The Data Chic Quadrant

A lot has been written, and continues to be written about ‘knowledge workers’. The term was first coined by Peter Drucker in 1959 and scholars and thought leaders have been working to define what a knowledge worker is and does ever since. It’s hard to pin down, because it’s still evolving and there are so many different knowledge worker roles. I like this definition I found on Quizlet: Knowledge workers are professionals who create, modify, and/or synthesize knowledge as a fundamental part of their jobs.

No matter how you define the term, I think what’s true of all knowledge workers is that they are continually learning and applying that learning to find better ways to do things. This involves consuming a lot of data, but data and data analysis are no longer the exclusive territory of the so-called data geek. There’s a new breed emerging: The data chic. With apologies to analyst firms, I’ve created my own “Data Chic Quadrant” to describe this trend:

Data geeks occupy the top right quadrant. These are the people who love statistics and numbers and are actively involved with analyzing them. They are likely to be educated and trained in math, or computer or information science, and undertake deep, scientifically rigorous analysis to arrive at the right answers. They may do this for fun as well as professionally, as they are driven to use data to uncover the truth.

In contrast, the data chic are more casual consumers of information and analysis. They want to make information-based decisions, but are not necessarily educated in an analytical field. Armed with smartphones, social media and apps, they wade into data analysis only when they need to make a decision, going just deep enough to get answers and quickly move on with their lives.

They’re slightly more active in their approach than people who just use these tools to find good restaurants or keep track of their fitness data, but like that group they’re not overly concerned with scientific rigor and statistical significance. They’re mainly concerned with winning, and using information to solve problems is a means to that end.

Sports fanatics are an extreme example of the active fact-based type. When we did our fantasy football draft at my office, these people looked at the teams’ players and historical performance. Then they looked at their schedules to see how many day, night, home and away games there were. They looked at where different athletes perform best and then where they’re playing when so they know how many games they’ll play in rain, snow or dry weather. Then based on all of that, they draft their team.

The data chic people did research too, but they didn’t go quite that deep. They looked at historical performance, read other expert analysis, figured out who has the best quarterback and things like that and then they drafted their teams.

The data chic realize that to win they can’t just pick their favorite team or the team whose quarterback went to the same college they did or the team whose jersey colors they like best. They’re actually going through and doing some level of information gathering and analysis, but it’s not exhaustive.

Both groups stand in contrast to the HiPPOs (Highest Paid Person’s Opinion), HiPPOs are the people who operate on gut feelings, only paying attention to analysis when it supports their beliefs. They often approach things with pre-conceived opinions based on qualitative experience. It’s a passive approach; the right answers are already known and no new information is needed. This seems partly generational to me: My dad would qualify as a HiPPO (although I hate to admit he actually was right about a lot of things . . .) This could be an outcome of the amount of information and tools for consuming it that they had for most of their lives, which was a lot more static than what we’re used to now.

A more active version of that might be the person who manages by walking around, or someone like my mom, who likes to consult with her friends on all matters, even if it’s just to confirm her own opinion. This person doesn’t have to be the smartest person in the room. They know they don’t have all the answers, so they crowd source information by talking to others, but they’re still not using quantitative, scientifically-based input, unless the people they’re talking to are data scientists. So, they’re still very much about intuition but it’s more like crowd-sourced intuition.

The data chic are open to crowdsourcing as well, but are more data driven in their approach. They not only want access to data and information to improve their decision making process, they want it in a highly collaborative and easy to digest fashion. They’re happy to ask others, and they may leverage the expertise of data scientists, but they need the tools to do that. Empowering the data chic with these tools is the next frontier of big data analytics.

When we start talking about big data and capturing all this information, the people on the fact-base side of the quadrant get excited about the art of the possible. They wonder, what are all the great things that I could do, or know, with all this data? The challenge is people don’t always know what data is available, or what to do with it. The data chic don’t want a petabyte of raw data. They want just the data they need, packaged up so it’s easy to consume.

Google Search is a great example of a data chic analytic tool. It’s just a box where you ask a question and you get an answer. You don’t question the data or where it comes from. There’s a presumption that this is a trusted data source, whether you’re looking at a stock price or a sports score or whatever it is. You just get the answer and move to the next step. That’s what data chic individuals are looking for.

The data chic far outnumber the data geeks and the HIPPOs, and they’re pushing the market for these types of big data solutions forward. As part of that, they’re pushing these two groups, which once stood at opposite extremes, closer to the their way of thinking.

Historically the data scientists, or quants, or lab coat technicians or whatever you want to call them inhabited their own realm, with their own language and jokes and style of dress. The whole data geek stereotype grew out of that.

Then there were the “suits,” often HIPPOs, seen as uptight and bottom-line focused, who were driving the business. Very seldom did those two groups ever meet.

Technology is now so pervasive that most of us use computers and analytical tools every day, at work and at home. It’s easy to forget that it was not that long ago that the only people who did that were the so-called nerds or geeks. In hindsight they were just early adopters. Today the term data geek is more likely to be a compliment than a put down, and many self-described data geeks wear the name as a badge of honor.

Not all of us are capable of doing what data geeks do or even understanding the results of it, but we have developed more of an appreciation and admiration for the work they do, which enables much of the content and convenience we enjoy on our computers and smartphones today. Big data and analytics have crossed the chasm into the mainstream, and masses of people have now developed enough skills to be data chic, a type of person that historically has never existed.

As this is happening, more data scientists are starting to take an attitude of, “I don’t need to be right all the time. I’m going to use these skills to help people win.” People that used to be HIPPOs are saying, “Okay, I don’t have to be right. I really just want to win.” Using data and information to win is the essence of the data chic attitude. It’s a way of operating that the next generation of solutions must support, and an attitude we can all understand.

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