Data science case interviews (what to expect & how to prepare)

Data science case study

Data science case studies are tough to crack: they’re open-ended, technical, and specific to the company. Interviewers use them to test your ability to break down complex problems and your use of analytical thinking to address business concerns.

So we’ve put together this guide to help you familiarize yourself with case studies at companies like Amazon, Google, and Meta (Facebook), as well as how to prepare for them, using practice questions and a repeatable answer framework.

Here’s the first thing you need to know about tackling data science case studies: always start by asking clarifying questions, before jumping in to your plan.

Let’s get started.

  • What to expect in data science case study interviews
  • How to approach data science case studies
  • Sample cases from FAANG data science interviews
  • How to prepare for data science case interviews

Click here to practice 1-on-1 with ex-FAANG interviewers

1. what to expect in data science case study interviews.

Before we get into an answer method and practice questions for data science case studies, let’s take a look at what you can expect in this type of interview.

Of course, the exact interview process for data scientist candidates will depend on the company you’re applying to, but case studies generally appear in both the pre-onsite phone screens and during the final onsite or virtual loop.

These questions may take anywhere from 10 to 40 minutes to answer, depending on the depth and complexity that the interviewer is looking for. During the initial phone screens, the case studies are typically shorter and interspersed with other technical and/or behavioral questions. During the final rounds, they will likely take longer to answer and require a more detailed analysis.

While some candidates may have the opportunity to prepare in advance and present their conclusions during an interview round, most candidates work with the information the interviewer offers on the spot.

1.1 The types of data science case studies

Generally, there are two types of case studies:

  • Analysis cases , which focus on how you translate user behavior into ideas and insights using data. These typically center around a product, feature, or business concern that’s unique to the company you’re interviewing with.
  • Modeling cases , which are more overtly technical and focus on how you build and use machine learning and statistical models to address business problems.

The number of case studies that you’ll receive in each category will depend on the company and the position that you’ve applied for. Facebook , for instance, typically doesn’t give many machine learning modeling cases, whereas Amazon does.

Also, some companies break these larger groups into smaller subcategories. For example, Facebook divides its analysis cases into two types: product interpretation and applied data . 

You may also receive in-depth questions similar to case studies, which test your technical capabilities (e.g. coding, SQL), so if you’d like to learn more about how to answer coding interview questions, take a look here .

We’ll give you a step-by-step method that can be used to answer analysis and modeling cases in section 2 . But first, let’s look at how interviewers will assess your answers.

1.2 What interviewers are looking for

We’ve researched accounts from ex-interviewers and data scientists to pinpoint the main criteria that interviewers look for in your answers. While the exact grading rubric will vary per company, this list from an ex-Google data scientist is a good overview of the biggest assessment areas:

  • Structure : candidate can break down an ambiguous problem into clear steps
  • Completeness : candidate is able to fully answer the question
  • Soundness : candidate’s solution is feasible and logical
  • Clarity : candidate’s explanations and methodology are easy to understand
  • Speed : candidate manages time well and is able to come up with solutions quickly

You’ll be able to improve your skills in each of these categories by practicing data science case studies on your own, and by working with an answer framework. We’ll get into that next.

2. How to approach data science case studies

Approaching data science cases with a repeatable framework will not only add structure to your answer, but also help you manage your time and think clearly under the stress of interview conditions.

Let’s go over a framework that you can use in your interviews, then break it down with an example answer.

2.1 Data science case framework: CAPER

We've researched popular frameworks used by real data scientists, and consolidated them to be as memorable and useful in an interview setting as possible.

Try using the framework below to structure your thinking during the interview. 

  • Clarify : Start by asking questions. Case questions are ambiguous, so you’ll need to gather more information from the interviewer, while eliminating irrelevant data. The types of questions you’ll ask will depend on the case, but consider: what is the business objective? What data can I access? Should I focus on all customers or just in X region?
  • Assume : Narrow the problem down by making assumptions and stating them to the interviewer for confirmation. (E.g. the statistical significance is X%, users are segmented based on XYZ, etc.) By the end of this step you should have constrained the problem into a clear goal.
  • Plan : Now, begin to craft your solution. Take time to outline a plan, breaking it into manageable tasks. Once you’ve made your plan, explain each step that you will take to the interviewer, and ask if it sounds good to them.
  • Execute : Carry out your plan, walking through each step with the interviewer. Depending on the type of case, you may have to prepare and engineer data, code, apply statistical algorithms, build a model, etc. In the majority of cases, you will need to end with business analysis.
  • Review : Finally, tie your final solution back to the business objectives you and the interviewer had initially identified. Evaluate your solution, and whether there are any steps you could have added or removed to improve it. 

Now that you’ve seen the framework, let’s take a look at how to implement it.

2.2 Sample answer using the CAPER framework

Below you’ll find an answer to a Facebook data science interview question from the Applied Data loop. This is an example that comes from Facebook’s data science interview prep materials, which you can find here .

Try this question:

Imagine that Facebook is building a product around high schools, starting with about 300 million users who have filled out a field with the name of their current high school. How would you find out how much of this data is real?

First, we need to clarify the question, eliminating irrelevant data and pinpointing what is the most important. For example:

  • What exactly does “real” mean in this context?
  • Should we focus on whether the high school itself is real, or whether the user actually attended the high school they’ve named?

After discussing with the interviewer, we’ve decided to focus on whether the high school itself is real first, followed by whether the user actually attended the high school they’ve named.

Next, we’ll narrow the problem down and state our assumptions to the interviewer for confirmation. Here are some assumptions we could make in the context of this problem:

  • The 300 million users are likely teenagers, given that they’re listing their current high school
  • We can assume that a high school that is listed too few times is likely fake
  • We can assume that a high school that is listed too many times (e.g. 10,000+ students) is likely fake

The interviewer has agreed with each of these assumptions, so we can now move on to the plan.

Next, it’s time to make a list of actionable steps and lay them out for the interviewer before moving on.

First, there are two approaches that we can identify:

  • A high precision approach, which provides a list of people who definitely went to a confirmed high school
  • A high recall approach, more similar to market sizing, which would provide a ballpark figure of people who went to a confirmed high school

As this is for a product that Facebook is currently building, the product use case likely calls for an estimate that is as accurate as possible. So we can go for the first approach, which will provide a more precise estimate of confirmed users listing a real high school. 

Now, we list the steps that make up this approach:

  • To find whether a high school is real: Draw a distribution with the number of students on the X axis, and the number of high schools on the Y axis, in order to find and eliminate the lower and upper bounds
  • To find whether a student really went to a high school: use a user’s friend graph and location to determine the plausibility of the high school they’ve named

The interviewer has approved the plan, which means that it’s time to execute.

4. Execute 

Step 1: Determining whether a high school is real

Going off of our plan, we’ll first start with the distribution.

We can use x1 to denote the lower bound, below which the number of times a high school is listed would be too small for a plausible school. x2 then denotes the upper bound, above which the high school has been listed too many times for a plausible school.

Here is what that would look like:

Data science case study illustration

Be prepared to answer follow up questions. In this case, the interviewer may ask, “looking at this graph, what do you think x1 and x2 would be?”

Based on this distribution, we could say that x1 is approximately the 5th percentile, or somewhere around 100 students. So, out of 300 million students, if fewer than 100 students list “Applebee” high school, then this is most likely not a real high school.

x2 is likely around the 95th percentile, or potentially as high as the 99th percentile. Based on intuition, we could estimate that number around 10,000. So, if more than 10,000 students list “Applebee” high school, then this is most likely not real. Here is how that looks on the distribution:

Data science case study illustration 2

At this point, the interviewer may ask more follow-up questions, such as “how do we account for different high schools that share the same name?”

In this case, we could group by the schools’ name and location, rather than name alone. If the high school does not have a dedicated page that lists its location, we could deduce its location based on the city of the user that lists it. 

Step 2: Determining whether a user went to the high school

A strong signal as to whether a user attended a specific high school would be their friend graph: a set number of friends would have to have listed the same current high school. For now, we’ll set that number at five friends.

