30 Examples Of Financial Graphs And Charts You Can Use For Your Business

Financial graphs and charts blog by datapine

Table of Contents

1) What Are Financial Graphs?

2) Why You Need Financial Analysis Graphs?

3) The Role Of Financial Data Visualizations

4) Financial Business Graph Examples

5) Best Types Of Financial Graphs

6) Financial Graphs Best Practices

The financial health, flow, and fluidity of your business will ultimately dictate its long-term success, which is why monitoring your money matters carefully, comprehensively, and accurately is absolutely essential.

In our data-driven digital age, 'business intelligent' organizations with the ability to collate, organize, and leverage the insights that are most valuable to their ongoing commercial goals are the ones that are destined to thrive in the long term. Online data visualization takes precedence in business operations, creating more efficient and faster workspaces.

That said, in a time wherein less than two years, around 1.7 megabytes of new information will be generated per second for every single person on the planet, businesses looking to keep their financial affairs fluid need access to KPI dashboards equipped with graphs and charts that are digestible, accurate, and deliver the level of insight required to increase efficiency and stop potential pitfalls before they occur.

In this article, we will present the basic definition of financial graphs, explain why you need them, and answer the most basic of questions: what graphs to include in financial analysis? By presenting data graphically, you will not only make the most out of your monetary information, but simple visuals will do half of the explaining for you. That said, let's get started.

What Are Financial Graphs?

Financial graphs and charts depicted on a dashboard

**click to enlarge**

Financial graphs and charts are visual tools that allow companies to monitor various performance metrics in areas such as liquidity, budgets, expenses, cash flow, and others.  By doing so, they can successfully manage risks to ensure healthy finances and steady growth.

To ensure the best possible performance for a company, conducting regular financial analytics and ensuring the highest quality of data management must be the top priorities of companies, no matter the size. If the finance department raises an alarm, everyone must carefully listen because it concerns the most crucial information and can lead to serious damages if ignored. That's why financial charts must be created with the utmost care and attention. Let's see this in more detail.

Why Do You Need Financial Analysis Graphs?

As humans, we respond to and process visual data better than anything else. That said, when it comes to digesting and taking action upon vital financial metrics and insights, well-designed finance graphs and charts offer the best solution. According to Illinois State University, when it comes to visual aids of this kind, three standards apply: graphs and charts should display unambiguous information, meaningful data, and presently said insights in the most efficient way possible.

Fundamentally, you need them because:

  • You will be able to track your liquidity, cash flow, budgets, and expenses accurately with ease, visually, and automate processes that were oftentimes done manually and with higher risks of errors.
  • By setting the right financial KPIs for your organization, you can set valuable goals that result in growth and success. While there are numerous charts out there, we will explain the invaluable ones for any company.
  • You will be able to make sense of all the financial information and metrics as they will be split into actionable categories and presented intuitively and scannable, no matter the metric you need to include and analyze.
  • Pen and paper or static data will no longer cut it in today’s fast-paced, competitive commercial landscape. As mentioned, manual work is prone to mistakes you can easily avoid by using self-service analytics software .

“Every second of every day, our senses bring in way too much data than we can possibly process in our brains.” – Peter Diamandis , Chairman/CEO, X-Prize Foundation .

Based on this quote alone, it’s clear that by leveraging the power of robust charts that deliver accurate, reliable, and clear-cut financial insights, busy fiscal departments will be able to make sense of the insights before them, resulting in success and evolution, rather than getting bogged down with droves of meaningless and convoluted data.

You can start by creating a simple income vs. expenses graph, add additional charts relevant to your organization's story and finally create a dashboard that will present all your information on a single screen. Let's see this in more detail.

Your Chance: Want to create interactive financial charts and graphs? Explore our 14 day free trial & benefit from great finance management!

Which Role Does Financial Data Visualization Play?

Financial data visualization example of a performance dashboard

Financial data visualizations such as interactive dashboards are complete with charts and graphs that assist in the tracking of all of your core KPIs on one navigable platform. For optimizing reports and detailed analysis, you can check our blog article about financial report examples.

These dashboards give time-stretched finance departments the power to remain on top of the economic performance of the business, resulting in more efficient cash management, accurate expense tracking, comprehensive insights on sales, and additional visual insights geared toward reaching valuable financial goals .

A financial dashboard offers all of the metrics and insights needed to ensure the success of your overall performance, cash flow, cash management, and profit and loss, among others. The business dashboard above not only makes extracting key data swift but is developed in a way that makes communicating your findings to important stakeholders within the business far more simple. And in contrast to a traditional Excel chart, these ones serve real-time data that will prove invaluable to the financial future of your company.

Not only will your company have the opportunity to explore, monitor, and access real-time data, but the interactivity levels are an invaluable resource for managing enormous amounts of information, especially in the financial sector where a small mistake can lead to millions of damages. That's why interaction with the finance charts and graphs is of utmost importance: a single KPI can be viewed in numerous useful ways and angles that static presentations could never offer.

Finally, we cannot avoid mentioning collaboration as one of the top roles of modern financial data visualization tools. As we said before, finances are arguably the most important aspect of any business. If something is wrong with them, most likely, the entire company will suffer. By using BI dashboard tools such as datapine, you will be able to share your financial insights live with the rest of the departments in your company and enhance a collaborative, data-driven work methodology that will optimize your business performance as a whole.

Graph use in financial reports is already a business standard in today's environment. When you add up intelligent tools, automation, stunning visuals, and interactivity for your data visualization process, your finance department will significantly increase productivity and decrease costs. Let's see this through our top 30 financial chart templates.

See Our 30 Financial Business Graph Examples

To put the importance of a dashboard-based financial business graph into perspective, here are 30 templates that cover the most critical money-centric aspects of the ambitious modern business.

1. Gross Profit Margin

Financial chart example: gross profit margin expressed in euros and percentage on a gauge chart

As a key component of our profit & loss dashboard , this indicator has been developed in the form of a traditional pie-style chart but with a more navigable design. The gross profit chart showcases your overall revenue minus the cost of goods sold, divided by your total sales revenue.

Offering a visual representation of your gross profit as well as clearly defined metrics, this chart will allow you to measure your organization’s production efficiency and ultimately enable you to enjoy a greater level of income from each dollar of your sales.

2. Operating Profit Margin

A CFO metric example showing the operating profit margin and its development over time

As another profit and loss-centric financial charting example, this visual is split into an easy-to-digest percentage gauge in addition to a detailed bar chart and will enable you to accurately calculate your Earnings Before Interest and Tax (EBIT).

The higher your operating income, the more profitable your business will potentially be, and this chart will help this metric from dipping through a mix of historical data and priceless real-time insights.

3. Operating Expense Ratio

A financial graph of the operating expenses ratio showing the value of 40%

The operating expense ratio (OER) is also strongly related to the profit and loss area of your finance department's key activities, and this color-coded health gauge will allow you to access the information you need, even at a quick glance.

The OER will give you the power to understand the operational efficiency of your business by comparing your operating expenses to your overall revenue. This is the best visual to show profit and loss, but you do need to connect it with other charts to create a proper financial data story. By monitoring this information regularly, you will be able to decide whether your venture is scalable and make necessary changes to your commercial strategy if you feel it isn't.

4. Current Ratio

Current ratio financial graph closely tied to the management dashboard

Closely tied to the cash management dashboard , this financial graph example is essentially a liquidity ratio that will give you the ability to understand how equipped the business is to pay your most critical obligations in the short term, often within a 6 or 12-month period.

Presented in the form of two visual ratio calculations for swift access to your overall liquidity health or performance as well as a column chart to help you compare data and spot trends, this chart will ensure that you will be able to meet obligations, commit to payments, and quash detrimental roadblocks before they unfold.

5. Net Profit Margin

Financial graph explaining net profit margin

Presented in a similar format to the operational expenses graph, this particular profit graph makes it easy for busy teams to obtain and analyze the information they need to delve deeper into the health of your bottom line, as a result gaining the level of insight required to boost your overall net profits.

As one of the most vital financial KPIs a business can track, this graph is invaluable - and by using this robust, reliable, and intuitive chart, you will be able to iron out any inefficiencies and boost your company’s net profit over time.

6. Accounts Payable Turnover Ratio

Accounts payable turnover ratio financial graph

Regarding the smooth and responsible handling of your company's cash management activities, the accounts payable turnover is another liquidity calculation that will ensure that you are able to pay all of your important expenses within the required deadlines or set timeframes.

The ratio itself changes according to real-time shifts and is displayed in a bold numbered format, while historical or chronological information is presented in the form of a column graph that showcases turnover percentages split into different periods of time. A higher ratio gives suppliers and creditors the assurance that your business pays its bills frequently and is a pivotal metric when negotiating a credit line with a supplier, so it's a chart your company cannot afford to live without.

7. Accounts Receivable Turnover Ratio

Financial chart essential for accounts receivable turnover

Presented as a scannable pie chart, accompanied by vital turnover metrics, this is one of the financial graphs templates that quantifies how swiftly your organization collects your payments owed, thus showcasing your effectiveness in extending credits.

The quicker your business can transform credit sales into cash, the better your liquidity, ultimately translating to a greater ability to handle your short-term liabilities.

8. Return On Assets (ROA)

Return on assets business graph example

This particular example is incredibly useful as it's a financial performance graph that will allow you to understand how well your business can leverage its assets to gain more profit.

Displayed in an easy-to-follow column chart and trend line format, this graph offers an exceptional visual representation of how profitable your organization is concerning your overall asset. The bottom line here is the higher your ROA, the better, particularly when you compare this metric to your direct industry competitors - so this chart is essential to your ongoing financial progress.

9. Return On Equity (ROE)

Financial graph return on equity example

This color-keyed visual offers a distinct measurement of the level of profit you are able to generate for your various shareholders. This particular metric is calculated by dividing your business’s net income (minus the dividends to preferred stocks) by the equity of your shareholders (excluding preferred shares) - not only does this provide an excellent gauge of financial performance, but it’s also effective for comparison with other competitors within your sector.

The better your Return on Equity, the more value you are offering to your shareholders, which will translate to tangible long-term commercial success.

10. Gross Margin Return On Investement (GMROI)

Financial graph example on retail displaying the gross margin return on investment (GMROI)

A great retail KPI is the gross margin return on investment (GMROI). It is an inventory profitability indicator, and it measures the ability of an organization to turn its inventory into cash (after subtracting the inventory costs). The GMROI is calculated by dividing the gross profit by the average inventory costs. The result will tell you how much money you made from the inventory you invested in. An industry standard for this metric is a ratio higher than 1. However, experts recommend that a successful retail store should have a GMROI of around 3. This means the company is making money from its investment. On the contrary, a ratio below 1 means something needs to be done to improve profitability. 

