In a previous post, Getting Better Data Analytics Questions, I stress the importance of asking compelling questions when serving as a member on a data science team. After all, questions are the impetus for exploration and discovery. In that post and a subsequent post, Three Places to Look for Data Analytics Questions, I recommend several techniques initiating question sessions.
However, the techniques I recommend aren't helpful unless you and others on your data science team are comfortable asking questions. In this post, I present four common reasons that data science team members may be uncomfortable asking questions. Simply by recognizing the common barriers to asking questions, you are better equipped to overcome those barriers on your own.
Asking questions may be very uncomfortable, especially when you're asking someone who's in a position of authority and especially when the person you're asking has an intimidating presence. After all, your question may be perceived as being dumb or as challenging or threatening the other person. No doubt about it — some people have even been fired over asking very good questions.
As a result, many employees, even those who serve on a data science team, may be reluctant to ask compelling questions. They have a natural desire to protect themselves. Nobody wants to seem dumb, wrong, or confrontational.
Overcoming this barrier requires working up the courage to ask compelling questions. Sometimes, you just need to do it — force yourself. If you can't work up the courage, try the opposite tactic — fear. Remind yourself that your job is to ask good questions. If you don't ask, you're not doing your job. And if you don't do your job, your team will fail, and you'll all end up in the unemployment line.
The good news is that over time and with lots of practice, asking tough questions becomes second-nature. When you begin to see that asking questions isn't a threat, and you begin to reap the benefits of asking good questions, any fear you may have had quickly disappears.
Some data science teams just don't have enough time and energy to ask compelling questions. Asking questions is hard work; it's exhausting, especially when you're just getting started on a project. It might seem as though each question meeting gets longer. Instead of feeling as though you're making progress toward an answer or solution, you may feel as though you're getting further and further from it. At this point, the team can quickly become discouraged and stop asking.
Many data science teams fall into this trap, and as soon as they stop asking questions, they turn their attention to routine work, such as capturing and cleaning data or implementing new data analytics and visualization tools.
Often, the rest of the organization celebrates this shift from what's perceived as esoteric to more practical endeavors — real work. Many organizations prefer a busy team over an effective one. When this happens, everyone gets so focused on rowing that no one takes the time to question where the ship is headed and why.
Remember that there is no prize for the most data, the cleanest data set, or the best data analytics and visualizations. Prizes are given out for delivering insights and creating business value. You can't do that unless you spend quality time coming up with compelling and relevant questions.
Some data science teams struggle to ask questions simply because they have little experience doing so. This is especially prevalent when team members are engineers, software developers, or project managers — people who have built their careers on answering questions and solving problems. These people want to do, not ask. Team members who come from science or academia tend to have an easier time making the transition.
Nothing is wrong with answers and solutions. In fact, a data science team often needs its members to propose answers and solutions, so those can be tested. However, during question sessions, the team needs to find a way to transform some statements into questions. For example, a team member who is unaccustomed to asking questions may say something like, "I see that more women than men are buying running shoes on our website. Maybe it's because our marketing department caters mostly to women.” The team could easily convert those statements into a question: "Why do more women than men buy running shoes on our website?"
Remember: statements don't spark discussion. Usually, the only option is for the other person to agree or disagree. With a question, the team can begin to consider a range of possibilities and discuss the data it needs to examine for answers.
A Corporate Culture That Stifles Questions
Some data science teams are stifled by a corporate culture that discourages employees from asking questions. In his book The Magic of Dialogue: Transforming Conflict into Cooperation, social scientist Daniel Yankelovich points out that most organizations in the U.S. have a culture of action. When they encounter a problem, their first instinct is to fix what's broken. Asking questions impedes progress.
Quick, decisive action is often needed in organizations, but it's counterproductive in data science, where the focus is on learning and innovation. One thing you don’t want to see the data science team doing is getting wrapped up in routine work to accomplish something practical. You don’t want the research lead saying something like, “You can ask questions once you finish uploading all the data to the cluster.” The team shouldn't be focused on completing projects but on coming up with new insights.
When you’re working on a data science team, watch out for an individual or organizational bias against questions. Questioning is one of the first steps toward discovery. If you skip this step, your team, and the organization overall, will have trouble learning anything new.
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