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Three Places to Look for Better Data Questions

Published August 28, 2017
Doug Rose
Author | Agility | Artificial Intelligence | Data Ethics

In a previous post, "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 better data 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:

  • Clarify the definitions of terms
  • Identify "facts" that are really assumptions
  • Reveal errors in reasoning

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:

  • Acceptable or adequate
  • Agile
  • Better, faster, bigger, etc.
  • Happy
  • Normally
  • Reasonable
  • Satisfied
  • Sufficient
  • Often, frequently, or rarely

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:

  1. They are often hidden or unstated. Very few people start sentences by saying, “If we assume this to be true, this other thing must be right.” They simply make the assumption and present it as a fact.
  2. Assumptions are usually taken for granted or accepted as “common sense.”
  3. They’re essential in determining your reasoning or conclusion. Your reasoning might even depend on the assumption.
  4. They can be deceptive. Often, flawed reasoning is hidden by a common sense assumption. Something like, “Sugar is unhealthy, so artificial sweeteners must be healthy.”

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):

  • Ad hominem: Attacking the person who made the claim instead of the claim itself. For example, a team member dismisses a statement on the basis that the person who made it lacked the expertise or credentials to be believed.
  • Ad populum: Accepting a claim as fact simply because it is a popular belief. In other words, if everyone agrees, then it must be right.
  • Appeal to authority: Assuming a claim is true because an authority on the topic says it's true, without providing other evidence to support the claim.
  • Appeal to ignorance: Assuming a claim is true because no evidence suggests it is untrue. For example, "Nobody has actually proven that God exists, so God does not exist" is a fallacy. So too is "You can't prove that God doesn't exist, so God does exist."
  • Question dismissal: Avoiding a question because it may result in an uncomfortable situation. For example, a team member shelves a question because he or she thinks that it might reveal an issue that makes the organization's leadership uncomfortable.
  • Circular reasoning: Using the conclusion of an argument to support the premise on which it is based; for example, “We are a data-driven company so our data must be correct.”
  • Straw man argument: Distorting someone else's position in order to weaken it and then attacking that position instead of what the person truly believes and claiming victory. For example, stating "If you accept Bill’s argument that the data is terrible, we have to start from scratch,” when Bill never said or meant "the data is terrible," is a straw man argument.
  • False dichotomy or either/or fallacy: Presenting an issue as though there are only two possibilities when there may be more; for example, “If the data is right, it means we’re all wrong.”

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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