A good practice when it comes to measuring the GMROI is to do it by product category. This way, you can understand which products return more and focus your efforts on those. 

11. IT Cost Break Down

IT costs break down is one of the financial graphs that focuses on the IT department

This financial graph template focuses especially on the IT department, but you can easily adjust it for any other function in a company. We can see how the allocation of costs behaves in designated units (software, hardware, SP, and personnel) while depicting the cost percentage of each of their elements (for instance, administration, development, operations, and support). It's crucial to monitor the expenses graph to identify the main cost drivers on the one hand and possibilities on the other so that the company can adjust its strategies.

If you see that one unit spends significant amounts of resources, it would make sense to investigate further and check if the costs are justified or need more attention. By using relevant online business intelligence software , you can directly interact with all of the values presented in this visual and dig deeper as much as you need. Not only will you cut time into exporting, importing, scrolling, and searching for the right information, but your comprehension will be much quicker since humans are visual creatures, as stated earlier.

12. Cost Avoidance

This financial graph example shows how much costs were saved in a procurement department by the supplier category

Our list of financial data visualization examples wouldn't be complete without cost avoidance. This is one of the graphs that are important to take care of since it tracks how much costs, in this case, of a procurement department, have been saved in a specific time frame. You can also depict a 5-year trend like in our template above and organize it by supplier category. This metric is not as tangible as direct cost savings, for example, but it does bring value to the whole procurement department.

The goal of every procurement professional is to reduce costs in the future (as well as the present), and this chart can easily depict how much these efforts have brought in a company and had a direct impact on the savings processes. For instance, a procurement professional or manager can lock the price of a contract with a vendor to avoid a future price increase. To see more details on procurement operations and management, you can explore our set of procurement metrics .

13. Cash Conversion Cycle

A financial graph depicting the cash conversion cycle in a specific time frame

The cash conversion cycle (CCC) is a metric that helps companies in tracking how much time a company needs to convert their resources into cash from sales. In our example, the formula is also simply depicted so that it can easily be followed: you need to add the day's sales outstanding to the days of inventory outstanding and deduct the days payable outstanding to calculate the cash conversion cycle. If you use a finance graph that you can interact with and calculates the data automatically based on your input, the possibility of making a human error is minimized. You don't have to calculate each time you need a report manually, but you can monitor your data in real time with just a few clicks.

In the end, the goal is always to decrease the cycle as much as possible since an increment can mean that the organization is not fully efficient in its management and operations. It's simple: if the company sells what consumers want to buy, the cycle is quick and healthy. If not, additional corrections need to be performed so that the company doesn't fall into even more serious difficulties.

14. Vendor Payment Error Rate

The vendor payment error rate is depicted with line graphs and in percentage during the last 12 months

Paying invoices and issuing them to vendors, suppliers, or other stakeholders is essential to analyze since it can show how many errors are made and if the accounts payable department is healthy. Of course, mistakes do happen, but sometimes they can be dangerous, so they should be kept at a minimum. Errors may include payment to the wrong entity, overpayments, or double invoicing, and each accounts payable manager usually strives to reduce those errors as much as possible.

A proper financial and analytical report can assist in this process. When you automate and digitalize your analytics process with the help of modern software tools, you don't have to worry that your error rate will increase any time soon. In our example above, we can see that our average error rate is 1.3%, but it has started to decrease in the last few months. The goal should be to have the lowest rate possible and avoid any possible business disputes.

15. Operating Cash Flow

The operating cash flow graph is depicted annually by the last 5 years, with an average annual growth

This cash flow graph gives a clear picture of the business operation's performance. The example presented above shows how much cash a company generated over the course of 5 years. It doesn't include investments and/or non-sales-related income, which basically means it focuses on main cash activities (for example, selling/buying inventory or paying salaries). This graph is important to track since it clearly depicts if a company can sustain its operations and eventually grow. It should be monitored closely and regularly to avoid any potential difficulties.

To create such a chart, there are some data visualization techniques that are useful to study and follow. That way, your analysis and presentation of vital information will yield the best possible value and ensure the most profitable results.

16. Fixed Operating Expenses

Financial graph example tracking fixed operating expenses

As its name suggests, the fixed operating expenses KPI tracks all expenses that need to be mandatorily paid in a specific time period and that will not vary depending on the volume of production or sales. These include salaries, rent and utilities, office supplies, marketing, and insurance, just to name a few. While these expenses are very hard to lower as they are not influenced by the production of the company, it is still fundamental to keep a close eye on them to make sure that they don’t go up too much as they account for a big percentage of revenue.

17. Variable Operating Expenses

Financial graph example tracking variable operating expenses

On the other hand, variable operating expenses are all expenses that can vary depending on the production level of the company. We are talking about raw materials, distribution and costs, sales commissions, packaging, and many more, depending on the industry. They are easier to control and manipulate than fixed ones because they follow a simple rule: the more you produce, the higher the variable expenses. The more you sell from what you produced, the less impact from these costs. Companies also use their variable expenses to define pricing, plan their budgeting strategies, and track their profitability (together with fixed expenses), among other things. 

18. Actual vs. Forecast Income

Actual vs. forecast income as a financial graph template

Forecasting is the process of using historical and current data to generate accurate predictions about the future. In finances, forecasting has become an increasingly important practice that enables managers to generate strategies based on realistic scenarios. Our next example is a table displaying the actual vs. forecast income with insights into the actual value, the forecast value, and the absolute difference between the two. Here, we can observe a difference of $-33,237 in the net profit. This can shine a light on some issues that need to be addressed to prevent the business from having profitability problems in the future. However, it is important to note that the difference between the forecasted and the actual value is not necessarily a negative thing. It will depend on the way the business approaches forecasting.

19. Actual vs. Forecast Expenses

Actual vs. forecast expenses as a financial chart example

Following with another forecasting example, we have the actual vs. forecast expenses. This time, displayed in a financial bar chart instead of a table. As we mentioned in previous examples, keeping expenses at a minimum while maintaining profitability is one of the biggest challenges for organizations of all sizes. Here, we can see the actual costs compared to the forecasted value and an absolute difference between the two. Overall, we can say that this business was successful at keeping costs low as their absolute value is on the lower side. That said, there is still room for improvement. For instance, we can see that marketing costs are almost $50.000 higher than the forecast. This is something that is worth exploring in more detail to find the causes and determine if it is a critical issue or not.

20. Working Capital

Working capital depicting details of current assets and current liabilities as one of the financial graph templates for showing short-term financial health

Moving on with our list of financial chart examples, we have the working capital. This is a straightforward graph that gives you a glance overview of the financial health of your company. It doesn't include any ratios or proportions but solely numbers that represent the state of your current liabilities, current assets, and total working capital. If the working capital is high, you might want to consider investing the excess cash, as higher values don't necessarily mean your company is performing well.

21. Income Before Tax 

Income before tax financial graph example

Our next financial chart template shows a summary of an income statement. We have mentioned the value of an income statement and discussed many of the KPIs present in it throughout this post. However, there is one missing that we will focus on right now: the income before tax, also known as EBIT. As its name suggests, the income before tax is a KPI that tracks the amount of income generated by a company before subtracting all tax-related expenses. It is used by managers and investors as a way to analyze the performance of a company’s core operations without considering tax costs, as they can cloud the actual operating values.

22. Berry Ratio

A financial chart example tracking the berry ratio of a business

The Berry Ratio compares the gross profit of a company with its operating expenses to understand the amount of profit from a specific time period. In the chart above, we see that 1,0 is the reference coefficient to measure this metric. If your company’s Berry Ratio is below 1,0, it means that you are losing money. On the other hand, if it’s higher, it means that you are making a profit above all variable expenses.

This business graph is a fundamental part of a CFO dashboard , if you track it regularly, you can understand which exact period your profit dropped or increased and draw conclusions to improve your business finances.

23. Economic Value Added

Economic value added business chart example

This interactive gauge chart aims to track the Economic Added Value (EVA) of a company, the colors red, gray, and green make it easier to understand if the number is positive or negative visually. This metric is obtained by deducting the costs of capital from the operating profit and adjusting it for taxes on a cash basis. In order to calculate your company’s Economic Added Value, you can use a simple formula consisting of: net operating profit after taxes (NOPAT) - invested capital * weighted average cost of capital (WACC).

The EVA is a fundamental financial metric to understand if a company’s investment is returning any value. If a business has a negative EVA, it means that it’s not generating any profit from its investments. By measuring this metric on a regular basis, you’ll have a bigger picture of your company's wealth and make better managerial decisions in the long run.

24. Payroll Headcount Ratio

Financial chart example showing payroll headcount ratio

Next, in our financial data visualization examples, we have the Payroll Headcount Ratio. This metric consists of dividing all the HR full-time positions by the total number of employees based on various aspects such as their associated costs or revenues. You can include full-time and part-time employees as well as freelancers or contractors in the calculation. The overall aim of the Payroll Headcount Ratio is to understand how well your company is managing its workforce costs. 

By tracking HR metrics like the Payroll Headcount Ratio, you can make sure that your labor costs are well invested and bringing positive financial gain to your company, as well as help you understand if your overhead costs for payroll are too high, this way you can take action quickly and avoid any difficulties.

25. Procurement Cost Reduction

Financial graph displaying cost reduction

Cost reduction is an important KPI that you will find in any procurement dashboard . This metric's aim is to track the tangible savings you have made in terms of cost management over the years. The image above displays two charts to understand cost reduction, the first one is a 5-year trend so you can compare your performance with other years, and the second one gives a detailed view of the savings by supplier category; this way, you can learn exactly on what area you saved money.

By currently monitoring your cost reduction, you can streamline your supplier lifecycle management, increase efficiency by leveraging supply chain analytics or train your staff on how to save costs. All of this will certainly increase your numbers in the long term.

26. Cost Per Hire

The Cost per hire measures all the costs involved in the hiring process of one candidate

This straightforward metric aims to track the number of resources you invest in each new employee you need to hire. In the pie chart above, we can see the yearly expenses divided by seniority level: Junior, Mid-level, and Senior. The chart covers all expenses that come from the recruiting process, such as marketing, time cost that the recruiter spends reviewing CVs and conducting interviews, as well as training and cost materials associated with it.

