Data is not black and white. Two people with access to the same data can uncover completely different insights. This is because so much depends on how one approaches the specific problem at hand – the questions you ask will make all the difference.
Contrary to common beliefs, true insights are not gained from answers. We don’t actually learn anything unless we truly question it. Most schools are based on a paradigm where students are taught to answer questions from their teachers, but aren’t taught how to ask questions themselves. The outcome is that they only learn facts.
There are two things wrong with this approach. First, we live in a world where facts and information are in abundance and immediately accessible at our fingertips. However, some of this information is either missing critical context or is not accurate. If we do not have the skills to question the information, we automatically believe it is true when often they are not.
At the same time, it is easy to find a piece of information that will suit our hypotheses. This is also known as confirmation bias, the tendency of selecting and favoring information that will confirm our beliefs. This is exactly what we are seeing play out, and with huge volumes of misinformation, treating what you see as truth without questioning it can be dangerous
Equally important is the pace of the world, which is continuously evolving. The lifespan of facts and information is shorter than it’s ever been. What’s more, we don’t really know what information we will need in the future. That is why asking the right questions of the data and information presented to us is so important. Even within the professional workforce, it can be difficult to apply and tech the art of questioning.
What can we do to ensure we ask the right questions when looking to gain insights from data?
Most people work with data backwards. They begin with the data they have available, then use tools and techniques to come up with insights. The problem with this is they end up using very simple, closed and leading questions, which then lead to uninteresting insights. When you build a house, you don’t start work before thinking about the specification of the house and developing a blueprint.
Starting with the data, without doing preliminary questioning and thinking, will not give you valuable insights. It Is important to reflect on the problem and what you are trying to solve to come up with questions that will help you filter the data and arrive at useful insights.
Ideally, organizations have already established a measurement framework with the proper objectives and KPIs before they even look at the data. If they haven't, they won't be able to ask the right questions, as they will be too focused on metrics, which may be irrelevant or not important to the situation or the business.
Adequate questioning to achieve the best insights requires the ability not just to question the data, but also the assumptions and other information (i.e., context) related to it. As mentioned before, confirmation bias can play a role and we can favor information that is convenient to us. We should try to fight this bias by being aware of it and looking up information that will break down our assumptions. Data can provide us different insights when we have different assumptions.
Asking open-ended questions is a great way to make hidden assumptions visible. This is akin to how children are asked to show their working when solving math problems to demonstrate the understanding behind the approach, which can reveal assumptions that impact the insights.
There are multiple questioning frameworks available on the internet that help with asking the right questions. One in particular is introduced by Max Shron in his book “Thinking With Data.” In his framework, he outlines the importance of starting by asking questions related to context, before asking others related to the need – including what the data will provide that wasn’t available before and why that is important.
He suggests that questions related to the vision should follow – particularly those pertaining what the results should look like. Finally, Shron recommends asking questions focusing on the desired outcome, to frame what success looks like and how the insights should be used. Although there are plenty of wrong answers, there are not nearly as many wrong questions. Be inquisitive, approach problems with a 360-view in mind, and continually ask why, what, who and how. Simply producing something by rote or formulaic command won’t get you to the insight you need, so embrace the lost art of questioning.
To learn more about how you can help you improve your questioning, as well as other data literacy skills, visit Qlik’s Continuous Classroom.