Join Doug Rose for an in-depth discussion in this video Harness the power of questions, part of Learning Data Science: Ask Great Questions.
- Imagine that you're giving a presentation to a group of coworkers. You've come up with a way to increase your company's sales. This new strategy took weeks to prepare. In the middle of your presentation, someone interrupts you to ask a question. They question your assumptions. They want to know how you come up with your results. How would you react? In most organizations, this is seen as confrontational. It could even be seen as combative. Usually these types of questions come from skeptical supervisors, or from someone who disagrees. Either way it's outside the normal rhythm of a presentation.
If a person does this too often, they could find themselves permanently uninvited from any future meetings. Most organizations still focus on getting things done. They have mission statements. They encourage teams to deliver. Everyone works with clearly defined goals and aggressive timelines. It's difficult to imagine a meeting where everyone asks interesting questions. In many organizations, there simply isn't any time or place to encourage this type of questioning. In Sidney Finkelstein's book, Why Smart Executive Fail, he points out that many executives accept good news without any question.
They save their questions for bad news or if they disagree. Questions are viewed as a type of disagreement. This can be a real challenge for organizations. When there aren't good questions, people usually repeat the same mistakes. They're prone to groupthink and have several blind spots. Several public failures are traced back to crucial questions that were never asked. It's important for your data science team to exist outside of this reality. Your team needs to create an environment that's open to interesting questions.
The rest of your organization may live in a world of statements. Your team needs to be comfortable in a world of uncertainty, arguments, questions, and reasoning. When you think about it, data science already gives you a lot of the information. You'll have the reports that show buying trends. There will be terabytes of data on product ratings. Your team needs to take this information and ask the interesting questions that creates valuable insights. It's important to create a setting where everyone feels comfortable questioning each other's ideas.
A good question will challenge your thinking. It's not easily dismissed or ignored. It forces you to unravel what you already neatly understood. It requires a lot more work than just listening passively. There's a few things to remember to keep your data science team on track. The first is that it's very unlikely that you're good at asking the right questions. It's probably because you haven't had much practice. When you were at school, your teachers probably quickly moved through material. Most schools still require that you memorize facts and read through expert advice.
When you raised your hand it was probably for a pretty narrow question. You may not have had many classmates that asked bolder questions, such as why are we learning this subject? Or even, can we learn something different? At work, you also probably don't have many opportunities to ask interesting questions. Most companies still promote employees based on their ability to follow through with a corporate vision. You need to work well with your coworkers. Always asking questions isn't the best way to get along. The second thing to remember is that asking questions is really hard work.
Most people still prefer to make simple statements. It's pretty easy to tell the world what you think. It's not so easy to defend what you think from someone who can ask good questions. Think about some of the things that you may do because it's considered healthy. Maybe you eat certain foods or do certain exercises. Now ask yourself, how do you know it's healthy? Is it because someone told you? Maybe it's because of how you feel. If it's because someone told you, then how do you know they were right? Many experts disagree on what's healthy. Which experts are right? It doesn't take long to realize that responding to questions can be exhausting.
It takes a lot of work to deconstruct what you already believe to be true. Now imagine doing that in a group setting. Try to remember that asking good questions is difficult to do, and not always well received. Still, it's a crucial part to being part of a data science team. The best questions will give you new insights into your data. This is a key part of building your organizational knowledge.
- Harnessing the power of questions
- Testing your reasoning
- Identifying question types
- Organizing questions
- Rooting out assumptions
- Finding errors
- Highlighting missing data
- Overcoming question bias