Although it might not seem like it, the recruitment process usually costs businesses a lot of money. By keeping track of this metric, you can optimize investments and extract all the potential out of your talent acquisition budget. In the end, investing in new talents is what will bring more value back to your company.  

27. P/E Ratio

Financial management graph tracking the price earning ratio (P/E)

Moving on with our list of financial graphics, we have the price-earning ratio (P/E). This indicator, displayed in an intuitive area chart, is used to measure the value of a company compared to its competitors. It does this by relating a company’s share price to its earnings per share. It gives potential investors an idea of how much money they would pay for stock shares for each dollar of earnings. The P/E calculations should always consider competitors from the same industry, as the values will considerably vary depending on the nature of each industry.   

28. Quick Ratio/Acid Test

Business graph example tracking the quick ratio

Ensuring liquidity is one of the greatest financial aims of any organization. The quick ratio, or acid test, aims at helping companies understand their liquidity’s health in a short-term period. It measures the ability of a business to turn its near-cash assets (assets that can be turned quickly into cash) to pay down its current liabilities. The higher your quick ratio, the better. Your goal should be to keep it at a minimum of 1,0. This means your business has the capacity to pay all of its current liabilities quickly. 

An important note when it comes to monitoring this metric is to understand that, when comparing it to the current ratio, the acid test will always be smaller due to the fact that it only includes near-cash assets. 

29. Budget Variance

Example of a financial graph displaying the budget variance in a table

Next, we have the budget variance displayed in a table chart. This straightforward metric expresses the difference between budgeted and actual figures in different accounting categories. The values can be favorable or unfavorable and are clearly depicted with the colors red for negative and green for positive. This way, you get a glance notion of what is working and what is not. Negative budget variances can indicate that the company was not able to forecast costs and revenues accurately. However, some negative variances can also happen due to external factors that are outside the control of the organization. This can be changing business conditions, changes in the overall economic environment, or an increase in the costs of raw materials, just to name a few. 

30. MRR Growth 

MRR growth rate being shown as a financial chart example in customer service

To start explaining the MRR growth, we first need to understand what MRR even stands for. The monthly recurring revenue is the income that a business can expect to generate every single month. It is a fundamental metric that serves as a foundation for calculating other relevant indicators, such as the customer lifetime value or the average selling price. Tracking the MRR growth for longer periods of time can tell you how sustainable is your business model and how fast you are growing. 

This metric proves to be specifically useful for companies working with subscription-based models, as predicting recurring revenue is easier for them. Monitoring your MRR growth with a line chart is the most effective way to do it, as it can easily indicate how the values increased or decreased during the observed period. 

Which Chart Type Is Best For Visualizing Your Financial Data?

We couldn't finish this article before mentioning a very important aspect to consider when analyzing or presenting your financial data: charts and graph types. Choosing the right business graph to display your information is just like taking a picture of something and showing it to others. You want it to be understandable and focused on what you need in order to support a discussion. Here we show you some of the most common charts types to visualize your financial insights:

  • Line chart: This type of finance chart is ideal for displaying multiple series of closely related data over a period of time; like this, you can find trends, accelerations, decelerations, or volatility in your data. Its minimalistic design consisting of thin lines makes this type of chart very easy to understand. In order to maintain it like this, you should always keep your axes scales close to your highest data point. This way, you avoid wasting valuable space in the chart. It is also important to consider only displaying the relevant metrics for your analytical process since too many variables can overcrowd the chart and make it hard to decipher. You can use line charts to track financial KPIs such as the return on equity, working capital ratio, or earnings before Interest and Taxes.
  • Number chart: A number chart is one of the most basic types of business graphs, as it is essentially a ticker that gives you an immediate notion of how a specific KPI is performing. You just need to choose the period you want to track and if you want to compare it to a trend or a fixed goal, depending on the aim of your analysis. In finances, you can use it to measure metrics like the total cash balance, your current assets and liabilities, or some sales KPIs like the total revenue. Keeping track of these live numbers will help you catch any anomalies in time.
  • Tables: Tables are a classic way of displaying information, and they can prove to be really useful for working with your raw data. You can use a table to display many precise measures and dimensions, always having the grand total to compare or support it. They can also be useful if several people need to access the data for different reasons, as they can filter it and work only with what they need. It is important to consider that due to its complexity, you should always try to make your tables as visually appealing as possible regarding colors and shapes. You can accomplish this with the help of a dashboard tool . In finances, you can use tables to display your profit and loss statements (P&L) to drive advanced insights into your company's revenues.
  • Gauge chart : The gauge chart is a straightforward and simple type of visualization often used to display the performance of a single metric with a quantitative context. With the help of colors and needles, this type of chart aims to track the progress of a KPI in comparison to a set target or to other time periods. It is important to consider that because gauge charts are most effective for displaying one single metric, it is not the best chart to use if you want to drive actionable insights from your analysis. You can go back to our list of financial graph templates to see the economic value-added and the net profit margin illustrated with colorful gauge charts.
  • Progress chart: As its name suggests, a progress chart aims to track how much percentage of a specific goal you have accomplished and how much you have left to complete it fully. The data can be expressed in circles or bar charts, and you can also add reference numbers to indicate where you should be in a specific time period and compare if you are late or advanced to accomplish your final goal. If you want a more detailed view, you can also break down your progress in different areas and track each of them separately to understand if any step-backs are happening and where. In finances, you can use it to keep track of your budget spending or the development of a big project where your company placed a big investment.
  • Waterfall chart : This type of visualization helps understand the cumulative effect between positive or negative values to reach a final value. For instance, if a company wants to illustrate its yearly profit, the waterfall would display all sources of revenue and then add or deduct all costs to reach the total profit of the year. The additions and subtractions can be both time-based and category-based. In the use case we just mentioned, they are divided by category of revenue and costs. Our example on return on assets at the top uses a monthly division.
  • Area chart : This type of graphic typically combines a line and bar chart to show how one or more numeric values change based on a second variable. The area chart differs from these two others by adding shading between the lines and the baseline. It is typically used to show trends between associated attributes over time. In finances, area charts are usually used to represent stock changes over time, as seen in our P/E ratio example above. 
  • Bar chart : This type uses horizontal bars in a rectangular shape to display categorical data. They are mostly used to compare values based on a specific category, with the categories represented on the y-axis and the values on the x-axis (horizontal). They are used in finances when summarizing large data sets, as the horizontal orientation allows you to fit multiple values and categories without overcrowding the chart. For instance, when you want to visualize revenue by top 15 products.  
  • Column chart : In many places, column and bar charts are considered the same. However, they do serve different purposes depending on the goal and the analytical context. Column charts display categorical data in rectangular columns that have a vertical orientation. This means they can fit fewer values before they get overcrowded. However, this doesn’t mean they are not extremely valuable. For instance, you can use them in finances to analyze your net profit by each quarter of the year. The sizes of the vertical columns can help you spot any under or over-performing quarters at a glance. Plus, they can be mixed with other types of charts, such as line charts, to provide an even deeper look into the data, as we saw in our MRR Growth example. 

 Although these might be considered the best charts for financial analysis, you should always consider what your analytical aim is and what questions you are trying to answer when picking your visualizations. Here we give you a useful overview to help you choose the right type of business chart depending on your goals.

Overview to use the right financial data visualization types for comparisons, compositions, relationships and distributions

Financial Graphs And Charts Best Practices

As you have seen throughout this insightful post and our list of 30 interactive examples, charts have the capacity to turn the most complex data points into understandable values that can significantly enhance the decision-making process and drive business growth. That said, financial data is not easy to deal with. While it might sound easy just to build a chart to display your most important performance indicators, there are still a few best practices you need to follow in order to make your visualizations successful. Here we tell you a few of them. 

  • Think of your goals and audience 

The first step to generating successful finance-related visuals is to think about your audience and goals. This is a best practice that you should apply to any analytics-related task or process, especially when it comes to generating data visualizations for finances, as the language and data being used are complex and critical to the correct functioning of the organization.  

In that sense, there are two things you should consider. For starters, what are the company’s financial goals? Thinking about this question will help you plan your visuals to tell a compelling story that will allow management and any other stakeholder that needs to use those charts to answer questions and inform their most important strategic decisions. You can also think outside of the box and include some graphics that provide context or deeper insights. 

Paired with the general goals, you should also think about your audience. What matters to them, what is their level of knowledge, and what are they expecting to get from these visuals? By putting yourself on the audience shows, you’ll be able to generate visuals that are compelling and engaging. Plus, it will help users that are not very technical with finances to understand the message on each graph easily. We’ll discuss this last point as a separate best practice a bit later in the post. 

  • Avoid unnecessary elements and be smart

The first best practice for financial data presentation is to avoid cluttering your graphs with unnecessary elements. To avoid this, you should first define a clear goal for the visual you are building. This way, you will be able to clearly distinguish which elements are needed and which ones are not. If you are using more than one axes, make sure that each of them provides value to the point you are trying to show. Otherwise, it can lead to a misleading interpretation of the data. 

Another important note here is to be smart about the way you present your insights. For instance, if you use a bar chart to show revenue growth over the past 12 months, it is only natural to order the values by month to see the progression. On the other side, if you are showing revenue growth by the department, it could be a good idea to order them from largest to smallest growth. This allows the audience to understand at a glance the highest and lowest categories. 

  • Keep a consistent visual identity 

Charts and graphs are integral in communicating complex financial information in an intuitive way. That said, when building them, the colors you use can significantly affect how the data is perceived. A carefully selected color palette can help your audience understand the values better, as well as keep them focused during the analysis process. On the contrary, a poor color palette can make the visualization process less effective and harder to understand. 

A few good practices for this is to define specific colors for specific topics. For instance, you can use orange every time you will display revenue-related charts and play with the different shades of the color to show different values of revenue. That way, your audience will automatically understand you are talking about revenue when they see the color orange. Another good practice is to keep the colors consistent with the business's visual identity. This makes them more friendly-looking to the audience as well as more professional in general.

  • Use understandable language

It is very likely that your financial goals will also affect the rest of the departments in your organization. If you want to increase sales in your online channels, then you need to connect with the marketing department to think of initiatives that can help achieve this objective. That same scenario can happen with several other departments. Hence, the need to make financial data understandable for every user level. 

That said, when building your finance statement charts, it is of utmost importance to use friendly language. If you are including acronyms in your axes, make sure you explain what they refer to. The same rule applies to any other type of technical language you include in your representations. You should always keep your audience in mind when building your charts.  

