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Converting Business Problems to Data Science Problems

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2nd Jul, 2018
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Converting Business Problems to Data Science Problems

In a lot of Data Science interviews, it is common to ask business-related questions. The interviewee is expected to solve the challenge faced by a business in an interview. For example, the profits earned by a newspaper company is dropping, what can be done to rescue the situation. How Reliance Jio can decide if it is beneficial to start its operations from a new location. How the launch of Baba Ramdev SIM will affect the business of Reliance Jio, etc.

It is common for Interviewees to prepare well for Data Science questions. They expect and prepare well for questions like, “How to impute missing values”, “How do you decide which algorithm is suitable for a dataset”. However, the candidates are completely baffled when they face business cases. A part of the reason is that the candidates are not expecting business case questions in an interview. Another reason being none of the data science blogs or courses does not touch upon how to convert business problems to data science problems. There are frameworks available for Data analysis but they are surprisingly quiet on how to convert the business to the data problems. For example, the CRISP-DM framework is very famous for Data analysis. The first and second steps of CRISP-DM are ‘Business understanding’ and ‘Data Understanding’. Most aspiring data scientist does not know how to proceed from the 1st to 2nd step. The aim of this article is to fill this gap. Victor Cheng’s book and videos helped me in understanding how to convert business problems to data problems. This article is based on his teachings.
Business Problems to Data Science Problems

Interviewer’s Mindset

Before diving-in how to answer case interviews, let us understand the interviewer’s mindset. What are the interviewers looking for when they ask business questions? Some of the things which interviewers are looking for are:-

  • Do the candidates possess independent thinking mindset?
  • Are the answers good enough if not precise?
  • Are the solutions offered by the candidate client friendly (In case of consulting companies)
  • Is the solution offered by the candidates linear?
  • Did the candidate explain the solution visually?
  • Is the candidate’s solution practical to implement?

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The candidate solution should proceed linearly and logically from a challenge to a solution. If the solution is scattered, jumping from one point to another arbitrarily then that interview is over immediately in the mind of the interviewer. Additionally, a right approach with a wrong answer is preferred over a right answer with a wrong approach. If the approach is wrong and the answer is correct, interviewers will assume that the candidate got lucky. This will not be the case in all situations. If the approach is correct then it is repeatable and can be applied to many business situations.
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Answering ‘Case interview’ questions

The answers to case interview questions can be divided into three stages. Open, analyze and close. Open and close are formulaic and can be answered easily with practice. Analyse stage differs according to the business problem and involves thinking and creativity. Let us see what to do in each stage of answering the question.
The steps for answering Case interview questions are:-

  • Stalling
  • Verify your understanding
  • Identify the structure of the problem
  • Analyse
  • Close

Stalling (Opening Stage)

Victor Cheng asks to take a pause of five seconds before telling something similar to, “Ah, this is a very interesting problem”. This is known as ‘stalling’. If you are waiting more than five seconds to answer, then the interviewer will think that you don’t know the solution. Stalling helps in getting some valuable time to think through the problem.

Verify your understanding

Usually ‘Case Interview’ questions are a one-liner. All the required information will not be provided to answer the question. The candidates are expected to ask questions and verify their understanding. As previously said if it is a case of increasing the newspaper profits, then you may ask questions like ‘what topics does the newspaper cover?’, ‘What is the target audience of this newspaper’, etc. Clarify about any terminology the interviewer used and about which you are not sure. Here it is important that you do not assume anything. Assuming may lead to solving wrong problems which are not faced by the company. This exercise will also inform the interviewer about how good you are in seeking help when it is required.
During the initial phases of answering you will have the freedom to ask open-ended questions. As time progresses you will lose freedom and will only able to ask close-ended questions. Asking open-ended questions towards the end will lead to the interviewer thinking that you are trying to ask the answers. So, don’t hesitate and ask questions which you feel are valuable for answering.
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Identify the Structure

Once you get the required information after asking questions, identify the structure to which the question belongs. Victor specifies four different frameworks to which a case interview question can belong. The frameworks in their order of importance (according to the frequency asked in interviews) are:

  • Profit
  • Business situation
  • Merger & Acquisition
  • Supply / Demand

The examples of Business situation framework are – the launch of a New product, responding to the competitor behaviour, changes in demand, growth strategies for a company, etc. Examples of Supply / Demand problems are building a new factory or shutting down a factory, change in capacity through acquisition, change in demand, etc.

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These frameworks are not hard and fast. The profit problem may ultimately end up in a business situation or supply/demand problem. Nevertheless, this categorisation will provide a structure to our thinking and help us in moving forward with the challenge at hand.
Once you identify the problem structure and match it to the appropriate framework, the next step is to describe the key components of the framework. For example, in case of increasing the profits of a newspaper, you can talk about revenue and cost. Be careful and don’t name the framework explicitly. A framework is only for structuring your thinking and not for mentioning it to the interviewer. Draw the key components of the framework along with your description. As you practice these steps it will become second nature to you. The key points in identifying the structure are:

  • Identify the nature of the problem
  • Match the problem to the appropriate framework
  • Describe the key components of the framework
  • Draw

