How to form good data questions
How to form good data questions
Consider the following questions:
Consider the following questions:
BAD: How many customers churned (went dormant) last month?
GOOD: What percentage of our customers are expected to churn in the next few months and why?
BETTER: What short term impact can we have on our business by reducing customer churn and what options do we have to do that?
Needless to say, bad questions get bad answers. As business leaders we often approach our data science and analytic teams with very specific questions. In most cases asking specific questions gets us very specific answers, which alone is useless. In this case, the good and better questions are likely to generate some very useful and actionable analysis.
Good data/analytics questions seek to get three things out of the answers or analysis. These are:
Insight — Something you didn’t know before
Action — Asking for possible solutions
Time — In a time-frame that your actions can have an impact
What you want is information (not data)
INSIGHTFUL
The answer should provide some new thought or idea that was not apparent before. For example,being told about your organization’s market share that you didn’t know before or your customer life time value. To ask questions that generate insight you should almost never ask for absolute numbers. Instead, ask for ratios and percentages of how data points compare.
ACTIONABLE
An insight is still pretty useless if you can’t do much about it. If you were simply told what your market share was, that’s not so useful. However, if you were told of a market share shift because your competitor was introducing a new product at a lower price point, that would be actionable.
TIMELY
Ask questions that don’t generate a rear view mirror answer. While historic data can help generate trends, it is the ‘prediction’ that’s valuable. For example, instead of finding out your current market share, it might be better asking what is the predicted market share in the next few months. Another important aspect of timeliness is the reproduction and periodic rerun of the analysis so that it continues to provide insights. You want your analysts to automate this process as well so that they are not spinning their wheels each week or month doing the same thing.
Originally published at ehasan.com.