  • Use interactive elements 

Financial data visualizations have been a part of businesses' regular operations for decades now. That said, the practice of generating visuals for the finance department has mutated with the years, shifting from static graphs and charts displayed in a PowerPoint presentation to modern online dashboards containing a mix of interactive graphics that allow users to navigate the data and extract deeper insights.  How, you might be wondering? The answer is interactive capabilities provided by modern data visualization software. 

Financial analytics tools such as datapine provide users with multiple interactivity options to give users the power to bring their data to life and uncover critical insights. Some of these interactivity features include:

  • Drill down : This filter enables you to go into lower levels of hierarchical data all in one chart. For instance, imagine you are looking at revenue per product category and want to look deeper into a specific category. All you need to do is click on that category, and the chart will adapt to show the best-selling products in that category. That way, you’ll be able to find the reasons for certain trends and patterns without going through infinite charts. 
  • Drill through: Similar to drill-downs, drill throughs also provide extra information from a particular chart, but instead of just going into lower levels of data, it shows the extra data in a popup. For instance, say you have a number chart displaying the total revenue of the year. A drill through would enable the user to click on that chart to see a pop-up displaying revenue by department. 
  • Time interval widget: This filter lets you visualize different time periods in specific KPIs. For example, you might be visualizing revenue for the past 5 years and realize that year 3 had a huge spike. You can click on that year for monthly or weekly revenue.

These are just a few of the many interactivity options you can include when generating your financial graphics. If you want to know more about this topic, check out our guide on the top 14 interactive dashboard features. 

6. Tell a cohesive data story 

Expanding on the point above, it is no secret that finance users are acquainted with numbers and formulas, probably more than any other department. That said, in order to achieve a collaborative environment with other relevant business players, the data needs to be displayed in a way that tells a cohesive understandable story. Data visualizations allow non-technical users to identify trends and patterns in the data. However, this is not possible without a correct organization of the different graphs and charts. Modern dashboard software assists you with this task by providing a centralized view of your most important financial indicators. 

The image below is a financial dashboard displaying relevant metrics related to profit and loss. Being able to quickly see how the numbers fluctuated over time and how each indicator affected the other allows users to get a complete picture and make informed decisions.  

Visual of a financial business dashboard example for top-management

7. Gather internal feedback and adapt 

As you’ve learned from this list of best practices, building successful financial data visualizations is a task that requires thoughtful consideration of the design but also of the audience and final use case. That means there’s probably always room for improvement, and you should see that as an opportunity. 

After you generate your graphs and charts and present them to the finance team, you should gather feedback from all users and find improvement opportunities to make the process as efficient and personalized as possible. This may sound like an exaggeration, but the way you choose to chart your financial KPIs is going to set the groundwork for future strategic decisions. Therefore, it should not be taken lightly.

Key Takeaways From Financial Charts & Graphs

We have expounded on what graphs to include in financial analysis and explained in detail each of them. We hope these graphs and charts templates have given you the inspiration you need to optimize your overall financial reporting and analysis . If you would like more data-driven, business-based pearls of wisdom, explore these sales report examples that you can use for daily, weekly, monthly, or annual reporting.

To get a more in-depth knowledge of the financial statement graphs essential for your business, you can test datapine for a 14-day free trial !

16 Best Types of Charts and Graphs for Data Visualization [+ Guide]

Jami Oetting

Published: June 08, 2023

There are more type of charts and graphs than ever before because there's more data. In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

Person on laptop researching the types of graphs for data visualization

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

  • Motivate your team to take action.
  • Impress stakeholders with goal progress.
  • Show your audience what you value as a business.

Data visualization builds trust and can organize diverse teams around new initiatives. Let's talk about the types of graphs and charts that you can use to grow your business.

graphical representation of a company

Free Excel Graph Templates

Tired of struggling with spreadsheets? These free Microsoft Excel Graph Generator Templates can help.

  • Simple, customizable graph designs.
  • Data visualization tips & instructions.
  • Templates for two, three, four, and five-variable graph templates.

You're all set!

Click this link to access this resource at any time.

Different Types of Graphs for Data Visualization

1. bar graph.

A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.

ypes of graphs — example of a bar graph.

Best Use Cases for These Types of Graphs

Bar graphs can help you compare data between different groups or to track changes over time. Bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

  • Product comparisons.
  • Product usage.
  • Category comparisons.
  • Marketing traffic by month or year.
  • Marketing conversions.

Design Best Practices for Bar Graphs

  • Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.
  • Use horizontal labels to improve readability.
  • Start the y-axis at 0 to appropriately reflect the values in your graph.

2. Line Graph

A line graph reveals trends or progress over time, and you can use it to show many different categories of data. You should use it when you chart a continuous data set.

Types of graphs — example of a line graph.

Line graphs help users track changes over short and long periods. Because of this, these types of graphs are good for seeing small changes.

Line graphs can help you compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.

A business might use this graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs

  • Use solid lines only.
  • Don't plot more than four lines to avoid visual distractions.
  • Use the right height so the lines take up roughly 2/3 of the y-axis' height.

3. Bullet Graph

A bullet graph reveals progress towards a goal, compares this to another measure, and provides context in the form of a rating or performance.

Types of graph — example of a bullet graph.

In the example above, the bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, you could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

  • Customer satisfaction.
  • Average order size.
  • New customers.

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. You can also use bullet graphs to visualize:

  • Customer satisfaction scores.
  • Customer shopping habits.
  • Social media usage by platform.

Design Best Practices for Bullet Graphs

  • Use contrasting colors to highlight how the data is progressing.
  • Use one color in different shades to gauge progress.

Different Types of Charts for Data Visualization

To better understand these chart types and how you can use them, here's an overview of each:

1. Column Chart

Use a column chart to show a comparison among different items or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Types of charts — example of a column chart.

Best Use Cases for This Type of Chart

You can use both column charts and bar graphs to display changes in data, but column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar graphs show data horizontally.

For example, warehouses often track the number of accidents on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

  • Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
  • Sales volume, like showing which services are the top sellers each month or the number of sales per week.
  • Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts

2. dual-axis chart.

A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous data set, and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.

 Types of charts — example of a dual-axis chart.

A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.

For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.

This makes it simple to see the connection between the number of customers and increased revenue.

You can use dual-axis charts to compare:

  • Price and volume of your products.
  • Revenue and units sold.
  • Sales and profit margin.
  • Individual sales performance.

Design Best Practices for Dual-Axis Charts

  • Use the y-axis on the left side for the primary variable because brains naturally look left first.
  • Use different graphing styles to illustrate the two data sets, as illustrated above.
  • Choose contrasting colors for the two data sets.

3. Area Chart

An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year. It helps you analyze both overall and individual trend information.

Types of charts — example of an area chart.

Best Use Cases for These Types of Charts

Area charts help show changes over time. They work best for big differences between data sets and help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These charts make the size of a group and how groups relate to each other more visually important than data changes over time.

Area graphs can help your business to:

  • Visualize which product categories or products within a category are most popular.
  • Show key performance indicator (KPI) goals vs. outcomes.
  • Spot and analyze industry trends.

Design Best Practices for Area Charts

  • Use transparent colors so information isn't obscured in the background.
  • Don't display more than four categories to avoid clutter.
  • Organize highly variable data at the top of the chart to make it easy to read.

4. Stacked Bar Chart

Use this chart to compare many different items and show the composition of each item you’re comparing.

Types of charts — example of a stacked bar chart.

These graphs are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view — when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These graphs can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say but not a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

  • Show the frequency of survey responses.
  • Identify outliers in historical data.
  • Compare a part of a strategy to its performance as a whole.

Design Best Practices for Stacked Bar Graphs

  • Best used to illustrate part-to-whole relationships.
  • Use contrasting colors for greater clarity.
  • Make the chart scale large enough to view group sizes in relation to one another.

5. Mekko Chart

Also known as a Marimekko chart, this type of graph can compare values, measure each one's composition, and show data distribution across each one.

It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values — instead of time progression, like column charts often do. In the graphic below, the x-axis compares the cities to one another.

Types of charts — example of a Mekko chart.

Image Source

You can use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart clarifies which firms manage the most assets in different areas.

It's also easy to see which asset managers are the largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts and graphs, so it's best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

  • Detailed profit and loss statements.
  • Revenue by brand and region.
  • Product profitability.
  • Share of voice by industry or niche.

Design Best Practices for Mekko Charts

  • Vary your bar heights if the portion size is an important point of comparison.
  • Don't include too many composite values within each bar. Consider reevaluating your presentation if you have a lot of data.
  • Order your bars from left to right in such a way that exposes a relevant trend or message.

6. Pie Chart

A pie chart shows a static number and how categories represent part of a whole — the composition of something. A pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

Types of charts — example of a pie chart.

The image above shows another example of customers by role in the company.

The bar graph example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

  • Customer personas in relation to all customers.
  • Revenue from your most popular products or product types in relation to all product sales.
  • Percent of total profit from different store locations.

Design Best Practices for Pie Charts

  • Don't illustrate too many categories to ensure differentiation between slices.
  • Ensure that the slice values add up to 100%.
  • Order slices according to their size.

7. Scatter Plot Chart

A scatter plot or scattergram chart will show the relationship between two different variables or reveal distribution trends.

Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or understanding your data's distribution.

Types of charts — example of a scatter plot chart.

Scatter plots are helpful in situations where you have too much data to see a pattern quickly. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

This type of graph makes it easy to compare two data sets. Use cases might include:

  • Employment and manufacturing output.
  • Retail sales and inflation.
  • Visitor numbers and outdoor temperature.
  • Sales growth and tax laws.

Try to choose two data sets that already have a positive or negative relationship. That said, this type of graph can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots

  • Include more variables, like different sizes, to incorporate more data.
  • Start the y-axis at 0 to represent data accurately.
  • If you use trend lines, only use a maximum of two to make your plot easy to understand.

8. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

 Types of charts — example of a bubble chart.

In the example above, the number of hours spent online isn't just compared to the user's age, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you're trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see your new product's cost, risk, and value. This can help you focus your energies on a low-risk new product with a high potential return.

You can also use bubble charts for:

  • Top sales by month and location.
  • Customer satisfaction surveys.
  • Store performance tracking.
  • Marketing campaign reviews.

Design Best Practices for Bubble Charts

  • Scale bubbles according to area, not diameter.
  • Make sure labels are clear and visible.
  • Use circular shapes only.

9. Waterfall Chart

Use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how different departments influence overall company revenue and lead to a specific profit number.

Types of charts — example of a waterfall chart.

