what people usually think of as data visualization. Adding quantitative data to a qualitative visual display actually does change its form. We represent categorical items quite differently than quantitative values. The graphical mechanisms are different. Quantitative displays rely on visual attributes (2-D position, length, size, etc.) that our brains interpret quantitatively (greater or lesser). These differences provide a natural boundary between data visualization and other forms of visual display.

I’ve limited my work (but not interest) to quantitative displays. Doing this has been quite useful.

By the way, I’m not arguing that the definition of data visualizaton that I’ve proposed above is right and every other definition is wrong. Instead, I’m arguing tht this definition makes sense and creates clairity that would benefit the field.

I wasn’t implying semantics don’t matter. Rather, by fruitless, I meant we have no way to prove our positions as being correct or incorrect. My position is that regardless of the type of data being represented (being either qualitative or quantitative), if it is rendered in a graphical format (e.g. A chart or diagram) in such a way that it facilitates greater understanding … that qualifies as data visualization. I have made no suggestion that comic books or other artful works should be considered data visualization. I agree that data visualization is typically geared towards quantitative endeavors. However, there are fields of data analysis that utilze visualizations that are geared towards understanding non-quantitative relationships.

I’m not suggesting opening some Pandora’s box. Rather, I’m suggesting any definition should allow for there being meaningful ways to graph and chart other types of data beyond the quantitative restrictor you’ve suggested.

In any case, I believe we’ve both adequately presented our positions and reasoning.

“In the beginner’s mind there are many possibilities, but in the expert’s there are few.”

Your final quote doesn’t apply to this situation or to me. You used it to suggest the superiority of your position — one that is open to possibilities while mine is supposedly closed minded, which isn’t the case. Closed-mindedness is not a characteristic of expertise, it is a flaw in reasoning. Your position could be every bit as closed minded as mine, but I’ll assume that neither of us is exhibiting this flaw. As I’ve said, this isn’t about being right or wrong. I’m arguing a position that I believe is useful. You’re certainly welcome to disagree.

The definition that you suggested does potentially include comics and visual art as examples of data visualization. You proposed the following definition: “Data visualization is the graphical representation of data to facilitate understanding and communication.” The term “graphical” does not refer only to charts and graphs. The term data refers to facts of all kinds. Comics and visual art can facilitate understanding and communication regarding facts. I’m proposing a definition that narrows the scope of data visualization to quantitiative data in an attempt to avoid the open-endedness and thus, lack of clarity, that your definition invites.

I meant no slight by the quotation, nor did I intend for it to be taken as a device to bolster my position or opinion.

I find your earlier definition perfectly acceptable.

You have felt a need to further narrow the scope whereas I do not. While I certainly respect your reasoning and position, I will continue to disagree that the qualifier is necessary as it implies a rule that in my mind doesn’t exist.

Good day sir.

Back to your original post – I agree and lament that fact that it equally applies to statistical analysis, data “science” (notwithstanding your critique of the term), big data, or any of the related topics. Tools have replaced understanding. I actually like good tools quite a bit, but when you read the job ads you would think that knowledge of particular computer languages, programs, etc. constitutes good data sensemaking. I attribute this to the fact that it is (fairly) easy to document knowledge of tools (hence job applicants and employers emphasize these) but difficult to document the ability to make sense out of data. I find students have the same bias – they want to be able to say they know how to use A, B, C,… because they are not confident that they know how to use data to make better decisions (or don’t know how to convey that to employers).

As the tools multiply, I wonder if this situation will become worse or better. There are more tools to be exposed to and list on resumes and job ads, but at some point people will (?) start to realize that knowing a tool does not mean you know how to use data to improve decisions. Just as knowing a foreign language does not make you able to be successful in business in another country (though, of course, knowing the language can help).

Hi Jonathon,

Just curious and seeking your clarification on the point you are trying to make quoting this:

“Although data visualization usually features relationships between quantitative values, it can also display relationships that are not quantitative in nature. For instance, the connections between people on a social networking site such as Facebook or between suspected terrorists can be displayed using a node and link visualization. In the following example, people are the nodes, represented as circles, and their relationships are the links, represented as lines that connect them.

Visualizations that feature relationships between entities, such as the people in the example above, can be enriched with the addition of quantitative information as well. For example, the number of times that any two people have interacted could be represented by the thickness of the line that connects them.” – Stephen Few”

You quoted Stephen stating that quantitative values can be encoded such as the thickness of the line that connects them. (Other quantitative values can include size of nodes or the distance of the nodes from each other – non-exhaustive).

In you first paragraph, are you suggesting that the node and link visualization such as Facebook/Terrorist network is not quantitative in nature? I would think the number of nodes (connections) is quantitative by itself even without the thickness of lines that connects them, although those would be most useful to make better sense of the data.

Node and link diagrams, at least in the field I work in, are typically used to visualize relationships so that connections that may not have been apparent are easier to recognize. These are relationships are qualitative in nature.

Sure, we can count the number of nodes/relationships and call it quantitative, but that isn’t an effective goal of this type of chart as a table or simple bar graph would serve better. As Stephen pointed out, there are several ways to significantly enrich these types of visuals by also encoding quantitative information, but calling them quantitative in and of themselves seems a stretch to me. What say you Mr. Few?

Very interesting article. I am an RN working in a Data Analytics & Informatics Department in a Hospital system. I create data visualizations utilizing Tibco spotfire. I do not have a background in Data or Analytics. However, I understand the data and the workflows that impede or impact the data. I tried to learn more about the art of data visualization. Requested to go to a TDWI conference this year. I was told no by the VP of our department… “that was not my role. Its not about the graph you choose, its about knowing your audience.” Although it is incredibly important to know your audience, there are skills I can learn to better present and sell my story to my audience. I appreciate your article! I know I am on the right path!

The VP of your department is half right. To visualize data effectively, you must know your audience, but you must also know data visualization best practices. Don’t expect to learn about data visualization by attending TDWI, however. As far as I know, no one who understands data visualzation has worked with TDWI since I stopped working with them several years ago. The best courses in data visualization are taught independently, not through large organizations such as TDWI.

This will require a full book but I think part of the problem is our “obsession” to group data as quantitative and qualitative. This is quite a bit of a gray line. To mention an example; imagine a flow chart or network. These can actually be represented quantitatively (matrix of numbers) which is the reason we can use tools to make analysis in them or visually. Some people may consider one quantitative data while others may consider it qualitative. In this case I will say that using “quantitative” in the definition is not necessary.

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