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ASKING THE RIGHT QUESTION AS A DATA ANALYST

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To solve a business problem, data analysts must ask the right questions. Asking effective questions leads to getting the right insights from your analysis. Asking the right questions is the first step in the data analysis process. As data analysts, we ask a lot of questions. However, many beginner data analysts do not pay attention to this important step.

The more questions you ask, the more you will learn about your data and generate more powerful insights from your analysis.

It is good to ask questions, but some questions are effective while some are ineffective. The ability to ask the right questions is a skill set all data analysts must have.


How to Ask Effective Questions

Effective questions follow the SMART model. Specific, Measurable, Action-oriented, Relevant and Time-bound. 

Specific questions are simple and focus on the most significant thing. Asking specific questions means you will get only the answers that are significant to the project. This will help you avoid being overwhelmed with information that is not significant to your analysis and may. If somebody asks you, do you like sports? Which sport are they talking about? Football, volleyball, athletics or basketball? A more specific question could be: do you prefer soccer or basketball?


Measurable questions return answers that can be assessed and quantified. Unmeasurable questions are ineffective and may not give the data you need to derive the necessary insights. Instead of asking unmeasurable questions like, why is our team’s goal difference negative? You could ask, how many goals have we scored against how many were scored? This question is measurable because it will enable us to know the counts of goals scored and goals conceded. Knowing these figures will enable the analyst to arrive at a workable conclusion.


Action-oriented questions encourage change. These questions focus on what could be done to achieve a targeted outcome. So instead of asking how can our opponents stop scoring? You could ask, what formations and tactics can we adopt to stop the team from conceding too many goals. 


Relevant questions are questions that matter and are significant to the outcome of your analysis. This is why it is important to first understand the business context before starting a project. If we are looking at how the team can win a league title next season, it is irrelevant to ask, why did team B get relegated last season? This question will not help us win the league. Instead, a more relevant question can be, what tactics did the league winners in the last 5 years adopt? This can help identify where the team needs improvement to be able to win the title.


Time-bound questions specify the time to be studied for analysis. In the last example, the time to be studied was 5 years. This narrows your work to only the data within the time significant to your analysis.


Always remember to make sure your questions are specific, measurable, action-oriented, relevant and time-bound.

As much as possible, avoid asking close ended questions such as, did you enjoy the meal? This question will give a simple Yes or No answer. A better way to ask this may be, on a scale of 1- 5, how would you rate our meal? This will give answers that can be quantified to derive insights.

Remember, asking the right questions is the foundation to an impactful data analysis project.


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