The success of any data science initiative hinges on the team’s ability to ask interesting questions that are relevant to the organization’s success and the team’s 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 that have become accepted as facts can be a significant challenge, 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 and problems to investigate.
Conduct Question Meetings
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 person is 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 techniques 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, although you may want to assign one person in the meeting the task of 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.
After a question meeting, you should have plenty of questions — far more than you need and some far more valuable than others. Now it’s time to pan for gold — to identify the few questions you want your team to explore.
When evaluating questions, it often helps to categorize questions as open- or close-ended and then identify individual questions as essential or non-essential:
If you’re the research lead, make sure that the team is not asking too many of any one type of question. Too many open-ended questions can result in the team spending too much time wondering and not enough time exploring the data. Too many close-ended questions can result in too much time digging up facts and too little time looking at the big picture.
You can also categorize questions as essential and non-essential:
If you’re a fan of detective shows, you’ve probably seen a crime wall. That’s when a detective tries to figure out all the different pieces of an unsolved mystery. He or she puts up pictures and notes on a wall and tries to connect the different pieces. The board becomes a visual story. That’s why you’ll often see the detective sitting on the floor staring at the board trying to pull together the story from all the little mysteries in the data.
Your data science team will have a similar challenge. They’ll try to tell a story but they’ll only have pieces of the puzzle. Your team can use the same technique to create a question board—a place where they can see all the questions and data. That way they can tell a larger story.
Creating a question board is a great way to display ideas and solicit questions from your team and the rest of the organization. At the very top of the board, you should put a simple identifier such as “question board” or “ask a question.” The question board is a clear way to communicate and organize them in one place.
Your data science team should have dozens or even hundreds of different questions. The question board will likely be a key meeting point for the team as well as a great place for team members and stakeholders to talk about the project.
To start, place your question board next to someone’s desk on the team or in a hallway. Open spaces aren’t good for a question board. You’ll want people to stand next to the board and read the questions. Another suggestion is to put the board next to an area with a lot of traffic. Ideal places are next to the water cooler, snack bar, or bathroom. It should be a place where several team members can meet and not distract other people.
Usually, the best way to organize your board is to use different color sticky notes. You’ll want to organize your board from top to bottom. The sticky notes at the top of the board contain your essential questions. Use red or pink sticky notes for these questions. Below them, you can use yellow sticky notes for nonessential questions. Remember that these are questions that address smaller issues. They are usually closed questions with a correct answer. Finally, you can use white or purple sticky notes for results. These are little data points that the team discovered that might help address the question.
There are five major benefits to having a question board:
Remember that you want your team to have deep discussions. Everyone should be able to question each other’s reasoning. The team should listen to each other’s questions and try to come up with questions of their own. They should be focused on learning and not judging the quality of their questions.
The question board helps with this because it provides a place for people to focus their discussions. It also helps the team stand up and participate physically and come up with new ideas.
Many of your questions will be interconnected. Often, you’ll have essential questions that are connected to several closed, nonessential questions. If it’s on the wall, you can use string to show these connections. If it’s on a whiteboard, you can just draw different colored lines. This will help your team stay organized and even prioritize their highest value questions.
The question board will invite other people outside your team to participate. You might want to leave a stack of green sticky notes next to the board. Leave a marker and a small note that invites other people to add their own questions. Sometimes these questions from outside the team tell the most interesting stories.
Create Question Trees
Your question board will be a key part of communicating your data science story. It should have the questions that your team is working to address. It may also have little bits of data that suggest some answers. A good question board encourages other people to participate and tempts people to be part of your shared story.
One of the challenges of a question board is to have it filled with questions and keeping it well organized. Since it’s designed for a group discussion, you want everyone to be able to share the same information. It shouldn’t have several different groups of one person’s notes. If each group only has one person’s ideas, that one person will be the only one to understand its meaning.
Instead, all your questions should be organized using the same system. One of the best ways to do this is by creating question trees. A question tree is a group of sticky notes all related to one essential question. You’ll want to have the essential questions as the most attention grabbing color. Usually this is either red or pink.
Let’s imagine a question board for our running shoe website. One question that your team came up with is, “Can our website help encourage non-runners become runners?” If you’re the research lead for the team, you want to put this essential question on a red sticky at the very top of the board.
Underneath that essential question, you can start adding other questions. It could be another essential question such as, “What makes people run?” It could also be a nonessential question like, “Do non-runners shop on our site?” Since this is a closed question, you could put a little data sticky next to the yellow question sticky. Maybe something like, “Data suggest that 65% of our customers don’t run in a given week.” You could use a pie chart like the one shown below to illustrate this point.
Assume that this generated data comes from a survey that the company did on its customers. The question asked, “How many times, on average, do you run per week?” When you look at the data, you see that about 65% of the respondents don’t run at all. 55% of the respondents run more than once per week.
Someone looking at the question tree should be able to follow the thought process of the team. She should see that the lower branches of questions started with one open-ended essential question (“Can our website help encourage non-runners become runners?”) and see the team addressing that question. She should be able to follow it all the way down to different branches.
Let’s say that the question, “What makes people run?”, branches off in its own direction. Underneath that question is another question that says, “Do they run to relieve stress?” Underneath that is another question that says, “Can non-runners who are stressed see the benefits of running?”’
With the question tree, the research lead now has a report to show progress to the rest of the organization. She could show that the data science team is working on several high-value questions simultaneously. It shouldn’t be too difficult to see how gaining insight into creating customers might increase revenue.
The question trees help the research lead connect the team’s work to real business value. A question board should have several questions trees. At the very top of the board, there should be several red or pink essential questions. Each of these should branch down like an upside down tree into several other questions. Be sure to use different color sticky notes as discussed previously (essential questions red or pink and nonessential questions yellow). Sometimes open questions will branch off into different question trees and you should end closed questions with little sticky notes that show the data.
Like any tree you’re going to want to prune your questions. This is one of the key responsibilities of the research lead. She needs to make sure that your questions lead to real business value. If he doesn’t think your questions will lead to insights, he might want to pull them off the question board so the data analyst doesn’t start searching for results.
Note: The research lead usually removes questions as part of the team’s question meetings. You don’t want your research lead pulling questions off the board without communicating the change to the team.
One of the key things about question trees is that they actually mirror how most teams come up with new questions. Remember that data science is using the scientific method to explore your data, which means that most of your data science will be empirical. Your team will ask a few questions, gather the data, and then they will react to that data and ask a series of questions. When you use a question tree, it reflects what the team has learned. At the same time, it shows the rest the organization your progress.