Don’t forget to call out trade-offs and edge cases as you go. In this case, there could be a student who has recently moved, and so the high school they’ve listed does not reflect their actual current high school. 

To solve this, we could rely on users to update their location to reflect the change. If users do not update their location and high school, this would present an edge case that we would need to work out later.

To conclude, we could use the data from both the friend graph and the initial distribution to confirm the two signifiers: a high school is real, and the user really went there.

If enough users in the same location list the same high school, then it is likely that the high school is real, and that the users really attend it. If there are not enough users in the same location that list the same high school, then it is likely that the high school is not real, and the users do not actually attend it.

3. Sample cases from FAANG data science interviews

Having worked through the sample problem above, try out the different kinds of case studies that have been asked in data science interviews at FAANG companies. We’ve divided the questions into types of cases, as well as by company.

For more information about each of these companies’ data science interviews, take a look at these guides:

  • Facebook data scientist interview guide
  • Amazon data scientist interview guide
  • Google data scientist interview guide

Now let’s get into the questions. This is a selection of real data scientist interview questions, according to data from Glassdoor.

Data science case studies

Facebook - Analysis (product interpretation)

  • How would you measure the success of a product?
  • What KPIs would you use to measure the success of the newsfeed?
  • Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?

Facebook - Analysis (applied data)

  • How would you evaluate the impact for teenagers when their parents join Facebook?
  • How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
  • How would you set up an experiment to understand feature change in Instagram stories?

Amazon - modeling

  • How would you improve a classification model that suffers from low precision?
  • When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?

Google - Analysis

  • You have a google app and you make a change. How do you test if a metric has increased or not?
  • How do you detect viruses or inappropriate content on YouTube?
  • How would you compare if upgrading the android system produces more searches?

4. How to prepare for data science case interviews

Understanding the process and learning a method for data science cases will go a long way in helping you prepare. But this information is not enough to land you a data science job offer. 

To succeed in your data scientist case interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

For more information on how to prepare for data science interviews as a whole, take a look at our guide on data science interview prep .

4.1 Practice on your own

Start by answering practice questions alone. You can use the list in section 3 , and interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. 

Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. This will help you get used to the answer framework and get used to answering data science cases in a structured way.

4.2 Practice with peers

Once you’re used to answering questions on your own , then a great next step is to do mock interviews with friends or peers. This will help you adapt your approach to accommodate for follow-ups and answer questions you haven’t already worked through.

This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

4.3 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

Interview coach and candidate conduct a video call

Future-Proof Your Career, Master Data Skills + AI

case study data preparation

Data Science Case Study Interview: Your Guide to Success

by Enterprise DNA Experts | 10:29 pm EST | November 28, 2023 | Careers

Data Science Case Study Interview: Your Guide to Success

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Data Science Case Study Interview

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Problem-Solving Scenarios in Data Science Interview

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Evaluation Metrics and Validation for case study interview

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Tips to Master Data Science Case Study Interviews

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

Communication and Presentation of Results in interview

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

case study data preparation

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Data Analytics Case Study Guide (Updated for 2024)

Data Analytics Case Study Guide (Updated for 2024)

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

Data Analytics Case Study Vide Guide

With data analyst case questions, you will need to answer two key questions:

  • What metrics should I propose?
  • How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

Data Analytics Case Study Question: Sample Solution

Data Analytics Case Study Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

  • If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
  • If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

product analytics on screen

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

  • Who uses this social network? How do they interact with each other?
  • Has there been any performance issues that might be causing the problem?
  • What are the goals of this particular launch?
  • Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

  • The decreasing comments issue isn’t a result of a declining user base.
  • The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

  • January: 10000 users, 30000 comments, 3 comments/user
  • February: 20000 users, 50000 comments, 2.5 comments/user
  • March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

Data Preparation for Analytics Using SAS by Gerhard Svolba

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Case Studies

Chapter 25   Case Study 1—Building a Customer Data Mart

Chapter 26   Case Study 2—Deriving Customer Segmentation Measures from Transactional Data

Chapter 27   Case Study 3—Preparing Data for Time Series Analysis

Chapter 28   Case Study 4—Preparing Data in SAS Enterprise Miner


In this part we will cover four case studies for data preparation. These case studies refer to the content we presented in earlier chapters and put together in the context of a concrete question. In the case studies we will show example data and complete SAS code to create from the input data the respective output data mart. The following case studies will be examined:

•  building a customer data mart

•  deriving customer segmentation ...

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  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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case study data preparation

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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Case Interview 101: The Online Guidebook

“Case Interview” is the cornerstone of consulting recruitment, playing a decisive role in final results. In 30 minutes, your “consulting” qualities will be tested to the limit as you cruise through a hypothetical “consulting project” with the interviewer.

Yes, this is a BIG topic. The depth of content in this single article is HUGE with various chapters ranging from beginner’s topics to more advanced ones. You would want to bookmark this page and go back often throughout your whole preparation journey.

What is a case interview?

A case interview is a job interview where the candidate is asked to solve a business problem. They are often used by consulting firms, and are among the hardest job interviews, testing both problem-solving skills and “soft” skills. Case interviews often last 30-45 minutes each, and firms can utilize up to 6 case interviews, usually divided into 2 rounds.

Example case questions:

  • “We have a restaurant called “In-and-out Burger” with recently falling profits. How can you help?”
  • “The CEO of a cement company wants to close one of its plants. Should they do it?”
  • “A top 20 bank wants to get in top 5. How can the bank achieve that goal?”

Case interviews are modeled after the course of actions real consultants do in real projects – so success in case interviews is seen by consulting firms as a (partial) indication of a good management consultant.

During the interview, the interviewer will assess your ability to think analytically, probe appropriate questions, and make the most client-friendly pitches. Be noted that the analytical thought process is more important than arriving at correct answers.

Generally, there are 2 styles of conducting cases:  Candidate-led and Interviewer-led. 

case study data preparation

Candidate-led cases

On this end, the interviewer rarely intervenes; the candidate will lead the approach from structuring the problem, drawing frameworks, asking for data, synthesizing findings, to proposing solutions. This format can be difficult for beginners but it provides you with much control over the case.

Interviewer-led cases

On this end, the interviewer controls the process in significant ways. He or she has the candidate work on specific parts of the overall problem and sometimes disregards the natural flow of the case. The game here is not to solve the one big problem, but rather to nail every question, every pitch, every mini-case perfectly. Because the evaluation is done on a question basis, the level of insightfulness required is higher.

Most cases will fall somewhere in the middle section of that spectrum, but for educational purposes, we need to learn case interviews from both extremes ends.

Great details in each and every aspect of the case, as well as tips, techniques and study plans are coming in the chapters below. You may skip straight to Chapter 3 if you have business background and confidence in your own understanding of the terminology used in case interviews. 

To better understand or practice candidate-led and interview-led cases, let’s book a personal meeting with our coaches . At MConsultingPrep, you can connect with consulting experts who will help you learn the ins and outs of both cases and the solving approach to each one. Get “real” practice now!

Case interview starter guide for non-business students

All consulting firms claim that all educational backgrounds have equal chances. But no matter what, case interview reflects  real-life business problems and you will, therefore, come across business concepts .

Not everybody has the time to go to a full Business Undergraduate program all over. So through this compact Chapter 2, I will provide you, the non-business people, with every business concept you need in case interviews.

Accounting and financial terms – The language of business

Accounting & Financial Terms are often called the language of business, which is used to communicate the firm’s financial and economic information to external parties such as shareholders and creditors.

There are three basic financial statements : Balance Sheet, Income Statement, and Cash Flow Statement.

Balance Sheet

A snapshot of the current stage of the company’s property, debt, and ownership at one given point in time, showing:

  • Assets: what the company owns: Building, Equipment, Cash, Inventory, along with some other intangible items.
  • Liabilities: what the company owes: Loans, overdrafts, bills to be paid, etc. Debt is like negative assets.
  • Equity (Net worth): Calculate by taking Assets subtract Liabilities.

The neat thing about the Balance sheet is that it’s always balanced. Every action, every transaction changes the three components but it’s always in harmony.

