11 Ways to Ask Smarter Data Analytics Questions

Not everyone has been trained to think like a data scientist or a data analyst, but they can learn to think more like one.

It's been said that the quality of insights depends on data. However, the quality of insights also depends on the questions asked. While one of life's pearls of wisdom is, "If you want better answers, ask better questions," how does someone know what a better question is, let alone a better answer?

“When an experienced data scientist is working on a problem, they’re usually not looking to prove themselves right; they’re looking to prove themselves wrong," said David Robinson, principal data scientist at data science platform provider Heap. "They’re thinking, what if this data source is untrustworthy? Would this be different if I used the median rather than the mean? How could I tell if this conclusion is confounded by other variables? This habit of critical thinking helps you build trust not only with stakeholders, but also helps you trust your own conclusions so you can build on them further.”

A couple of factors to consider are context and the level of specificity that's appropriate. For example, 2020 involved many changing contexts including hoarding, supply chain issues, stay-at-home mandates, remote work and shifting consumer behavior that could have altered the way a question should have been articulated. Similarly, is the point to understand a general trend or a trend specific to a segment of a target market using a particular product? Bear in mind that different types of questions tend to require different types of data to produce appropriate answers.

"Looking at a problem from a variety of angles is a great way to approach an issue analytically," said Eric Blank, marketing analyst lead at marketing agency PACIFIC Digital Group. "Take into account multiple sources of information that play different roles in the space you’re analyzing. In doing so, the data will begin to tell a story and allow you to uncover where opportunities or issues exist.”

Data scientists and data analysts know they may not necessarily ask "the right" question and that's OK.

"Since this is an iterative process -- full of dead-ends -- the analysis is not always on the right track," said David Smith, VP of data & analytics at TheVentureCity, an international, operator-led investment organization. "But after a while, I start to understand the correlations between different variables, and which are the most important drivers of outcomes. Then it becomes easier to home in on the salient insights to glean from the data. It can also help to touch base mid-course with stakeholders to see how initial insights match up with their intuition and domain knowledge."

By talking with the people who want the problem solved (such as people working in lines of business) data scientists and data analysts can better understand what their "customer" wants to accomplish. An interactive approach, with other people and data, can help pinpoint what the better questions are.

"Analysts are indeed better-than-average in distilling a challenge into meaningful questions. Getting from 'Why aren't our websites converting better?' to 'Will including customer testimonials improve our website conversion?' takes a big leap into creating value through analysis," said Ilkka Petola, head of growth and former head of data science at online invoicing software provider Zervant and the company's previous head of data science. "This skill is best acquired by working on a problem that's meaningful to you, together with an analyst." 

Other options that are not mutually exclusive include: 

• Taking a course or reading a book about how to think analytically
• Learning how to use an analytics platform proficiently
• Asking data scientists and data analysts for their suggestions on how to improve query quality
• Reading articles on the subject
• Learning by doing

Good, analytical thinking requires a shift in mindset that tends to be more skeptical than the average business professional tends to be with data.

"Critical thinking is the practice of thinking logically and analytically, and it underpins all scientific thought and process," said Peter Watson-Wailes, founder of scientific product management solution provider Hirundin. "Problem solving is thinking to analyze data and find ways to find solutions to challenges you encounter. These two modes of thought are the basis of all the work you do in research and analysis, and both are things you can get better at."

Following are some suggestions from more data scientists and analysts.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio