Data is the lifeblood of consulting case interviews. You need to collect, analyze, and present data to support your problem-solving process and recommendations. But how can you find the best data for consulting case interviews? Here are some tips to help you navigate the data landscape.
The first step is to identify the sources of data that are relevant and reliable for your case. There are two main types of sources: primary and secondary. Primary sources are data that you collect directly from the client, such as interviews, surveys, or financial reports. Secondary sources are data that you obtain from external sources, such as market research, industry reports, or public databases. You should use a combination of both types of sources, but prioritize primary sources as they are more specific and accurate for your case.
I believe the best way has always been to communicate with the whole ecosystem rather than just a specific group of stakeholders to learn about more angles and have different perspectives. You first have to know what you have to collect or you have to know the research problem even before conudcting the research for which the stone has to be looked at from every angle. One of my teachers used to say look at the stone before dropping the hammer. There is always a spot where one blow shall open the stone. Of course, the sources then can be narrowed down from a pool of stakeholders to a few. But your initial touch with everyone in the circle gives you a direction and a lead towards the real data source.
The second step is to evaluate the quality and validity of your data. Ask yourself questions such as whether the data is recent and updated, consistent and comparable, relevant and representative, or credible and unbiased. Additionally, you should check the assumptions and methods behind the data, and be aware of any potential gaps or limitations. It's important to avoid using data that is outdated, inconsistent, irrelevant, or biased as it can have a negative impact on your analysis and conclusions.
Data has to be consistent, efficient as well as sufficient. Consistency menas the sample evaluation should be comparable to population evaluation. The data shall have minimum errors and shall be efficient. And of course it should be sufficient- having ample observations, in terms of analytics >30
The third step is to organize your data in a clear and logical way. You should use charts, tables, graphs, or other visual tools to display your data and highlight the key insights. You should also label your data and provide sources and references. You should follow the MECE principle, which stands for mutually exclusive, collectively exhaustive. This means that you should divide your data into distinct and non-overlapping categories, and cover all the relevant aspects of your case.
The fourth step is to interpret your data and draw meaningful conclusions. You should use frameworks, models, formulas, or hypotheses to guide your data analysis and problem-solving. You should also use benchmarks, ratios, trends, or scenarios to compare and contrast your data and identify patterns or anomalies. You should avoid jumping to conclusions or making unsupported claims. You should always explain how your data supports your logic and recommendations.
The fifth step is to communicate your data effectively and persuasively. You should use the pyramid principle, which means that you should start with your main message and then provide supporting evidence and details. You should also use the STAR method, which stands for situation, task, action, and result. This means that you should provide context, objectives, steps, and outcomes for your data analysis and recommendations. You should also use simple and concise language, and avoid jargon or technical terms.
The sixth step is to practice with data as much as possible. You should expose yourself to different types of data and sources, and practice collecting, evaluating, organizing, interpreting, and communicating data. You should also seek feedback and learn from your mistakes. You can use online resources, books, courses, or mentors to help you improve your data skills. You can also use mock cases, casebooks, or case partners to simulate real consulting case interviews and test your data proficiency.