Income Statement

A record of the business performance through a period of time , given it a quarter or a year. The Income Statement directly tells you how the company is doing in terms of making money, the heart of any business.

From the top to bottom, the Income Statement shows the Revenues, Costs, and Profits. That’s why often, Profits are referred to as the “bottom line”.

There are a few types of costs to notice – see the two pictures below this table.

One important thing to notice is that even though it may seem like, the Income Statement does NOT necessarily relate to cash. Many times, especially for B2B transactions, the selling happens before the money flow. Therefore, we may have to record revenue without having the cash.

Cash Flow Statement

There’s a famous saying that: Income statement is an opinion, Cash Flow statement is a fact.

The Cash Flow statement just strictly monitors the cash flow in or out, categorized into different sections. Three of them are:

  • Operation: illustrates how much cash the company can generate from its products and services.
  • Financial: includes the sources of cash from investors or banks and the uses of cash paid to shareholders.
  • Investing: includes any sources and uses of cash from a company’s investments.

case study data preparation

Upon completion this section, you should be able to read and interpret financial statements for business diagnosis and decision-making.

More importantly, you possess the conceptual base to start solving case interviews on your own. Do not forget that, as with any other language, becoming proficient with accounting and financial terms require constant practice.

Organizational structure – The heart of a company

When it comes to organizational structure, it is important to notice the fine line between the company’s ownership and management .

Technically, at the highest level, there are shareholders . For private companies, the group of shareholders and their shares are not necessarily disclosed and publicly tradable. For public companies, on the other hand, shares are publicly traded on different stock exchanges. One of the most famous is the NYSE, which stands for New York Stock Exchange.

case study data preparation

  • A company can have one, a few, or millions of individual owners, but being governed by the Board of Directors – a group of people elected by owners, with the President or Chairman being their highest leader.
  • The Board usually hires a management team to manage the company. They are led by the Chief Executive Officer – CEO , who makes every decision on day-to-day work. Most of the time, the Board of Directors doesn’t directly intervene in the CEO’s work, but they reserve the right to fire CEOs.
  • Besides that, there’s a committee called Supervisors. The supervisor’s job is to independently monitor the CEO and the management team and report to the Board.

Below CEOs, there are two general two ways of structuring the company. One way is through business lines and the other one is through functions. Think of business lines as mini-companies themselves inside the big company.

case study data preparation

Within functions, here are a few most typical divisions most companies have:

case study data preparation

Business strategy concepts

Even with business students, strategy is a challenging topic – especially with those without a strategy major. These fundamental concepts will get you started.

  • Organization: In general, this refers to how a company is organized, what are different components that make up a company
  • Governance refers to how a company is managed and directed, how well the leader team runs. The leader team includes the Board of Directors and Board of Managers. A company with good governance has good leadership people, tight control, and effective check & balance processes, etc.
  • Process looks like rules and common practices of having a number of processes, entailing every single activity. Process design should include 4 factors: who, what, when, and accompanied tools.

For example, let’s look at Kim’s family picnic process.

case study data preparation

  • The who part is presented on the y-axis, left-hand side, labeling all departments, a.k.a: family members, involved.
  • The what part is presented through the big mid-session with each box represents every single activity.
  • The when and tools parts are presented at the bottom

B2B  vs B2C : stand for “business-to-business” and “business-to-customer”. These two terms refer to two types of transactions a company typically does: transactions with other companies and transactions with individual customers.

Bottom-up vs Top-down: this refers to two opposite schools of thought or action. Top-down usually encompasses various general branches while bottom-up tends to narrowly focus. 

Management consulting terms & concepts

These are the most common consulting terms you may encounter not just in case interviews but also in consulting tasks .

  • Lever: Think of this as one or a group of initiatives, actions to perform to meet certain goals. e.g. some levers to help increase customer experience in a hotel are free breakfast, free Wi-Fi, 24/7 support, etc.
  • Best practice: Refers to how things should be done, especially if it has been successfully implemented elsewhere.
  • Granular: This refers to how specific and detailed a break-down or an issue goes. For example, a not-so-granular breakdown of the NBA is the West and the East conferences. A much more granular is something like this: Leagues, Conferences, Divisions, and Teams.
  • MECE: MECE is so important and we explain it in detail in this article. In short, MECE is the standard, per which we can divide things down in a systematic, comprehensive, and non-overlapping way.

There are three parameters the consulting world uses in the categorization of businesses.

  • Industry: used to group different companies mostly based on their product (Banking, Construction, Education, Steel Industry, etc.)
  • Function: is the categorization mostly based on missions and the type of roles of different parts of a company. We can count some as Human Resource, Finance, Strategy, Operation, Product Development, etc.
  • Location: is where things are, geographically.

Normally two consultants ask each other “What do you work on?”, they need to give 3 pieces of information in all of those three parameters, such as “I worked on a Cement project, focusing on Finance, in Southeast Asia”. In fact, all of the McKinsey support networks are organized in this way. During my projects, I would need to speak to some Cement experts, some Finance experts, and some local experts as well.

This chapter is relatively long, yet it is still way shorter than 4 years at business college. I hope this will act as a great prerequisite to your case interview study. Make sure that you have mastered all of these content before really tackling the Case Interview.

Case interview example – The typical flow

In a simplified way, a typical case would go through these phrases (we will talk about exceptions in great detail later):

Case question -> Recap -> Clarification -> Timeout -> Propose issue tree -> Analyze issue tree -> Identify root-causes -> Solutions -> Closing pitch

Problem-solving fundamentals – Candidate-led cases

case study data preparation

Though most cases will be conducted in mixed format, let’s dive deep and learn about each extreme end of the spectrum to get the full picture.

Even though this is the harder format, it shows us the foundation of how management consulting works, i.e: the consulting problem-solving logics!

If you were exposed to case interviews, you have probably heard about some of these concepts: framework, issue tree, benchmark, data, root cause, solutions, etc. But how do they all fit into the picture?

It all starts with the PROBLEM

Before getting into anything fancy, the first step is to define and be really clear about the problem.

This sounds easy but can be quite tricky. Here are a few guidelines:

1. What’s the objective?

2. What’s the timeline required?

3. Any quantified or well-described goals?

For example, one client can state a problem as: “I lost my car key”. In normal contexts, this is a perfectly simple and straightforward problem. But a consultant tackling this would go ask clarification questions to achieve even more details:

1. Objective: the client in fact just needs to be able to use the car.

2. Timeline: this is an urgent need. He is happy only if we can help him within the next hour.

3. Specificity: help the client put his car into normal operation like before he lost the key.

case study data preparation

Find the ROOT-CAUSE, don’t just fix the symptom

To completely wipe out the problem and create long-lasting impacts, consultants always  search and find the root causes.

For example, fixing the symptom is like you breaking the door lock, getting into the ignition electrics behind the wheel, and connecting the wires to start the car.

That does fix the surface symptom: the client can drive the car. But it does NOT create a long-lasting impact because without you there, the car can’t be started. The client will need to rely on you every single time. Plus, more problems even arise (now he needs to fix the broken door lock too).

A much better approach is to find the root cause. What is the bottom-line reason causing the problem? Once we trace, find, and fix it, the problem will be gone for good.

In this example, the root cause is “the lost key”. We need to find its location!

case study data preparation

Use ISSUE TREE to isolate potential root-causes into groups

There could be thousands of possible root-causes. How do we make sure every possible one is examined? If we are to list out all thousands and test one by one, there is simply not enough time. On the other hand, if we just list out some of the most “possible” ones, we run a high risk of missing the true root-cause.

This is where we need issue trees ! We would group possible root-causes into big groups. Those big groups will have smaller sub-groups and so on. All is done in the spirit of top-down and MECE. By doing this, we have an organized way to include all possible root-causes.

Continue with the example: A “bottom-up” approach to search for the car key is to go straight to specific places like the microwave’s top, the black jacket pocket, under the master bed, etc. There can be thousands of these possible locations.