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

  • Deal pipelines.
  • Conversion and retention analysis.
  • Bottlenecks in manufacturing and other multi-step processes.
  • Marketing campaign performance.
  • Website conversion tracking.

Design Best Practices for Funnel Charts

  • Scale the size of each section to accurately reflect the size of the data set.
  • Use contrasting colors or one color in graduated hues, from darkest to lightest, as the size of the funnel decreases.

11. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

 Types of charts — example of a heat map.

Best Use Cases for Heat Maps

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design .

Another common use for heat map graphs is location assessment. If you're trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can't communicate.

Heat maps can also help with spotting patterns, so they're good for analyzing trends that change quickly, like ad conversions. They can also help with:

  • Competitor research.
  • Customer sentiment.
  • Sales outreach.
  • Campaign impact.
  • Customer demographics.

Design Best Practices for Heat Map

  • Use a basic and clear map outline to avoid distracting from the data.
  • Use a single color in varying shades to show changes in data.
  • Avoid using multiple patterns.

12. Gantt Chart

The Gantt chart is a horizontal chart that dates back to 1917. This chart maps the different tasks completed over a period of time.

Gantt charting is one of the most essential tools for project managers. It brings all the completed and uncompleted tasks into one place and tracks the progress of each.

While the left side of the chart displays all the tasks, the right side shows the progress and schedule for each of these tasks.

This chart type allows you to:

  • Break projects into tasks.
  • Track the start and end of the tasks.
  • Set important events, meetings, and announcements.
  • Assign tasks to the team and individuals.

Gantt Chart - product creation strategy

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. do you want to compare values.

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

  • Scatter plot

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

  • Stacked bar

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want more information about how a data set performed during a specific time, there are specific chart types that do extremely well.

You should choose one of the following:

  • Dual-axis line

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

Featured Resource: The Marketer's Guide to Data Visualization

Types of chart — HubSpot tool for making charts.

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Tired of struggling with spreadsheets? These free Microsoft Excel Graph Generator Templates can help

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Visualizations That Really Work

  • Scott Berinato

graphical representation of a company

Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.

By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz.

This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

Know what message you’re trying to communicate before you get down in the weeds.

Idea in Brief

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine…

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it’s easy to create terrible charts. Visualization can be so much more: It’s an agile, powerful way to explore ideas and communicate information.

…But Strategy Is Key

Don’t jump straight to execution. Instead, first think about what you’re representing—ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

  • Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts Workbook: Tips Tools, and Exercises for Making Better Data Visualizations and Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations .

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Graphical Representation

Graphical representation definition.

Graphical representation refers to the use of charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions, and other qualitative structures. ‍

graphical representation of a company

What is Graphical Representation?

Graphical representation refers to the use of intuitive charts to clearly visualize and simplify data sets. Data is ingested into graphical representation of data software and then represented by a variety of symbols, such as lines on a line chart, bars on a bar chart, or slices on a pie chart, from which users can gain greater insight than by numerical analysis alone. 

Representational graphics can quickly illustrate general behavior and highlight phenomenons, anomalies, and relationships between data points that may otherwise be overlooked, and may contribute to predictions and better, data-driven decisions. The types of representational graphics used will depend on the type of data being explored.

Types of Graphical Representation

Data charts are available in a wide variety of maps, diagrams, and graphs that typically include textual titles and legends to denote the purpose, measurement units, and variables of the chart. Choosing the most appropriate chart depends on a variety of different factors -- the nature of the data, the purpose of the chart, and whether a graphical representation of qualitative data or a graphical representation of quantitative data is being depicted. There are dozens of different formats for graphical representation of data. Some of the most popular charts include:

  • Bar Graph -- contains a vertical axis and horizontal axis and displays data as rectangular bars with lengths proportional to the values that they represent; a useful visual aid for marketing purposes
  • Choropleth -- thematic map in which an aggregate summary of a geographic characteristic within an area is represented by patterns of shading proportionate to a statistical variable
  • Flow Chart -- diagram that depicts a workflow graphical representation with the use of arrows and geometric shapes; a useful visual aid for business and finance purposes
  • Heatmap -- a colored, two-dimensional matrix of cells in which each cell represents a grouping of data and each cell’s color indicates its relative value
  • Histogram – frequency distribution and graphical representation uses adjacent vertical bars erected over discrete intervals to represent the data frequency within a given interval; a useful visual aid for meteorology and environment purposes
  • Line Graph – displays continuous data; ideal for predicting future events over time;  a useful visual aid for marketing purposes
  • Pie Chart -- shows percentage values as a slice of pie; a useful visual aid for marketing purposes
  • Pointmap -- CAD & GIS contract mapping and drafting solution that visualizes the location of data on a map by plotting geographic latitude and longitude data
  • Scatter plot -- a diagram that shows the relationship between two sets of data, where each dot represents individual pieces of data and each axis represents a quantitative measure
  • Stacked Bar Graph -- a graph in which each bar is segmented into parts, with the entire bar representing the whole, and each segment representing different categories of that whole; a useful visual aid for political science and sociology purposes
  • Timeline Chart -- a long bar labelled with dates paralleling it that display a list of events in chronological order, a useful visual aid for history charting purposes
  • Tree Diagram -- a hierarchical genealogical tree that illustrates a family structure; a useful visual aid for history charting purposes
  • Venn Diagram -- consists of multiple overlapping usually circles, each representing a set; the default inner join graphical representation

Proprietary and open source software for graphical representation of data is available in a wide variety of programming languages. Software packages often provide spreadsheets equipped with built-in charting functions.

Advantages and Disadvantages of Graphical Representation of Data

Tabular and graphical representation of data are a vital component in analyzing and understanding large quantities of numerical data and the relationship between data points. Data visualization is one of the most fundamental approaches to data analysis, providing an intuitive and universal means to visualize, abstract, and share complex data patterns. The primary advantages of graphical representation of data are:

  • Facilitates and improves learning: graphics make data easy to understand and eliminate language and literacy barriers
  • Understanding content: visuals are more effective than text in human understanding
  • Flexibility of use: graphical representation can be leveraged in nearly every field involving data
  • Increases structured thinking: users can make quick, data-driven decisions at a glance with visual aids
  • Supports creative, personalized reports for more engaging and stimulating visual  presentations 
  • Improves communication: analyzing graphs that highlight relevant themes is significantly faster than reading through a descriptive report line by line
  • Shows the whole picture: an instantaneous, full view of all variables, time frames, data behavior and relationships

Disadvantages of graphical representation of data typically concern the cost of human effort and resources, the process of selecting the most appropriate graphical and tabular representation of data, greater design complexity of visualizing data, and the potential for human bias.

Why Graphical Representation of Data is Important

Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever increasing flow of data. Graphical representation enables the quick analysis of large amounts of data at one time and can aid in making predictions and informed decisions. Data visualizations also make collaboration significantly more efficient by using familiar visual metaphors to illustrate relationships and highlight meaning, eliminating complex, long-winded explanations of an otherwise chaotic-looking array of figures. 

Data only has value once its significance has been revealed and consumed, and its consumption is best facilitated with graphical representation tools that are designed with human cognition and perception in mind. Human visual processing is very efficient at detecting relationships and changes between sizes, shapes, colors, and quantities. Attempting to gain insight from numerical data alone, especially in big data instances in which there may be billions of rows of data, is exceedingly cumbersome and inefficient.

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17 Data Visualization Techniques All Professionals Should Know

Data Visualizations on a Page

  • 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.

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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 Chart Example

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

Bar Chart Example

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

Histogram Example

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 Chart Example

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

Heat Map Example

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

Box and Whisker Plot Example

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

Waterfall Chart Example

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

Area Chart Example

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

Scatter Plot Example

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 Example

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

Timeline Example

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

Highlight Table Example

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

Bullet Graph Example

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

Choropleth Map Example

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

Word Cloud Example

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 Diagram Example

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

Correlation Matrix Example

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

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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.

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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.

Principle of Graphical Representation of Data

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.

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The Concise Encyclopedia of Statistics pp 236–237 Cite as

Graphical Representation

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Graphical representations encompass a wide variety of techniques that are used to clarify, interpret and analyze data by plotting points and drawing line segments, surfaces and other geometric forms or symbols.

The purpose of a graph is a rapid visualization of a data set. For instance, it should clearly illustrate the general behavior of the phenomenon investigated and highlight any important factors. It can be used, for example, as a means to translate or to complete a  frequency table .

Therefore, graphical representation is a form of data representation.

The concept of plotting a point in coordinate space dates back to at least the ancient Greeks, but we had to wait until the work of Descartes, René for mathematicians to investigate this concept.

According to Royston, E. (1970), a German mathematician named Crome, A.W. was among the first to use graphical representation in statistics . He initially used it as a teaching tool.

In his works Geographisch-statistische Darstellung...

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Crome, A.F.W.: Ueber die Grösse und Bevölkerung der sämtlichen Europäischen Staaten. Weygand, Leipzig (1785)

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Crome, A.F.W.: Geographisch-statistische Darstellung der Staatskräfte. Weygand, Leipzig (1820)

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Schmid, C.F.: Handbook of Graphic Presentation. Ronald Press, New York (1954)

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Home » Graphical Methods – Types, Examples and Guide

Graphical Methods – Types, Examples and Guide

Table of Contents

Graphical Methods

Graphical Methods

Definition:

Graphical methods refer to techniques used to visually represent data, relationships, or processes using charts, graphs, diagrams, or other graphical formats. These methods are widely used in various fields such as science, engineering, business, and social sciences, among others, to analyze, interpret and communicate complex information in a concise and understandable way.

Types of Graphical Methods

Here are some of the most common types of graphical methods for data analysis and visual presentation:

Line Graphs

These are commonly used to show trends over time, such as the stock prices of a particular company or the temperature over a certain period. They consist of a series of data points connected by a line that shows the trend of the data over time. Line graphs are useful for identifying patterns in data, such as seasonal changes or long-term trends.

These are commonly used to compare values of different categories, such as sales figures for different products or the number of students in different grade levels. Bar charts use bars that are either horizontal or vertical and represent the data values. They are useful for comparing data visually and identifying differences between categories.

These are used to show how a whole is divided into parts, such as the percentage of students in a school who are enrolled in different programs. Pie charts use a circle that is divided into sectors, with each sector representing a portion of the whole. They are useful for showing proportions and identifying which parts of a whole are larger or smaller.

Scatter Plots

These are used to visualize the relationship between two variables, such as the correlation between a person’s height and weight. Scatter plots consist of a series of data points that are plotted on a graph and connected by a line or curve. They are useful for identifying trends and relationships between variables.

These are used to show the distribution of data across a two-dimensional plane, such as a map of a city showing the density of population in different areas. Heat maps use color-coded cells to represent different levels of data, with darker colors indicating higher values. They are useful for identifying areas of high or low density and for highlighting patterns in data.

These are used to show the distribution of data in a single variable, such as the distribution of ages of a group of people. Histograms use bars that represent the frequency of each data value, with taller bars indicating a higher frequency. They are useful for identifying the shape of a distribution and for identifying outliers or unusual data values.

Network Diagrams

These are used to show the relationships between different entities or nodes, such as the relationships between people in a social network. Network diagrams consist of nodes that are connected by lines that represent the relationship. They are useful for identifying patterns in complex data and for understanding the structure of a network.

Box plots, also known as box-and-whisker plots, are a type of graphical method used to show the distribution of data in a single variable. They consist of a box with whiskers extending from the top and bottom of the box. The box represents the middle 50% of the data, with the median value indicated by a line inside the box. The whiskers represent the range of the data, with any data points outside the whiskers indicated as outliers. Box plots are useful for identifying the spread and shape of a distribution and for identifying outliers or unusual data values.

Applications of Graphical Methods

Graphical methods have a wide range of applications in various fields, including:

  • Business : Graphical methods are commonly used in business to analyze sales data, financial data, and other types of data. They are useful for identifying trends, patterns, and outliers, as well as for presenting data in a clear and concise manner to stakeholders.
  • Science and engineering: Graphical methods are used extensively in scientific and engineering fields to analyze data and to present research findings. They are useful for visualizing complex data sets and for identifying relationships between variables.
  • Social sciences: Graphical methods are used in social sciences to analyze and present data related to human behavior, such as demographics, survey results, and statistical analyses. They are useful for identifying trends and patterns in large data sets and for communicating findings to a broader audience.
  • Education : Graphical methods are used in education to present information to students and to help them understand complex concepts. They are useful for visualizing data and for presenting information in a way that is easy to understand.
  • Healthcare : Graphical methods are used in healthcare to analyze patient data, to track disease outbreaks, and to present medical information to patients. They are useful for identifying patterns and trends in patient data and for communicating medical information in a clear and concise manner.
  • Sports : Graphical methods are used in sports to analyze and present data related to player performance, team statistics, and game outcomes. They are useful for identifying trends and patterns in player and team data and for communicating this information to coaches, players, and fans.

Examples of Graphical Methods

Here are some examples of real-time applications of graphical methods:

  • Stock Market: Line graphs, candlestick charts, and bar charts are widely used in real-time trading systems to display stock prices and trends over time. Traders use these charts to analyze historical data and make informed decisions about buying and selling stocks in real-time.
  • Weather Forecasting : Heat maps and radar maps are commonly used in weather forecasting to display current weather conditions and to predict future weather patterns. These maps are useful for tracking the movement of storms, identifying areas of high and low pressure, and predicting the likelihood of severe weather events.
  • Social Media Analytics: Scatter plots and network diagrams are commonly used in social media analytics to track the spread of information across social networks. Analysts use these graphs to identify patterns in user behavior, to track the popularity of specific topics or hashtags, and to monitor the influence of key opinion leaders.
  • Traffic Analysis: Heat maps and network diagrams are used in traffic analysis to visualize traffic flow patterns and to identify areas of congestion or accidents. These graphs are useful for predicting traffic patterns, optimizing traffic flow, and improving transportation infrastructure.
  • Medical Diagnostics: Box plots and histograms are commonly used in medical diagnostics to display the distribution of patient data, such as blood pressure, heart rate, or blood sugar levels. These graphs are useful for identifying patterns in patient data, diagnosing medical conditions, and monitoring the effectiveness of treatments in real-time.
  • Cybersecurity: Heat maps and network diagrams are used in cybersecurity to visualize network traffic patterns and to identify potential security threats. These graphs are useful for identifying anomalies in network traffic, detecting and mitigating cyber attacks, and improving network security protocols.

How to use Graphical Methods

Here are some general steps to follow when using graphical methods to analyze and present data:

  • Identify the research question: Before creating any graphs, it’s important to identify the research question or hypothesis you want to explore. This will help you select the appropriate type of graph and ensure that the data you collect is relevant to your research question.
  • Collect and organize the data: Collect the data you need to answer your research question and organize it in a way that makes it easy to work with. This may involve sorting, filtering, or cleaning the data to ensure that it is accurate and relevant.
  • Select the appropriate graph : There are many different types of graphs available, each with its own strengths and weaknesses. Select the appropriate graph based on the type of data you have and the research question you are exploring. For example, a scatterplot may be appropriate for exploring the relationship between two continuous variables, while a bar chart may be appropriate for comparing categorical data.
  • Create the graph: Once you have selected the appropriate graph, create it using software or a tool that allows you to customize the graph based on your needs. Be sure to include appropriate labels and titles, and ensure that the graph is clearly legible.
  • Analyze the graph: Once you have created the graph, analyze it to identify patterns, trends, and relationships in the data. Look for outliers or other anomalies that may require further investigation.
  • Draw conclusions: Based on your analysis of the graph, draw conclusions about the research question you are exploring. Use the graph to support your conclusions and to communicate your findings to others.
  • Iterate and refine: Finally, refine your graph or create additional graphs as needed to further explore your research question. Iteratively refining and revising your graphs can help to ensure that you are accurately representing the data and that you are drawing the appropriate conclusions.

When to use Graphical Methods

Graphical methods can be used in a variety of situations to help analyze, interpret, and communicate data. Here are some general guidelines on when to use graphical methods:

  • To identify patterns and trends: Graphical methods are useful for identifying patterns and trends in data, which may be difficult to see in raw data tables or spreadsheets. Graphs can reveal trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions.
  • To compare data: Graphs can be used to compare data from different sources or over different time periods. Graphical comparisons can make it easier to identify differences or similarities in the data, which can be useful for making decisions and taking action.
  • To summarize data : Graphs can be used to summarize large amounts of data in a single visual display. This can be particularly useful when presenting data to a broad audience, as it can help to simplify complex data sets and make them more accessible.
  • To communicate data: Graphs can be used to communicate data and findings to a variety of audiences, including stakeholders, colleagues, and the general public. Graphs can be particularly useful in situations where data needs to be presented quickly and in a way that is easy to understand.
  • To identify outliers: Graphical methods are useful for identifying outliers or anomalies in the data. Outliers can be indicative of errors or unusual events, and may warrant further investigation.

Purpose of Graphical Methods

The purpose of graphical methods is to help people analyze, interpret, and communicate data in a way that is both accurate and understandable. Graphical methods provide visual representations of data that can be easier to interpret than tables of numbers or raw data sets. Graphical methods help to reveal patterns and trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions. They can also help to identify outliers or unusual data points that may warrant further investigation.

In addition to helping people analyze and interpret data, graphical methods also serve an important communication function. Graphs can be used to present data to a wide range of audiences, including stakeholders, colleagues, and the general public. Graphs can help to simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.

Overall, the purpose of graphical methods is to provide a powerful tool for analyzing, interpreting, and communicating data. Graphical methods help people to better understand the data they are working with, to identify patterns and trends, and to make informed decisions based on the data.

Characteristics of Graphical Methods

Here are some characteristics of graphical methods:

  • Visual Representation: Graphical methods provide a visual representation of data, which can be easier to interpret than tables of numbers or raw data sets. Graphs can help to reveal patterns and trends that may not be immediately apparent in the data.
  • Simplicity : Graphical methods simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.
  • Comparability : Graphical methods can be used to compare data from different sources or over different time periods. This can help to identify differences or similarities in the data, which can be useful for making decisions and taking action.
  • Flexibility : Graphical methods can be adapted to different types of data, including continuous, categorical, and ordinal data. Different types of graphs can be used to display different types of data, depending on the characteristics of the data and the research question.
  • Accuracy : Graphical methods should accurately represent the data being analyzed. Graphs should be properly scaled and labeled to avoid distorting the data or misleading viewers.
  • Clarity : Graphical methods should be clear and easy to read. Graphs should be designed with the viewer in mind, using appropriate colors, labels, and titles to ensure that the message of the graph is conveyed effectively.

Advantages of Graphical Methods

Graphical methods offer several advantages for analyzing and presenting data, including:

  • Clear visualization: Graphical methods provide a clear and intuitive visual representation of data that can help people understand complex relationships, trends, and patterns in the data. This can be particularly useful when dealing with large and complex data sets.
  • Efficient communication: Graphical methods can help to communicate complex data sets in an efficient and accessible way. Visual representations can be easier to understand than numerical data alone, and can help to convey key messages quickly.
  • Effective comparison: Graphical methods allow for easy comparison between different data sets, making it easier to identify trends, patterns, and differences. This can help in making decisions, identifying areas for improvement, or developing new insights.
  • Improved decision-making: Graphical methods can help to inform decision-making by presenting data in a clear and easy-to-understand format. They can also help to identify key areas of focus, enabling individuals or teams to make more informed decisions.
  • Increased engagement: Graphical methods can help to engage audiences by presenting data in an engaging and interactive way. This can be particularly useful in presentations or reports, where visual representations can help to maintain audience attention and interest.
  • Better understanding: Graphical methods can help individuals to better understand the data they are working with, by providing a clear and intuitive visual representation of the data. This can lead to improved insights and decision-making, as well as better understanding of the implications of the data.

Limitations of Graphical Methods

Here are a few limitations to consider:

  • Misleading representation: Graphical methods can potentially misrepresent data if they are not designed properly. For example, inappropriate scaling or labeling of the axes or the use of certain types of graphs can create a distorted view of the data.
  • Limited scope: Graphical methods can only display a limited amount of data, which can make it difficult to capture the full complexity of a data set. Additionally, some types of data may be difficult to represent visually.
  • Time-consuming : Creating graphs can be a time-consuming process, particularly if multiple graphs need to be created and analyzed. This can be a limitation in situations where time is limited or resources are scarce.
  • Technical skills: Some graphical methods require technical skills to create and interpret. For example, certain types of graphs may require knowledge of specialized software or programming languages.
  • Interpretation : Interpreting graphs can be subjective, and the same graph can be interpreted in different ways by different people. This can lead to confusion or disagreements when using graphs to communicate data.
  • Accessibility : Some graphical methods may not be accessible to all audiences, particularly those with visual impairments. Additionally, some types of graphs may not be accessible to those with limited literacy or numeracy skills.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business process modeling gives organizations a simple way to understand and optimize workflows by creating data-driven visual representations of key business processes.

