When looking for information, it’s easy to get bogged down in the details. We all know this from personal experience: we start reading one article, then another, then another, and before long, our eyes begin to glaze over, and we lose track of what we were reading in the first place. That’s why the art of questioning is so critical!
Imagine the same from the perspective of trying to find critical information on your overloaded static dashboard. If you don’t understand the need for questioning, I assure you will be lost in the maze of all that information thrown at you. Hence, it’s even more important to look at questioning from the data perspective.
Questioning is a scientific method to find answers. And it’s also a way to think about problems, solve them and learn more about things. Questioning allows you to consider your experience open-mindedly and ask whether it makes sense—in other words: “What if?”
Questioning Helps Uncover Additional information in Data Analytics
The master key of knowledge is, indeed, a persistent and frequent questioning.
When you question, you’re not just looking for answers. You’re also trying to get more information and understanding. This is why questioning helps uncover additional information. Subconsciously the human mind also approaches data analytics from a similar perspective. Let me try to demonstrate the same with two simple examples.
Imagine that you’re a recent dog owner. Getting your dog trained with the most basic tricks is a big issue. Anyways you’re walking down the road and meet your friend who had been a long-time dog owner. After exchanging your usual pleasantries, your immediate question to your friend will be,
"Hey, how did you train Bruno to obey your commands?".
After he gives the answer, there will be a follow-up question based on the answer. Another way you would approach the same would be by asking a similar question to Google and ending up on a WikiHow page. You collect information in a chronological manner using the answers thrown at you. Hence it’s critical that you frame your questions in the right manner.
In the second example, imagine a similar scenario at your office. Assume you’re an HR leader in a Fortune 500 company. You are figuring out what is happening with the attrition rate amongst your younger Gen-Z crowd. You start with the basic questions,
"How many employees between the age of 22-25 joined my organization in the last 6 months?" followed up by "How many quit in the first three months?" and "What were the top three reasons for their resignation?" etc.
Questioning helps uncover additional information in a chronological fashion, and the field of data analytics is no different in nature.
The 4 Types of Questions Anybody can Ask their Data
Data analytics is critical to any business’s success and an ever-growing field. In fact, data analytics is a big reason why businesses have become more successful over time. Data analytics helps you discover more information about your customers, products, services, or any arm of your business by looking at large amounts of data in an organized way. It enables you to find patterns and trends within your data set to decide the next action better. Even from a data analytics perspective, there are only 4 recurring types of questions anybody can ask their data based on the intent and scope of the information one seeks.
As the name suggests, Clarifying questions are to help us understand better what has been stated or what one already might know. Clarifying questions dispel the said doubt by removing ambiguities with pointed questions. A simple instance from the earlier example in the perspective of a clarifying question would be,
“What are the top 3 demographics leaving my organization in the last 3 months?”.
As an HR leader, you know that Gen-Z is the one leaving the most, but this question can still show additional insights as to whether other demographics, like the millennials, are also following the trend. It may sound basic. However, it is important because, generally, humans tend to assume certain facts. “Assumptions” are the biggest roadblocks when it comes to accessing information.
Adjoining questions help us explore additional insights related to the information we seek. They’re important as it helps in drawing comparisons with a similar situation even though they’re generally ignored by the populace. Continuing the example, the HR leader can ask,
“Is the rising attrition among Gen-Z employees unique to my organization?” or “How many other Fortune 500 companies are affected by the same issue of mass Gen-Z resignation?”.
Adjoining questions broaden one’s perspective and further the understanding of the problem at hand.
The third type of question, funneling questions, help us “funnel” down further or dive deeper into the problem at hand. It helps in understanding how a specific answer was derived or what is the root cause of the problem.
“What is the most cited reason for my Gen-Z to quit?” and “Show me the trend of attrition for the first quarter of 2022 along with the reasons” are simple examples of funneling questions.
It tries to address the problem’s specific issues and helps one take corrective action if necessary.
Elevating questions are the fourth and final type of questions that help the information seeker take a step back and look at the problem from a bird’s eye viewpoint. They sometimes lead to changes in corporate strategy. Taking a cue from the previous example, an elevating question can be something like
“How about we look at changing our training practice to ensure better career growth for Gen-Z?” or “Should we look at introducing a more challenging work environment for the Gen-Z?”.
Questioning is the process of seeking answers. Questioning helps you uncover additional information about a subject and get to the bottom of things. It helps you find out why something happened, who did it, how it happened, and where they went afterward—all in one fell swoop!
Questioning helps one form a complete picture by connecting the dots.
We believe the first step in data analytics is questioning. The questions posed to your data must be meaningful and strive to uncover the next bit of insight for the information you seek. Most importantly, they should inspire the right action from one to solve the business problem at hand. Interested to know more about questioning? We encourage you to read our next blog, where we deep dive into the art and science of questioning.