The top-down approach is to draw an issue tree, breaking the whole house into groups and examine the whole group one by one. For example: first floor, second floor, and the basement.

case study data preparation

Issue Tree only works if it’s MECE

What happens if we break down the search area into the First floor and East wing? The search area would not cover the whole house and there will be some overlapping which creates inefficiencies.

So for an issue tree to work properly, it has to be MECE – Mutually Exclusive and Collectively Exhaustive … which in simple language just mean 2 things: no overlap and no gap

case study data preparation

How to draw MECE issue trees? Use FRAMEWORKS!

Each problem requires a unique issue tree. Coming up with MECE and spot-on issue trees for each problem can be really difficult. This is where “framework” helps.

Think of frameworks as “frequently used templates” to draw issue trees in any particular context. Many people use the word “framework” to refer to “issue tree” but this is conceptually incorrect.

case study data preparation

We will talk about frameworks in more detail in the below chapters. You can also check out this deep-dive article on Frameworks.

Choosing which branch to go to first? Use HYPOTHESES!

So let’s say you have an issue tree of First floor, Second floor, and Third floor. Now what?

To make the problem-solving process even faster and more efficient, we use hypotheses. In simple language, it’s the educated guess of where the root cause may lie in. So we can prioritize the branch with the highest chance.

So let’s say, the client spends most time on the first floor, it’s where he/she most likely leaves the car key. Any consultant would hypothesize that the root cause is in the first-floor branch and go search there first.

Notice: hypothesis and issue tree always go together. It doesn’t make sense to draw an issue of First, Second, and Third floor and hypothesize that the key is in the East wing. Many times, hypotheses are even the inspiration to draw issue trees.

case study data preparation

How to test a branch? Use DATA and compare it with BENCHMARK

Now that we decide to test the branch “First floor”, how do we do that?

We prove or disprove our hypothesis by collecting DATA. That data is then compared with benchmarks to shed more meaning. Two main types of benchmarks are: historical and competitive. For example, let’s say by some magic, the client has a metal detection machine that can measure the metal concentration of any space.

To test the “first-floor” branch, the consultant would come to the first floor, measure the metal concentration and compare it with the data before the car key is lost, a.k.a: historical benchmark.

case study data preparation

If a hypothesis is true, drill down; if it’s false, go sideways

What happens when we test a hypothesis?

Assuming that we have access to enough data, it either gets proven TRUE or proven FALSE. How do we proceed from here? 

  • Proven True: go DOWN the issue tree to sub-branches! Let’s say the metal detector identified the key IS indeed on the first floor. Go deeper. Draw sub-branches of that first-floor branch and repeat the process.
  • Proven False: go HORIZONTAL to other big branches! Let’s say the metal detector denies the key presence on the first floor. We then can cross out this branch and go test others, a.k.a: the second and third floor.

Test, Sleep, Test, Repeat … until the ROOT-CAUSE shows up!

case study data preparation

Once identified the ROOT-CAUSES, go for SOLUTIONS

With all proven root causes identified, the last step is to come up with solutions to kill the problem … and we are done! There can be multiple solutions to each root cause. These solutions should attack straight to the root cause.

case study data preparation

Case interview questions – Interviewer-led cases

While candidate-led cases are all about the logical foundation of problem-solving, interviewer-led cases are more about tackling each individual question itself. The structure of the whole case is relatively loose and flexible.

In this chapter, we touch on some of the most popular ones. You can read in-depth about each in this designated article.

Framework/Issue Tree questions

“Which factors would you consider when tackling this problem?”

This is one of the most popular question types in case interviews, often asked in the beginning. It comes with several shapes and forms, but the real meaning is always: “Give me the bloody issue tree!”

So how do you tackle it? Just like in candidate-led cases. Take a timeout; brainstorm about the problem and how it should be broken down into; plug a few frameworks to see how it looks; and go for the most appropriate issue tree.

Unlike in candidate-led cases where you only present the upper-most layer, here you should walk the interviewer through the whole issue tree, covering at least 2 layers. Interviewer-led cases are much less interactive. It’s more like they ask you a question, and you deliver a comprehensive and big answer. They ask you another one. And so on.

Market-sizing / Guesstimate questions

“How many face masks are being produced in the whole world today?”

This is among  the most popular question types and you will likely face a few of them throughout several interview rounds. These questions ask you to “guess” and come up with number estimations in non-conventional contexts. These questions are called “Guesstimate”.

When a guesstimate question asks you to “guess” the size of a market, it’s called a “Market-sizing” question. Though this variation is very popular in consulting, the nature is nothing different from other Guesstimate questions.

It can be intimidating to face a question like this. Where to start? Where to go? What clues to hold on to?

The key is to understand that you don’t have to provide an exact correct answer. In fact, nobody knows or even cares. What matters is HOW you get there. Can you show off consulting traits, using a sound approach to come up with the best “estimate” possible?

Read the designated article on this for great details. Here, let’s walk through the 4-step approach that you can apply to absolutely every market-sizing question.

Step 1: Clarify

Make sure you and the interviewer are on the same page regarding every detail and terminology, so you won’t be answering the wrong question.

Step 2: Break down the problem

Break the item in the question (number of trees in Central Park, market size of pickup trucks) down into smaller, easy-to-estimate pieces.

Step 3: Solve each piece

Estimate each small piece one at a time; each estimation should be backed by facts, figures, or at least observations.

Step 4: Consolidate the pieces

Combine the previous estimations to arrive at a final result; be quick with the math, but don’t rush it if you aren’t confident.

Math questions

“If the factory can lower the clinker factor by 0.2, how much money will they save on production cost?”

Almost all cases involve some math. So you will face math questions for sure. These “questions” can go at you either explicitly and implicitly. Sometimes, the case interviewer will ask out loud a math problem and have you solve. But sometimes, you have to do multiple calculations on the background to push the analysis forward.

Either way, a strong math capability will help you a lot during cases and the future career in consulting. See this Consulting Math article for more details.

Chart insight questions

“What insights can you draw from this chart?”

Consultant works with data and a big chunk of those data are presented by charts. Many times, the interviewer would pull out a sanitized exhibit from an actual project and have you list out insights you can see from it.

There are many types of charts. Getting yourselves familiar with the most popular ones is not a bad idea.

  • Bar charts simply compare the values of items that are somewhat parallel in nature.

case study data preparation

  • Line charts illustrate the continuous nature of a data series, e.g: how my heart rate evolved through time.

case study data preparation

  • Pie charts illustrate proportions, i.e “parts of a whole” analyses.

case study data preparation

  • Scatter-plots use data points to visualize how two variables relate to each other. Correlation for example.

case study data preparation

Tips on tackling chart-insights questions:

1. Read labels first: from Chart titles, Axis titles, Legend titles, etc. Don’t jump straight to the content of the chart. It takes more time to get lost there and has to go back to read the label. Besides, you may also run a risk of misunderstanding the content.

2. Look for abnormalities: important insights always lie in those unexpected and abnormal data. Look for them!

Value proposition questions

“What factors does a customer consider when deciding which car insurance company to buy from?”

In simple language, this question type asks you: what do the customers want? Understanding exactly this need will put any company in the best position to tailor products/services.

Like any other questions, Value-proposition questions are not only about correctly identifying customer preferences (insights) but also about analyzing and delivering the answer in a structured fashion. Here are a few tips for you to do that:

How to be more insightful: 

  • It always helps to break customers into groups and provide different substances for each.
  • Put yourselves into the customers’ shoes. Think from the first-view perspective and more insights will arrive.
  • If there is any data/ information previously provided in the case, definitely use it.
  • A library of factors? Safety, speed, convenience, affordability, flexibility, add-on services, durability, fashion, ease of use, location, freshness, etc.

How to appear more structured:

  • Follow this structure: Customer group 1, Customer group 2, etc. Under each: Factor A, factor B, factor C.
  • Develop your personal script for this question type. Make sure it’s easy to follow and structured in nature.

Information questions

What kind of data do you need to test this hypothesis? How do you get data

Consulting is a data-driven industry. As consultants, we spent most of our time gathering and presenting data to clients ( see the What the heck does a consultant do video ). No surprise information questions are relatively popular in cases.