Most enterprises have a pretty good idea of the various business processes powering their daily operations. However, when they need to ensure that those processes consistently drive optimal outcomes, “a pretty good idea” isn’t enough.

If an organization wants research and development (R&D) investments to produce sufficient returns, IT issues resolved with minimal downtime or a highly accurate lead qualification workflow, it needs to understand these processes on an objective and comprehensive level. Even the business users directly involved in these processes may lack total transparency into exactly what happens at every step of the way.

Business analysts can gain end-to-end views of the business process lifecycle through business process modeling , a business process management (BPM) technique that creates data-driven visualizations of workflows. These process models help organizations document workflows, surface key metrics, pinpoint potential problems and intelligently automate processes.

What is business process modeling?

A business process model is a graphical representation of a business process or workflow and its related sub-processes. Process modeling generates comprehensive, quantitative activity diagrams and flowcharts containing critical insights into the functioning of a given process, including the following:

  • Events and activities that occur within a workflow
  • Who owns or initiates those events and activities
  • Decision points and the different paths workflows can take based on their outcomes
  • Devices involved in the process
  • Timelines of the overall process and each step in the process
  • Success and failure rates of the process

Key aspects of business process modeling

  • Process models are not made manually. Rather, they are produced by data-mining algorithms that use the data contained within event logs to construct models of the workflows as they exist.
  • Because process models are based on quantitative data, they offer genuinely objective views of workflows as they exist in practice, including key data, metrics or events that may have otherwise gone unnoticed. For example, by creating a model of its new account creation process, a software company might discover that a significant number of customers are abandoning the sign-up process because it takes too long. A model could even help the company pinpoint the exact stage at which these drop-offs occur.
  • Arrows represent sequence flows
  • Diamonds represent decision points or gateways
  • Ovals represent beginnings and endpoints of processes
  • Rectangles represent specific activities within a workflow
  • Swimlanes are used to identify who owns which components of a process
  • Business process models shouldn’t be confused with process maps , another common type of business process diagram. Process maps are based on employee reports, are created manually and provide higher-level views of workflows. Process models are data-driven deep dives that present more objective views of workflows.

Learn more by reading “Process Mining vs. Process Modeling vs. Process Mapping: What’s the Difference?”

How business process models are made

To fully understand business process modeling techniques, one must first understand the relevant business process modeling tools — event logs and process mining .

Most enterprise IT systems maintain event logs . These event logs are digital records that automatically track state changes and activities (i.e., “events”) within the system. Anything that happens within a system can be an event. The following are some common event examples:

  • A user logs in
  • A user updates a record
  • A user submits a form
  • Information is transferred between systems

Event logs track both the occurrence of events and information surrounding these events, like the device performing an activity and how long the activity takes. Event logs act as the inputs during the production of process models.

Process mining is the application of a data-mining algorithm to all of this event log data. The algorithm identifies trends in the data and uses the results of its analysis to generate a visual representation of the process flow within the system. This visual representation is the process model . Depending on the process targeted for modeling, process-mining algorithms can be applied to a single system, multiple systems or entire technological ecosystems and departments.

Business process modeling use cases

Process models offer unprecedented levels of transparency into company workflows, making them a key business process management tool. While process models can be leveraged in any scenario that requires analyzing business processes, these are some of the most common use cases:

Gaining 360-degree insight into processes

A single process model can contain a wealth of workflow data, allowing team members to analyze a workflow from multiple perspectives. Business analysts often use business process modeling to zero in on the following workflow components in particular:

  • Control flow: “Control flow” refers to the order in which steps and commands are executed within a process. A process model depicts a flowchart of a given process so that a team can clearly see what steps are taken and when. This perspective also helps the team identify any dependencies between steps.
  • Organization: A process model can capture who is involved in a process — including people, teams, systems and devices — and how they interact with each other. This perspective illuminates the connections between people and systems that form the organizational social network. In this way, a process model offers insight into how various components of a business function together.
  • Time: A process model can record how long a process takes, overall, and how long each step takes, allowing the team to identify delays, slowdowns and bottlenecks within the workflow.
  • Case: A process model can offer a general view of how a given workflow typically plays out, or it can reflect a particular case – or instance – of a workflow. Teams often use this case perspective to analyze anomalous process outcomes. For example, if a specific instance of a workflow results in lower-than-average outcome quality, teams can isolate exactly what went wrong.

Optimizing and standardizing processes

Process models accurately reflect existing workflow inefficiencies, making it easier to identify opportunities for process optimization. Once workflows have been optimized, businesses can use process modeling to standardize workflows across the entire enterprise. The model acts as a template for how processes should play out, ensuring that every team and employee approaches the same process in the same way. This leads to more predictable workflows and outcomes overall.

Assessing new processes

Process models can take the guesswork out of implementing and evaluating new business processes. By creating a model of a new process, business users can get a real-time look at how that workflow is performing, allowing them to make adjustments as necessary to achieve process optimization.

Analyzing resource usage

Process models can help companies track whether money and resource investments produce suitable returns. For example, by creating a model of the standard sales process, an organization can see how sales representatives are utilizing the tools and systems at their disposal. It may turn out that a certain tool is used much less frequently than anticipated, in which case, the organization can choose to disinvest from the tool and spend that money on a solution the sales team actually uses.

Communicating processes

Process models transform complex processes into concrete images, making it easier to disseminate and discuss processes throughout the organization. For example, if one department has a particularly efficient process for troubleshooting technical problems, the business can create a model of this process to guide implementation on an organization-wide scale. 

The benefits of business process modeling

Business process modeling arms an enterprise with objective business intelligence that supports more informed decisions for resource allocation, process improvement and overall business strategy. With a clear view of processes, enterprise teams can ensure that workflows always drive the desired results. As a result, operating costs are lower, revenue is higher and business outcomes are stronger.

Specifically, business process modeling allows companies to do the following:

  • Access and utilize quantitative process data: Without a process model, teams are limited to discussing and analyzing workflows in qualitative and subjective terms. As a result, teams may not accurately understand their workflows; they may make business decisions based on misunderstandings, assumptions and/or incomplete knowledge. With process modeling, teams have access to quantitative workflow data, including success rates and error rates, allowing for a more rigorous analysis of business processes.
  • Streamline and accelerate process automation: Before a process can be automated, an organization needs a clear understanding of how that process plays out in reality, including the business logic underpinning each decision point. A process model illuminates both the way a workflow unfolds and the relationships between events, actors, tools and systems within and between processes. This viewpoint helps a team document the process itself and the business rules that guide its execution. This information makes it easier to effectively automate workflows the first time.
  • Keep operation costs down: Process models provide organizations with an easy way to identify opportunities to optimize existing processes. This makes it easier for the company to ensure that processes consistently produce the desired outcomes. As a result, business processes require less investment to maintain and generate positive outcomes at a lower cost.

Business process modeling and IBM

Process modeling forms a cornerstone of any automation effort or business process management initiative. Without comprehensive views of existing processes and their undergirding business logic, enterprises cannot effectively optimize and automate workflows at scale.

Take the next step:

IBM Blueworks Live is a cloud-based business process modeling software designed to help organizations discover business processes and document them in a collaborative fashion across multiple stakeholder groups. Teams can work together through an intuitive and accessible web interface to document and analyze processes. No download required.

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Market Realist

Graphical Representation of General Electric’s Business Model

General Electric’s industrials and finance services are its two broader divisions, contributing 91% and 9%, respectively, to its consolidated 2015 earnings.

Jessica Stephans - Author

Nov. 20 2020, Updated 2:59 p.m. ET

GE’s business at a glance

General Electric Company’s (GE) industrials and finance services represent its two broader divisions, contributing 91% and 9%, respectively, to the company’s consolidated earnings in fiscal 2015. Operating margins for the industrial segment was 17% while the net interest margin of the finance division is ~5%. There are seven sub-segments under industrial while the financial (XLF) operating segment is known as GE Capital. The graph below clarifies GE’s business model.

*Note: GE has sold its appliance division to Haier.

GE’s industrial backlog and geographic reach

GE’s order backlog can be classified in two categories: servicing and sales of equipment. For the industrial (XLI) segment, the order backlog from servicing is 71.7%, or $226 billion, while the order backlog from sales of equipment was at 28.3%, or $89.3 billion.

According to the 2010 annual report, GE’s total revenues from the US was at 46%, while in 2014 the company’s revenue from the US stood at 48%, compared to Europe at 17%, Asia at 16%, 9% from Americas, and the remaining 10% from the Middle East and Africa.

GE’s core strength and competitiveness

General Electric (GE) stands out among other large conglomerates on the back of both innovative technology and industrial depth. GE is unique in terms of the following:

  • wins with technology
  • wins in growth markets
  • wins with services

The above value across the globe will be targeted in the following ways:

  • keeping a lean management with most decisions distributed
  • speedily competing and achieving by undertaking smaller things and adopting fast works like Lean and Six Sigma
  • tapping commercial intensity by way of seamless market alignment globally, value for customers, and providing more horizontal solutions

In all cases, digital capabilities are the need of the hour, and a new talent base with a new skill set could make GE as the smartest and most efficient company in the world.

Operating EPS target for 2016

GE is part of the Industrial Select Sector SPDR ETF ( XLI ) and accounts for 11.78% of XLI’s total holdings. GE is also one of the top ten holdings of the Vanguard High Dividend Yield ETF ( VYM ), accounting for 3.6% of VYM’s total holdings. Microsoft (MSFT) and Exxon Mobil (XOM) are among the top holdings in the fund.

In leveraging its core strengths and competitiveness, GE is aiming for a 75% operating EPS target from industrials for 2016. Now let’s take a tour of GE’s eight segments for a better understanding of GE’s business model.

Latest Industrial Select Sector SPDR® ETF News and Updates

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  • Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

  • Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
  • Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
  • Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
  • Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
  • Frequency Table – The table shows the number of pieces of data that falls within the given interval.
  • Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
  • Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
  • Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

Graphical Representation

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

  • Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
  • Measurement Unit: Mention the measurement unit in the graph.
  • Proper Scale: To represent the data in an accurate manner, choose a proper scale.
  • Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
  • Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
  • Keep it Simple: Construct a graph in an easy way that everyone can understand.
  • Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

  • Time Series Graphs – Example: Line Graph
  • Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Principles of graphical representation

Generally, the frequency distribution is represented in four methods, namely

  • Smoothed frequency graph
  • Pie diagram
  • Cumulative or ogive frequency graph
  • Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

  • The graph is easily understood by everyone without any prior knowledge.
  • It saves time
  • It allows us to relate and compare the data for different time periods
  • It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

  • Obtain the frequency distribution and find the midpoints of each class interval.
  • Represent the midpoints along x-axis and frequencies along the y-axis.
  • Plot the points corresponding to the frequency at each midpoint.
  • Join these points, using lines in order.
  • To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

graphical representation of a company

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

  • Line Graphs
  • Frequency Table
  • Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

  • It makes data more easily understandable.
  • It saves time.
  • It makes the comparison of data more efficient.