Once this is done proceed to the next step in the process which is analysing the problem.
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Here Analyse does not mean analysing the data. It is still far away. Data comes at the end. Before asking for data there are other things which need to be addressed.
Start your analysis by asking where to start. To improve the profitability of a newspaper ask if you should proceed with cost or revenue. Depending on the choice of the interviewer proceed with your answer.
Visualise the underlying problem as a decision tree and you start at the root. State the hypothesis and pick a branch according to the choice of the interviewer. Identify and state the key issues within a branch. Ask standard questions and keep drilling down. In this process keep refining your hypothesis. If you reach a dead end of a branch without any resolution, trace up to the node of the branch and traverse the opposite direction.
Think out loud during the whole process. This will help interviewers to know about your thinking and analytical skills. If there is any flaw in your thinking they may even point it out and help you in the right direction. It is always a good practice to think aloud about case interview questions.
Once you reach the leaf of your decision tree with a hypothesis or get to a point where the interviewers are not sure about the hypothesis, then you may go ahead and ask for data. Data which can help either accept or refute your hypothesis. Data which will help derive insights and make business decisions. A point to be remembered is, all the data requests should be backed by solid explanation. Asking for data without any explanation will not go well with the interviewers or the clients in case of consultancy.
This completes the conversion of business problems to data analysis problem. Once this is done, now you know how to proceed ahead with analysing the data. From here the standard CRISP-DM steps follows:

  • Understanding the data
  • Modelling the data
  • Validating the model
  • Model deployment
  • Updating the model and keeping it relevant

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Tips and Tricks to answering the Case Interview questions

  • Segment your numbers
  • Company Vs competitor
  • Current year Vs past year
  • Think aloud
  • Explain why you need data before asking for it
  • Don’t assume anything
  • Practice answering business problems
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Segment your numbers

Data science is all about breaking down the problem into its constituent parts. Analyse the parts and derive insights. Combine the parts together and offer recommendations backed by data analysis. This combination of parts is also known as ‘synthesis’ in the language of consultancy.
Better insights can be derived when data is segmented into parts. Let us assume that the newspaper profits are down due to losses in revenue. The loss in revenue as a whole does not offer many insights. If this revenue is segmented into different buckets based on the age of subscribers it may provide more valuable insights. A company can execute an action targeting a particular age group to improve the revenue. Given a business problem, always see how best to break it into different parts.
Given a business problem, think if it is faced only by a particular company or by the whole industry. The solution and recommendations will be different for both the cases. Another line of thinking is comparing between the past and present performance. Both these line of thoughts will provide a direction to move ahead in answering the case interview question.
Business Problems to Data Science Problems
Knowing about the steps and different frameworks will only take you up to a certain point. What will help you in successfully answering the case interview questions are, ‘Practice, Practice and more Practice’. This is no other way around it. The fluency in asking verifying questions, identifying the structure, matching the problem to a framework, formulating the hypothesis, traversing along the decision tree, etc. will only come by practice. It is impossible to crack a case interview question or convert business problem to data analysis problem without practice.
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To summarise, converting business problems to data science problem can be equated with a case interview question. To successfully answer case interview questions the steps to be followed are – stalling the interviewer, identify the structure of the problem, match it to the underlying framework, formulate a hypothesis, traverse the decision tree by asking relevant questions and finally ask for the data by explaining why you want it. Finally, to succeed in answering case interview questions, practice it. Once you are able to convert a business problem into a data science problem, follow the CRISP-DM framework to analyse the results and provide recommendations backed by data.

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Thulasiram is a veteran with 20 years of experience in production planning, supply chain management, quality assurance, Information Technology, and training. Trained in Data Analysis from IIIT Bangalore and UpGrad, he is passionate about education and operations and ardent about applying data analytic techniques to improve operational efficiency and effectiveness. Presently, working as Program Associate for Data Analysis at UpGrad.

Frequently Asked Questions (FAQs)

1Can a Business Problem be solved using Data Science?

The answer is Yes. These days most of the business problems are solved using Data Science and Analytics. Data Science uses 5 steps to approach and solve business problems

1. Understanding the business problem
2. Using analytics to solve business problems
3. Preparing Data
4. Model Development
5. Performance Testing

Data science and Analytics cannot magically fix all the problems of an organization. These are very useful tools which help companies to make accurate decisions, automate repetitive work and choices that teams need to make. Data science approaches can be used by companies to help their management make better decisions, anticipate future profits, and develop better content.

2How can a business problem be translated into AI and Data Science solution?

To translate a business challenge into an AI and Data Science solution, a data scientist must first comprehend the problem, as well as the goals and KPIs of data analysis, as well as the AI and Data Science approaches that can be utilised to solve the problem. A data scientist should also understand what the organisation expects to gain from the data analysis and how it plans to use the results.

3How to apply Data Science to solve actual business problems?

Nowadays, businesses invest in Data Science for various purposes. Almost all the different business sectors like finance, marketing, retail, manufacturing, etc. can leverage Data Science in different ways. But the one and only goal of every company to use Data Science is to solve business problems.

Data scientist use their skills in different ways to solve the business problems such as

1. Innovation – Data scientists develop newer ways to solve the business problems which have been existing in the company for a long period of time and couldn’t have been solved with previous approaches. In short, they replace the old solutions with the new ones.
2. Continuous Improvement – Data scientists make continuous improvements in the existing data science project to make it work better.
3. Exploring Data-Value : When companies just start using Data Science, they have a lot of data which is saved in an unorganised manner. Here a Data Scientist plays a vital role by extracting useful insights from all the available data and exploring it for potential opportunities.

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