The best way to tackle this question type is to understand inside out the types of data actual consultants use in real projects. Because almost no candidate knows about this. This is also a very quick way to build rapport. The interviewer will feel like he/she is talking to a real consultant.

Case interview example video – Pandora case

Enough theory! Enough cute little illustrations here and there. Time to get our hands into a serious case interview example.

Notice the following when watching the video:

  • How the problem is given and clarified
  • How the problem-solving approach is layouted and executed
  • How the candidate use wording and frame the pitches
  • The dynamic of a case. How energy transfers from one to another person.

Every case is unique in its own way but principles are universal. The more examples you see, the better. This video is extracted from our  Case Interview End-to-end Secrets program, where you can find 10 complete examples like this and many other supplement contents.

How to prepare for case interviews

Case Interview preparation is a long and tough process. In an ocean of books, videos, programs, how do we navigate to maximize learning? Most materials floating around are quite good, at least in terms of substance. But the timing and the organization of them can be confusing.

  • Too much theory in the beginning can burn brain power very quickly.
  • Tackling cases without basics can develop bad habits, which eventually cost more time to unlearn.
  • Practicing complicated (or even just normal) cases in the beginning can destroy morale drastically.

So a good study plan is constantly switching between 3 activities: reading theory, watching examples, and practicing, with cases increasing difficulty level. It’s so crucial to start with super easy cases, be patient, and stay on that level until you are ready to move up. There are so many skills, habits, and scripts to develop and these take time.

“The quickest way to do just about everything is … Step by Step”

Even for candidates with cases coming up urgently, I still strongly recommend spending the most valuable time practicing cases that match your level. After all, cases are just the context. What you will be evaluated on is your approach, your skills, your techniques, etc.

So, this is a sample study plan you can adopt for yourselves:

Step 1: Learn the basics of case interview theory

  • Read this article thus far
  • Watch this  Case Interview 101 video

Step 2: Watch a simple case interview example

  • Read the sample case flow above.
  • Watch this  Case Interview Example video
  • Go to this list of free case examples and try to select a very simple one. If you can’t follow one, it’s probably not good for you. Just skip it.
  • Watch the first example in the  End-to-end Program

Step 3: Review the theory of case interview approaches  

  • Read deeply about the logical foundation of problem-solving in this BCG & Bain Case Interview article.
  • Watch intensively the logical foundation of problem-solving in this Candidate-led cases video.

Step 4: Do one mock case interview

  • Practice with consultants. They have the insight and knowledge to help you pass the interview. Discover our experienced coaches from McKinsey, BCG and Bain here .
  • Find a partner to practice with. Make sure you both watch this  Guide on how to conduct a case. A bad coach can do more harm than good.
  • Get your hand on another example in the  End-to-end Program. But this time, don’t just watch. Actively solve the case as you see it! Try to say out loud your version, then listen to the candidate, then hear the feedback!

Step 5: Start improving your business intuition

Business Intuition is like your natural sense of the business world: how to be insightful and creative in various business contexts, how to feed the “content” into your approach, etc. Think of this as a basketball player trained for muscle strength, agility, or durability. Intuition can be improved gradually through constantly exposing yourselves to a wide range of business situations and contexts.

You can do this by:

  • Read consulting publications. One article per day for example. Three wonderful sources are: McKinsey Insights, BCG Perspectives, and Bain Publications
  • Train  case interview questions individually. By isolating each part of the case, you can focus more on the substance. Hit that link or get more question training on the End-to-end Secret Program .

Step 6: Start training consulting math

  • Visit this in-depth consulting math article.
  • Train our  Mental Math methodology.

Step 7: Practice another mock case interview

At this stage, please still stick to very basic cases. The goal is to see all of the knowledge and skills above in real action. Again, this can be done by either:

  • Book a meeting with coaches
  • Find another partner to practice with. Just make sure you both watch this Guide on how to conduct a case. A bad coach is always more harmful than not practicing at all.
  • See another example in the End-to-end Program. Like the previous one, try actively solving the case as you see it! Say out loud your version, then listen to the candidate, then hear the feedback!

Step 8: Equip yourself with tips, techniques, and advance theory

  • Read on! The below chapters of this very article will provide you with more advanced theory and killer tips.
  • Watch the whole Tips & Techniques sections of the End-to-end Program. You will find 10 examples with clear walkthroughs of tips and techniques right in the middle of real action.

Step 9: Do further mock cases, review, and improve

Practicing for case interviews is a time consuming process – but as long as you have the right method, you will make it!

  • First, brush up on knowledge related to case interviews with the Case Interview End-to-End Secrets Program .
  • Second, get personalized practice with ex-consultants. That way, you’ll receive clear and tuned feedback to understand what to improve, building your own proper case approaches.  See a list of experienced coaches here .

Stay tuned with us on this website and our Youtube channel for continuously updated information on case interviews and management consulting recruitment; you can also subscribe to the newsletter below for free materials and other insightful content!

Good luck with your case prep!

Case interview tips – With instant results

Imagine a case interview just falls out of the sky and into your lap, scheduled for tomorrow – how can you even prepare?

The answer lies in a few “quick and dirty” tips, which I’ll share with you in a moment.

I am a firm believer in the 80-20 rule – which states that 20% of the causes lead to 80% of the consequences.

In the case interview prep context, 20% of your learning efforts will bring about 80% of the improvements – so the key to instantly and dramatically improving your case performance is to identify and focus on that 20%.

case study data preparation

In the next 8 chapters, I’ll tell you the killer tips and tricks that helped me get a McKinsey offer, the majority of which were previously only available in the premium End-to-End Secrets Program , including:

  • Chapter 9: Delivering the perfect case opening
  • Chapter 10: Remaining absolutely structured throughout the case
  • Chapter 11: Taking the best notes
  • Chapter 12: Getting out if stuck
  • Chapter 13: How to ask for data
  • Chapter 14: What to do when receiving data
  • Chapter 15: Deliver the most convincing closing pitch
  • Chapter 16: Developing your personal scripts

One thing before you proceed: don’t forget to learn the fundamentals, the question types, and the frameworks. Remember, these 20% tips can only get you 80% performance; if you want 100%, there’s  no substitute for hard work.

How to deliver the perfect case opening

The result of a case interview is determined  the first 3 minutes – and I’m not even exaggerating.

Most people will be put off by this fact – indeed, with all those efforts spent on learning for the later part of the case, and the hiring decision is made when you’re not even properly warmed up yet.

However, putting a spin on it, this is the 20% to focus on – if you nail the opening, you’ll make a better impression than most candidates; it’s also easier to perform well in 3 minutes than in 30 minutes, especially when the case hasn’t gotten tricky. Additionally, you can prepare the opening in a formulaic manner – essentially learning by heart until it becomes natural.

There are 7 steps in the perfect case opening formula:

1. Show appreciation

2. Announce case introduction

5. Announce case approach

7. Ask for a timeout

In this chapter, I’ll walk you through each of those steps.

Step 1: Show appreciation

The quickest way to score the first points with any interviewer is to  sincerely compliment them. Everybody loves compliments.

Case interviewers are not dedicated HR staff, but Engagement Managers, Partners, and Directors who conduct interviews ON TOP OF their projects as goodwill for the firm, so you should at least be thankful for the time they spend with you.

Begin your interview with a sincere “thank you” for the interesting case (if you have to fake these words because deep down you don’t like case interviews, you aren’t exactly cut out for the job).

Step 2: Announce case introduction

Announce you’re going to do  steps 3, 4, and 5.

This step is related to what I call the “map habit”, which I’ll describe in detail in the next chapter. For now, just understand that it  helps the interviewer follow your introduction, and shows you’re a structured person.

Step 3: Recap  

What is the key question of the case?

On a side note: one common mistake is to mix up step 3 with step 4 (clarify) – remember, don’t ask anything , just rephrase the case to ensure that you get it right.

Step 4:  Clarify

Ask questions to clear up any  potential confusion about the details of the case.

Case questions are always very short with a lot of vague details; if you don’t see the need to ask anything, you’re doing it wrong.