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graphical representation of a company

Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

Thanks very much for the information

graphical representation of a company

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Graphical Representation of Break-even Analysis

graphical representation of a company

Written by True Tamplin, BSc, CEPF®

Reviewed by subject matter experts.

Updated on March 26, 2023

Fact Checked

Why Trust Finance Strategists?

Table of Contents

Break-even chart.

Cost-volume-profit (CVP) relationships, or break-even relationships, can be visualized using graphs. Doing so comes with the advantage of showing CVP relationships over a range of sales.

Graphical analysis also enables managers to identify areas of profit or loss that would occur for a broad range of sales activities.

To give an example, consider how the data in the table below have been used to create the break-even chart.

Table Data

In plotting the graph, it is assumed that the selling price remains at $25, the variable cost remains at $15 per unit, and the fixed cost remains at $30,000 over the range of units sold.

The units sold are plotted on the horizontal axis, while total revenue is shown on the vertical axis.

The total revenue line is plotted, running from $0 at zero sales volume to $150,000 at a sales volume of 6,000 units at $25 per unit.

The total cost line is the sum total of fixed cost ($3,000) and variable cost of $15 per unit, plotted for various quantities of units to be sold.

The intersection of the two lines indicates the break-even point. Below and to the left of the break-even point, the difference between the total cost line and the total revenue line reflects the net loss for the period.

Conversely, the distance between these two lines to the right of the break-even point represents the net profit for the period.

The break-even graph clearly shows the relationship between profit and volume by indicating the net profit or loss associated with any given volume of units sold.

However, the graph can be interpreted only within the relevant range of operations (i.e., the level of activity over which fixed costs are assumed to remain fixed).

Profit-volume Graph (P/V Graph)

A simpler version of the break-even chart is known as the profit-volume graph (P/V graph). This graph shows a direct relationship between sales and profits, and it is easy to understand.

Break-even charts and P/V graphs are often used together to benefit from the advantages of both visualizations.

The vertical axis shows total profits or losses, while the horizontal axis represents units of product and sales revenue.

An advantage of the P/V graph is that profit and losses at any point can be read directly from the vertical axis.

The data used to prepare the break-even chart, as shown above, have also been used to prepare the P/V graph shown below.

Profit-volume Graph

The intersection of the profit line with the horizontal line gives the break-even point. Points above the line measure profits while points below the line measure losses.

The P/V graph is a simple and convenient way to show the extent to which profits are affected by changes in the factors that affect profit.

For example, if unit selling prices, unit variable costs, and total fixed costs remain constant, the P/V graph can show how many units must be sold to achieve a target profit.

Additionally, if the variable cost per unit can be reduced, the P/V graph shows the additional profits that can be expected at any given sales volume.

An advantage of the P/ V graph is that profits and losses at any point in time can be read directly from the vertical scale.

However, a major disadvantage is that the graph does not clearly reveal how costs vary with changes in activity.

For these reasons, and as mentioned earlier, both the P/V graph and break-even chart are used alongside one another by financial managers.

Graphical Representation of Break-even Analysis FAQs

What is cost-volume-profit (cvp).

CVP is a budgeting process that can be used to establish the break-even point and the expected operating income of the business.

What is a profit-volume graph?

The simplest form of the break-even chart, wherein total profits are plotted on the vertical axis while units sold are plotted on the horizontal axis.

What are the benefits of using both graphs?

Graphical representation of break-even analysis is useful as it can show the relationship between profits and volume by indicating the net profit or loss associated with any given volume of units sold.

What limitations exist when using either graph?

The break-even chart is restricted to its relevant range of operations (i.E., Level of activity over which the variable costs are assumed to remain constant) while the p/v graph does not clearly reveal how costs vary with changes inactivity.

What does the break-even analysis show?

Break-even analysis is a tool that can be used to demonstrate and calculate how much revenue is needed to make a certain amount of profit, assuming expenses remain constant.

About the Author

True Tamplin, BSc, CEPF®

True Tamplin is a published author, public speaker, CEO of UpDigital, and founder of Finance Strategists.

True is a Certified Educator in Personal Finance (CEPF®), author of The Handy Financial Ratios Guide , a member of the Society for Advancing Business Editing and Writing, contributes to his financial education site, Finance Strategists, and has spoken to various financial communities such as the CFA Institute, as well as university students like his Alma mater, Biola University , where he received a bachelor of science in business and data analytics.

To learn more about True, visit his personal website or view his author profiles on Amazon , Nasdaq and Forbes .

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Related Articles

  • CBSE Class 9 Maths Revision Notes

Chapter 1: Number System

  • Number System in Maths
  • Natural Numbers | Definition, Examples, Properties
  • Whole Numbers | Definition, Properties and Examples
  • Rational Number: Definition, Examples, Irrationals, Exercises
  • Irrational Numbers- Definition, Identification, Examples, Symbol, Properties
  • Real Numbers
  • Decimal Expansion of Real Numbers
  • Decimal Expansions of Rational Numbers
  • Representation of Rational Numbers on the Number Line | Class 8 Maths
  • Represent √3 on the number line
  • Operations on Real Numbers
  • Rationalization of Denominators
  • Laws of Exponents for Real Numbers

Chapter 2: Polynomials

  • Polynomials in One Variable - Polynomials | Class 9 Maths
  • Polynomial Formula
  • Types of Polynomials
  • Zeros of Polynomial
  • Factorization of Polynomial
  • Remainder Theorem
  • Factor Theorem
  • Algebraic Identities

Chapter 3: Coordinate Geometry

  • Coordinate Geometry
  • Cartesian Coordinate System in Maths
  • Cartesian Plane

Chapter 4: Linear equations in two variables

  • Linear Equations in One Variable
  • Linear Equation in Two Variables
  • Graph of Linear Equations in Two Variables
  • Graphical Methods of Solving Pair of Linear Equations in Two Variables
  • Equations of Lines Parallel to the x-axis and y-axis

Chapter 5: Introduction to Euclid's Geometry

  • Euclidean Geometry
  • Equivalent Version of Euclid’s Fifth Postulate

Chapter 6: Lines and Angles

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  • Pairs of Angles - Lines & Angles
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Chapter 7: Triangles

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  • Congruence of Triangles |SSS, SAS, ASA, and RHS Rules
  • Theorem - Angle opposite to equal sides of an isosceles triangle are equal | Class 9 Maths
  • Triangle Inequality

Chapter 8: Quadrilateral

  • Angle Sum Property of a Quadrilateral
  • Quadrilateral - Definition, Properties, Types, Formulas, Examples
  • Introduction to Parallelogram: Properties, Types, and Theorem
  • Rhombus: Definition, Properties, Formula, Examples
  • Kite - Quadrilaterals
  • Properties of Parallelograms
  • Mid Point Theorem

Chapter 9: Areas of Parallelograms and Triangles

  • Area of Triangle | Formula and Examples
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Chapter 10: Circles

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Chapter 11: Construction

  • Basic Constructions - Angle Bisector, Perpendicular Bisector, Angle of 60°
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Graphical Representation of Data

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  • CBSE Class 9 Maths Formulas
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In today’s world of the internet and connectivity, there is a lot of data available and some or the other method is needed for looking at large data, the patterns, and trends in it. There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting the numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics . The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, astrostatistics, and so on. There are two ways of representing data, 

  • Pictorial Representation through graphs.

They say, “A picture is worth the thousand words”.  It’s always better to represent data in graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in form of visuals as Human beings process data better in visual form than any other form. Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time. Let’s look at some of them in detail. 

Types of Graphical Representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux out of the data pertaining to different items. Let’s look at all the different types of graphical representations briefly: 

Line Graphs

A line graph is used to show how the value of particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data predicting further trends. 

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A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

graphical representation of a company

Histograms 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

graphical representation of a company

Line Plot 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point. 

graphical representation of a company

Stem and Leaf Plot 

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

graphical representation of a company

Box and Whisker Plot 

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

graphical representation of a company

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

graphical representation of a company

Graphical Representations used in Maths

Graphs in maths are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

  • Value-Based or Time Series Graphs

Frequency Based

Value-based or time series graphs .

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study. 

Example: Frequency Polygon, Histograms.

Principles of Graphical Representations

All types of graphical representations require some rule/principles which are to be followed. These are some algebraic principles. When we plot a graph, there is an origin, and we have our two axes. These two axes divide the plane into four parts called quadrants. The horizontal one is usually called the x-axis and the other one is called the y-axis. The origin is the point where these two axes intersect. The thing we need to keep in mind about the values of the variable on the x-axis is that positive values need to be on the right side of the origin and negative values should be on the left side of the origin. Similarly, for the variable on the y-axis, we need to make sure that the positive values of this variable should be above the x-axis and negative values of this variable must be below the y-axis. 

graphical representation of a company

Advantages and Disadvantages of using Graphical System

Advantages: 

  • It gives us a summary of the data which is easier to look at and analyze.
  • It saves time.
  • We can compare and study more than one variable at a time.

Disadvantage: 

It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 

General Rules for Graphical Representation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

  • Whenever possible, the data source must be mentioned for the viewer.
  • Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
  • The measurement Unit should be mentioned in the top right corner of the graph.
  • The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
  • Last but not the least, a suitable title should be chosen.

Frequency Polygon

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

graphical representation of a company

Sample Problems

Question 1: What are different types of frequency-based plots? 

Answer: 

Types of frequency based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

Solution: 

Steps:  Put each of the channels on the x-axis The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

Steps:  Put each of the x-axis row value on the x-axis joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

Steps:  Draw the class intervals on the x-axis and frequencies on the y-axis. Calculate the mid point of each class interval. Class Interval Mid Point Frequency 0-3 1.5 3 3-6 4.5 4 6-9 7.5 2 9-12 10.5 6 Now join the mid points of the intervals and their corresponding frequencies on the graph.  This graph shows both the histogram and frequency polygon for the given distribution.

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