Data science needs to start by asking good questions. Not just the data scientist, but the whole team.
The success of any data science initiative hinges on the team's ability to ask interesting data analytics questions that are relevant to the organization's success and its ability and willingness to challenge assumptions and beliefs. After all, without questions, you can have no answers. However, asking compelling questions and challenging long-held beliefs can be difficult, especially in organizations with strict hierarchies that discourage questioning and the challenging of authority.
If your data science team is struggling to come up with compelling questions and hesitates to challenge assumptions, the suggestions I present in this post can get the ball rolling. Getting started is the most difficult part. As soon as the team gets into the swing of asking questions and questioning beliefs, it will have no shortage of follow-up questions.
One of the best ways to encourage data science team members to ask questions and challenge beliefs is to build an environment that's conducive to the free exchange of ideas. The research lead is ultimately responsible and can start to nurture the free exchange of ideas by modeling the desired behavior — listening and learning without judging. Everyone on the team should engage in deep listening— focused listening that enables them to hear and understand what others are saying, ignoring any initial impulse to judge what they hear. Team members need to recognize that they have plenty of time later to analyze what they hear, but the first step is to fully understand what the other people are getting at.
A good way to encourage questions and reinforce deep listening is to conduct question meetings. In these meetings, the research lead should encourage participants to ask questions before making statements. This technique is sometimes called a "question first" approach. These meetings are about eliciting the maximum number of questions. They’re focused on everyone asking their questions and listening. Ban smartphones, laptops, and other electronic devices from these meetings. Everyone should focus on listening, with one person taking notes.
Although question meetings are mostly unstructured, consider starting the meeting like this:
Avoid quick statements that are likely to limit the scope of the discussion, such as "The CEO suspects that we are losing market share due to the recent reorganization of our marketing department." Such statements keep people from coming up with their best ideas. Remember that it’s the discussion that gives your team the greatest value. You want the team to consider all possibilities.
If you’re a fan of detective shows, you’ve probably seen a crime wall plastered with maps, photos, names, clues, sticky notes, and so on. The board functions as a combination collage, story board, and puzzle that provides the detective with a clear visualization of the evidence.
Your data science team can create its own "crime wall" by soliciting questions from across the organization through the use of a question board. Here are some suggestions for hosting an effective question board:
A question board delivers the following benefits:
Hosting question meetings and a question board are only two ways to encourage people in the organization to ask compelling questions. You are likely to come up with your own unique ideas. What's important is that you provide the encouragement and means for people to contribute their questions.
Data science needs to start by asking good questions. Not just the data scientist, but the whole team.
Watch out for data storytelling pitfalls with common mistakes when trying to communicate data stories and insights.
With machine learning you have to create a culture that can ask data science questions.