Run this checklist through your mind to help you clarify as many unclear points as possible:

  • Definitions: are there words you don’t understand or can be interpreted in multiple ways?
  • Timeframe: what is the “deadline” for solving this problem?
  • Measurement: how are the important variables (performance, revenue, etc.) measured?

Additionally, number your questions so it’s easier for you and the interviewer to keep track.

Step 5: Announce case approach

Roughly sum up  how you’ll analyze the problem.

Again, this is related to the map habit, which makes the overall case progress easier to follow.

There are 3 types of cases: (1) problem-solution, (2) should I choose A or B, and (3) how to do C. For each type, there is a different approach. The latter two are discussed in the “Advanced Logic” chapter, for now, we’ll continue with the first type: tell the interviewer you’re going to find the root cause to ensure long-lasting solutions, and to do that you’ll develop an issue tree.

Step 6: Align

Check if the interviewer  approves of your case approach.

This is an important habit of real consultants  because nobody wants to waste resources going in the wrong direction; interviewers expect candidates to show it in the case interview.

Simply ask “Does this sound like a reasonable approach to you?” – most likely the interviewer will give you the green light, but if you’re lucky he/she may even suggest a better approach.

Step 7: Ask for timeout

After you’ve gone continuously through the 6 steps above, ask the interviewer for timeout to (make this explicit) gather your thoughts and develop the first part of the issue tree.

Make the most of your timeout session, and keep it as short as possible. Any unnecessary silence will damage the impression and hurt your chances (refer to the End-to-End Program example in Chapter 6 to “feel” how awkward a lengthy timeout session is).

Case opening – Example script

Now it’s time to see how you can put all those steps into action!

Thank you for this very interesting case, I am really happy to get a chance to solve it!

The first step in solving any business problem is to make sure we solve the right one, so before diving into the problem, I would like to first recap the case, then ask a few clarification questions to make sure we’re both on the same page, and lastly announce my overall case approach.

So here is my understanding of the case:

  • [facts regarding the client and situation]
  • [key case question]

Does that correctly summarize the case?

<assume the interview confirms that your playback is correct>

Great, now I’d like to ask my three clarification questions:

  • [question 1]
  • [question 2]
  • [question 3]

<wait for answers>

Thank you for the clarification. Is there anything else I should be aware of?

Thanks for all the insights. It’s great that we all agree on the key details.

For the overall approach to this case, to completely wipe out the problem for a long-lasting impact, we will need to find out the root causes of this problem. To do that I will try to break the problem down into bite-size pieces with issue trees, in order to quickly isolate the root causes inside the branches, then drill down accordingly to gather information until we can draw actionable solutions.

So before I go on to establish my first issue tree, does that approach sound reasonable to you?

<assumes the interviewer agrees with your approach>

It’s great to see that we’re on the same page regarding the key details as well as the overall approach to the case. I do need some time to gather my thoughts, so may I have a short timeout?

Being structured throughout the case

The high stress and large amount of information in case interviews make it easy for even the brightest candidates to derail from the objective or present in an unstructured manner.

I’ll be sharing with you my 3 most impactful tips for keeping the structure in case interview:

1. The map habit

2. Numbering your items

3. Sticking to the big problem

The map habit

It means regularly and explicitly checking where you are, and where you’re doing next.

I call it the map habit because it’s similar to using a map while traveling – pausing every once in a while to check your location, destination, and direction.

This habit gives you a sense of direction and authority while making it easier for the interviewer to follow your case progress. It also makes you sound organized and systematic – a definitive mark of management consultants – and the interviewer will love it!

You’ll see this habit a lot in our Case Interview End-to-End Secrets Program, where candidates would often pause at each key step during the case. Do the same thing in your own case interviews, and you’ll greatly impress the interviewer.

Numbering your items

A very easy and effective way to make your pitches sound structured is to number each item.

The formula is simple: “There are X items that I’m going to say; they are: No.1 … No.2 … No.3 …”

By now you may have noticed that I use this structure many times throughout this guidebook – it’s already quite effective in written language, but it’s even more impactful in spoken communications!

Having this numbering habit will make it very easy for the listener to follow your speech, and it creates an impression of MECE (even if content-wise it’s not MECE).

Sticking to the big problem

There are two ways to keep yourself on track  all the time in those high-stress case interviews

1. Occasionally check your position on the issue tree, and quickly get back on track if it seems you’re “derailing”. If this sounds like the previous map habit, you’re right, it is the map habit.

2. Take good notes, with the case question being written big and bold on top of your scratch paper. That way you’ll be reminded every few seconds.

That last point brings us to the next issue: how to take notes.

How to take notes in case interviews

The best notes for case interviews are always  clear-cut, structured, and relevant.

Even the smartest candidates suffer from seemingly silly problems in case interviews – forgetting data, messing up the numbers, getting stuck with frameworks, losing sight of the original objective, etc. And in the true management consulting spirit, I set out to find the root causes.

And looking back at hundreds of coaching sessions I did, I found one thing in common – none of those candidates could take good notes.

case study data preparation

I’ll tell you precisely how I took notes to get a McKinsey offer; however, I hope that after this chapter, you can install the spirit of the method, not just the method itself.

So here we are, with the 3 groups of sheets laid out for the ideal note-taking:

1. Data sheets

2. Presentation sheets

3. Scratch sheets

Data sheets

Data sheets are used to store and process every piece of incoming data .

Try to draw tables for these sheets, because this not only makes the calculation process easier but also gives the impression that you’re a careful and organized person.

Also, remember to write only the results of calculations on this sheet, to keep it neat and tidy. Most of your calculations should be done mentally (see the article on Consulting Math for more details); if you really need to jot down the calculations, do it on the scratch sheets.

Presentation sheets

Presentation sheets are used to develop and present any “outgoing” content.

Your issue trees should be drawn on these sheets, along with the big-and-bold case question/objective right on top. When delivering your pitches, always turn around the presentation sheets so the interviewer can clearly read what’s on them.

As with the data sheets, avoid any messy “mid-process” drawings. Put them on the scratch sheets instead.

Scratch sheets

Scratch sheets exist to keep other sheets clean.

Ever felt irritated receiving a notebook full of correction marks? That’s exactly how the interviewer feels if you present with untidy notes. You should try your best to hide all the unorganized, messy parts of your thought process.

The scratch sheets provide a sanctuary for that unstructured part of yours – it’s okay to go all over the place for brainstorming, as long as you can organize the incoming resources and present in a systematic manner.

“I took the notes just as you instructed, but I still get stuck in cases. How can I avoid it?” – Well, that’s the subject for our next chapter – “Stuck” situations and how to get out of them.

Stuck in cases – What to do

We’ve all been there – that scarily awkward feeling when you don’t know what to do next in a case interview, that fear of being rejected.

In every “stuck” situation, the most important thing is to remain calm and collected (you’ll lose points if you panic) – then methodically work your way out. I’ll teach you how to get out of those situations, with style.

There are actually 3 different kinds of “stuck”, and for each, I have a different solution:

1. The “Framework” stuck

2. The “Data” stuck

3. The “I-Cannot-Find-The-Problem” stuck

Let’s go through each in detail.

The framework stuck

This situation happens when the candidate does not know which framework to use, and the secret tool for it, is “segmentation”.

Segmentation works just like any framework, and like a Swiss Army knife, it’s usually safe and easy to use. So if you’re unsure how to break things down, say these magic words:

“At this point, I’d like to break down this X item, and one good way is to use the natural segmentation within this line of business. So may I ask how they break this X item in this industry?”

If you get it right, the interviewer will reply with the most industry-relevant way to segment the item.

You may be wondering why I’m not talking about issue trees and frameworks here, after all the theory at the beginning of the guidebook.

The answer is that the textbook and “ideal” solution – learning the problem-solving fundamentals and deep-diving the frameworks to increase your flexibility – takes a lot of time, while the “cliched” solution – learning as many frameworks as possible, usually at the cost of depth – is inherently dangerous.

The data stuck

The “data stuck” happens when the candidate can’t extract relevant insights from the given data. And when this happens, ask for benchmarks.

