In a previous post, Getting Better Data Analytics Questions, I recommend a couple ways to get the get the ball rolling when it comes to getting people in your organization to start asking compelling questions. However, getting people to ask great questions is not always as simple as creating the right environment. Even a highly skilled data science team often needs more guidance.
To stimulate questions, it is often helpful to focus on specific areas that are fertile grounds for questions. In this post, I highlight three key areas that are not only the places you’ll find great questions, but also are a good place to start. These are questions that:
Note: These three areas are intended to initiate the process or get your team moving if it's stuck. Don't let these areas limit the scope of your exploration. If you address these three areas, you’re bound to come up with at least a few questions to grease the gears. When the team develops some momentum, team members will naturally ask more questions.
Clarify Key Terms
George Carlin once joked that he put a dollar in a change machine and nothing changed. Jokes like this are possible because many words in the English language have different meanings based on the context in which they're used and on different individual's understanding of the words. While jokes are funny, however, people often get into heated arguments when they don't have a shared understanding of what certain words or phrases mean. Just look at how different people define "success." For some, it's spending time with family, for others it's financial security, and for some knowledge or power.
The world of business is not immune to ambiguity inherent in certain terms; for example, ask two people to define "custom satisfaction." Does it simply mean that the person is a return customer? Is a customer who never complains satisfied? Can a customer who returns a product for a refund be satisfied? If a customer never buys another product, can we assume that customer was not satisfied?
Your data science team needs to be sensitive to ambiguous terms and nail down their intended meanings. Here's a short list of ambiguous terms commonly used in various organizations:
Identify "Facts" That Are Really Assumptions
People often accept assumptions as facts. A company's leadership, for example, may believe that the company has such a unique manufacturing process that nobody can compete with it on price or quality even when that's not true. The truth may be that some other company has yet to develop something better or that there is an entirely new product being developed somewhere that will make the company's existing product obsolete — leadership just doesn't know about it yet.
In general, assumptions have four characteristics:
Data science teams must remain on the lookout for false or questionable assumptions. Not all assumptions are bad. If the assumption reflects reality and facilitates positive or productive decisions and activity, it can be helpful. However, false assumptions can create blind spots and introduce misinformation into the decision-making process.
Reveal Errors in Reasoning
Data science teams need to be aware of the possibility of errors in data and errors in reasoning, which are even worse. A data error may result in a minor setback or a series of false reports. On the other hand, an error in reasoning can lead the team down the wrong path or result in completely wrong conclusions. Watch out for the following types of logical fallacies(reasoning that results in invalid arguments):
All three of the techniques described in this post boil down to listening and observing closely and being skeptical about what you hear and observe. Whenever you encounter a statement presented as a fact, ask yourself, "Is this really true?" Whenever you encounter someone presenting a position, ask yourself, "Is the conclusion based on sound reasoning?" Questions like this force you to take a closer look and determine for yourself the truth and validity of a statement or conclusion.
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