Comparing with benchmarks is the quickest way to put data into perspective, yielding useful insights. There are 2 kinds of benchmarks – if you remember from the chapter on Candidate-led Cases: 

  • Historical benchmarks: data on the same entity in the past
  • Competitor benchmarks: data on similar/competing entities in the same timeframe

To ask for benchmarks, Just say the following lines:

“For now, I hypothesize that the root cause of the problem comes from the X branch of this issue tree. However, to further break down the problem in a spot-on way, I do need some information on the context of our client’s problem.

One of the quickest ways to grasp that context is to use competitor’s data; so can I have the X figure for our client’s competitors?”

The “I-Cannot-Find-The-Problem” stuck

This is the scariest “stuck” because there’s no obvious reason or solution – you’ve done your math right, your framework is suitable, and you’ve got a lot of interesting insights from data. Why are you still stuck?

From my experience in coaching sessions, there are 2 scenarios where this happens: (1) your issue tree is not MECE, and (2) if your issue tree is MECE, it does not isolate the problem.

You can try to avoid this in the first place by mastering the MECE principle, improving intuition, as well as aligning with the interviewer early and often.

But what if you still get stuck? The answer is to calmly admit you’ve hit a dead-end, and ask for time to fix the problem; be it the first or second scenario, you have to redraw your issue tree.

Literally use the following script:

“My whole analysis seems going towards a dead-end, which means either part of my issue tree is not MECE or my method of breaking down does not isolate the problem. Either way, I would like to take a timeout to have a look at it.”

You likely get stuck when practicing on yourself. That’s the reason why you need personal coaching. Veteran coaches at MConsultingPrep will give insightful feedback, propose actionable steps, and help you significantly enhance your performance. Find my coach !

How to ask for data

Data is the fuel for the case interview engine. Without it , your analysis can’t progress.

The problem is that interviewers don’t simply give out precious data for free. It has to be earned. There are 4 tips you can use to show that “worthiness”, and prompt the interviewer to supply you with the best information:

1. Create a good impression

2. Explain the purpose of the data

3. Explain the method of acquiring the data

4. Ask open-ended questions

Tip 1: Creating a good impression

The interviewer will love you if you think and act like a real consultant – if you can achieve that, he/she will always give you the best pieces of data available.

In this guidebook, there are countless tips to show your consulting characteristics – I even write a whole chapter on how to install consulting culture into your own personality. Generally, you must always be (1) structured , (2) fact-based, and (3) action-oriented.

Additionally, common people skills and interview tips also apply – show your appreciation by thanking for their help, keep a smile on your face to maintain a positive atmosphere, etc.

Tip 2: Explaining the purpose of the data

Say why you need that data, so the interviewer knows you can actually use it.

There are only two purposes for data in case interviews: (1) to test a hypothesis, and (2) to understand the context.

You can use the following scripts to when to reason your data requests:

“For now, I’m hypothesizing that the root cause of this problem comes from the X branch. Since this hypothesis can only be tested with the data on X, may I have those figures?”< testing hypothesis>

“For now, I hypothesize that the root cause of the problem comes from the X branch of this issue tree. However, to further break down the problem in a spot-on way, to better understand the context of our client’s problem, I will ask a few more questions. Does that sound reasonable to you?” < understanding the context>

Tip 3: Explaining the method to acquire the data

By stating how to get the data, you prove its feasibility and reinforce your data request.

In real consulting projects, data is not always available; the interviewer may rely on this logic and refuse to give you any information. So, when you ask for data, make sure your request is realistic, then state the method to acquire it using these words:

“If this was a real project, this information can be acquired from/by X source/method”.

In our  Prospective Candidate Starter Pack ,   there is a sheet listing all the possible sources of information in consulting projects, which you can download for your own use, along with many other free case interview materials.

Accurately explaining the data acquisition method also shows that you’ve done your homework and you know the consulting industry inside-out. Any interviewer will be greatly impressed.

Ask open-ended questions

This prompts the interviewer to give you data you haven’t thought of.

The precise questions mostly depend on specific cases (meaning you need to sharpen your intuition), but there is a Swiss Army knife here: “Is there anything else?” – which is a question real consultants ask several times a day, at the end of their conversations.

Use open-ended questions when you feel you might be missing something – for example, during clarification – and only after a series of well-defined, close-ended questions. Otherwise, you risk appearing lazy and over-reliant.

What to do when receiving data

Suppose the interviewer agrees to give you data. Now what?

Time to shine! If you do these following 3 steps, even just once, in the interviewer’s mind, you already pass:

1. Acknowledge the data and show appreciation

2. Describe the data, especially its notable features

3. State the implications of the data

Let’s dive into each separately.

Step 1: Acknowledging the data

Simply  thank the interviewer for the interesting piece of data.

Firstly, it confirms that you have received, and can understand the data.

Secondly, it’s always good to give out modest, subtle compliments to the interviewer. Trust me, conducting case interviews is hard work, and the interviewer does appreciate those little compliments.

Last but not least, it buys you a few seconds to fully absorb the new information and minimize any possible silence.

Step 2: Describing the data

Summarize  the most important insights you can extract.

Don’t recite a short essay about the data, there is no time for that. Quickly and mentally calculate all the important points, then state it out loud in 1-2 sentences.

This step has several uses:

It showcases your consulting math skills (chart insights and mental calculation)

It eliminates the silence during your analysis

It helps you quickly memorize the key trends in the data

Step 3: Stating the implications

Concisely explain how the insights from the data  related to the issue tree – do they confirm or reject the current hypothesis? Do they open new areas for investigations?

This part is extremely important because it connects to the action-oriented mindset of actual management consultants while laying solid foundations for your next steps (fact-based).

Example – Handling revenue data

Suppose you’re working on a profitability case (how to fix low profits), and you’re trying to dictate whether the root cause comes from the revenue side.

The interviewer gives you this data:

How would you respond? Try to answer it yourself before revealing the sample answer.

Sample Script - Receiving Data 

Thank you for the very interesting data. (acknowledging)

It seems that our client’s revenue has been increasing steadily throughout four years – around the mark of 20% annual growth, in fact. (describe the data)

This suggests that the problem may not come from this side of the issue tree. However, in order to fully reject the possibility, I need the figures on the revenue of other companies in this industry around this time. Do we have those numbers? (implications)

Delivering the perfect closing pitch

“You have one minute to summarize all of your findings to the client CEO. What would you say?”

Your answer must be short, to-the-point, action-oriented, and client-friendly.

The closing pitch of the case interview is sometimes called the “elevator pitch” , where you supposedly meet the client CEO inside the elevator and must somehow deliver the results of the project before the elevator arrives at its destination floor (it’s even worded like that sometimes).

Regardless of the wording, the principles remain the same, and your closing pitch must consist of these 4 parts:

1. Introduction / Lead-in

2. Summary of the root causes

3. Summary of the solutions

4. Next step

Part 1: Introduction / Lead-in

Open your pitch in a client-friendly way. Remember, consulting is a service – a premium one, in fact.

There is a simple formula for this part of the pitch:

“Mr. CEO, it has been a great pleasure to be working with you on your company’s X problem.”

Everybody loves a little compliment, don’t they?

Part 2: Summary of the root causes

Don’t go into detail about your analysis – show them the results first.

CEOs are busy people, they have no time for a 15-minute break-down of your issue tree. They only care about the “big picture” – “Why is the problem happening?”.

You need to sum up root causes in a structured manner, with a numbered list – in the case interview context, that’s one characteristic the interviewer looks for, and in real projects, it helps the listener follow your pitch.

“After careful analysis, we have found X root causes for the company’s problem: 1… 2… 3… X”.

Part 3: Summary of the solutions

The solutions are what the clients pay for in the first place, so make sure to deliver them clearly and systematically.

This step must also be structured. Additionally, list the solution in the same order as their corresponding root causes, to imply the connection between them (if the root causes are listed as A, B, C, then the solutions should never be C, B, A).

“To solve the aforementioned issues, we propose the X following solutions: 1… 2… 3… X”.

Part 4: Next step

The ending must lead the customer towards a follow-up project, in a client-friendly way.

This step shows that you have an action-oriented mindset and necessary people skills to represent the firm before the clients.

Moreover, follow-up implementation projects are a major source of revenue for the top consulting firms (such as McKinsey, BCG or Bain), so mentioning them in your case interview ending pitch proves that you did the appropriate research before applying.

So here’s what you’ll say when the elevator reaches the destination:

“We would be more than happy to work with you to implement these solutions”.

Develop personal interview scripts

Every tip I’ve mentioned in the previous 7 chapters is for recurring situations in case interviews, and they can be dealt with using formulaic responses.

What that means for you – the candidate – is that you can make personal scripts and learn them by heart until they all become your second nature. That will save you a lot of brainpower to use on the issue tree. This approach has proven successful with all of my coachees, and it’s also a major part of our Case Interview End-to-End Secrets Program. You will find my own personal script I used back in the day, and I will also personally give feedback to scripts of members of the program.

So open your document tool and start writing now. Once you’ve finished the scripts, learn them by heart one at a time. When you feel comfortable with every one of them, you can move up to a higher level and practice with whole cases.

Inside the case interviewer’s mind – Consulting culture

The best way to impress your consulting interviewer is to act like a consultant. And to do that, you need to know what goes on inside their mind – both the conscious and unconscious – then install it into your own personality.

In this chapter, I’ll guide you through 15 ingredients that make up a consulting mind. However, I won’t tell you how to apply this in case interviews because it will sound fake – what you need is to immerse yourself in a consulting environment, and incorporate these “ingredients” into your own mindset.

case study data preparation

Responsibility & proactivity

Everyone talks about responsibility and proactivity these days, but in management consulting, we have a much more powerful word – “ownership” . When you “own” the work, you deeply and sincerely care about it, and you always try to go beyond what is required.

If you ever spend your efforts trying to improve a piece of work that your boss already approved, just because you know it is the right thing to do, because you feel so good seeing a job well-done, you have that “ownership” mindset.

In management consulting, you are expected to possess that mindset. In my early days at McKinsey, I was almost thrown out the window for working on a cement project but not knowing where the aggregate mines were (which was outside my responsibilities, but my boss expected me to know it, since I “owned” that cement project).

If you fail to do your work, don’t ever blame anyone or anything. Your responsibility is to draw up contingency plans for the “worst-case scenarios”:

  • Missing the deadline because the client did not send you the data? You should have accounted for it in your schedule. 
  • Late for work because of a traffic jam? Why didn’t you get up earlier?
  • Your pet bite your suit? Any sensible person should have a spare one; even if that one is bitten, aren’t we paying you enough to get a new suit at the store this morning?

In short, if you want to be a consultant,  don’t make excuses.

Result-oriented / Can-do attitude

“There’s nothing I can’t do” – that’s the mindset you need to work in management consulting.

The result orientation inside a consulting firm is intense – saying that it’s “Mission Impossible” everyday would not be an exaggeration, but at the end of the day it’s always “Mission Accomplished”.

The boss doesn’t pay much attention to how you do a task, or what resources it takes, as long as you get it done. The firm has enough resources of every kind to help you with that, so there’s no reason you can’t pull it off.

Top-down communication

Communications made by consultants are always short, concise, to-the-point, action-oriented, and structured.

We were all given full-on lectures by our parents back when we were kids, for wasting food or not exercising (or not studying, for Asians like me). If they were management consultants, most of those lectures would be replaced with powerful, action-oriented messages: “Go study. If you don’t get an A+ for the next test, I’ll have to discipline you”.

A consultant seeing something non-MECE is like your mom seeing your messy bedroom. It’s that discomforting.

If you wish to be a consultant, train yourself to be MECE in everything you do. Once you can be MECE effortlessly, and you start spotting the annoying non-MECE-ness in everything around you, you know you’ve got it. 

case study data preparation

If you’re unstructured, you won’t get into the business.

Being “structured” is a pretty vague concept, but everyone in the consulting industry knows when they see it. It’s about being organized, logical, top-down, MECE, etc.. Basically, if you can approach things the same way as real consultants, you will be deemed “structured”

If you can’t meet the deadline, you’re dead (of course, not literally).

A consulting firm works like the perfect machine, where every part operates as intended. When consultants promise to help you with something, you can be nearly 100% sure that they’ll keep their word. This makes work management that much easier.

Consequently, if you start missing the deadlines, you’ll be out of the game soon enough.

Manager from Day 1

You’ll get the idea right away if you watched this video on the job of management consultants:

In short, even as an entry-level associate, you’ll be managing a multitude of resources (experts, specialists, etc.), contents (reports, client data, expert knowledge,…), and stakeholders (the two most important being your client and your boss).

Pulling all of these together to create impactful results would be an impressive feat, even for the best and brightest new hires.

Client first

Don’t. Ever. Piss off. The client.

Management consulting is a special service industry – besides the usual “don’t disrespect the client” and “don’t leave a bad image of the firm”, there’s also “don’t make them hate you while telling them to do what they probably hate.” (which is a good way to sum up a consultant’s job).

In case interviews and PEIs, the interviewer will be asking himself a big question: “Can I trust this guy to represent me and my firm before the client?” – if the answer is anything below a stellar impression, you won’t be receiving an offer.

Consultants will have valid reasons for everything they do.

In both consulting work and case interviews, you need to be very explicit about the basis of your actions – every conclusion must have backing data, every idea must be explained, and every request must serve a purpose. Don’t ever assume that you’re justified.

Being fact-based is part of the foundation for the trust people place in consulting firms, so people who draw ideas out of thin air and act impulsively will never get into the industry.

case study data preparation

Effective time & resources management

Every consultant works hard, so the only way to stand out is to work smart.

Yes, I know it’s a buzzword, and I know it’s cliched, but the 80-20 rule really does apply in this line of work. The best performers are always the ones to identify the most important lever and focus on it.

With the intense workload and up-or-out policy at major consulting firms, this skill is vital. Don’t be surprised if you pull all-nighters and work hard all the time but still get fired, while that one guy who goes home at 5 gets promoted. If you want to survive, learn from him.

Key takeaways & key messages

To a management consultant, everything has a key takeaway.

Consultants are efficient people, they don’t simply waste time, effort, and resources on irrelevant things. Things are only worthy of their attention if they have an interesting, helpful “so what”:

  • You tell a story? So what?
  • You perform a data analysis? So what are your key insights, and what’s the implication?
  • You draw a slide? What’s the key message you’re trying to deliver?

If you already think like this, trust me, the interviewer will love you.

Think on your feet first

You should only ask for leadership assistance only  after you’ve thought well about the problem.

Just pause for a second and think: would you be more ready to help someone who really tries their best at the job or someone who does nothing and relies solely on you?

The same thing is true in consulting work, and even in case interviews: the interviewer will assist you if you can deliver well-informed opinions.

With that said, “asking without thinking first” is a very common mistake in case interviews, which you can see in the numerous examples from our End-to-End Secrets Program. 

Align early, align often

Always try to reach and maintain a consensus with co-workers and your boss, from the most mundane tasks to the largest projects.

Nobody wants to spend a whole week building a model that the team doesn’t need; it’s a huge waste of time and resources. As such, consultants have this aligning habit very early and often – a little time spent on reaching an agreement now will save a lot of trouble later.

Remember to align in case interviews as well – at the start of the case, and every important step.

Consultants are very action-oriented people who always think about the next step.

Every meeting, phone call, even random catch-up must end with everybody being explicitly and absolutely clear about what to do next.

So what’s YOUR next step, after reading this guidebook?

Scoring in the McKinsey PSG/Digital Assessment

The scoring mechanism in the McKinsey Digital Assessment

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Six types of charts in case interview are: Bar/Column chart, Line chart, Percentage chart, Mekko chart, Scatter plot chart, Waterfall chart.

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There are 9 type of questions that mostly used in actual case interviews. Each type has a different solution, but you can rely on the a 4-step guide to answer

A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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