"I think statistics go in one ear and out the other. All of us respond to stories more than numbers."

— Koren Zailckas

As algorithms increasingly dominate our society, what can you do to become more valuable in the data economy? Most people are not interested in becoming a data scientist, any more than most wished to be software developers in a previous generation. It's an esoteric specialty that doesn't appeal to most.

But the truth is, we are inherently data analysts. People are pattern seekers — it is one of the core evolutionary advantages of homo sapiens. The degree of rigor with which they recognize patterns varies, obviously, from the Three Stooges to Albert Einstein.

Even stooges are more talented at data analysis than their surface behavior would suggest. Just about everyone recalls the high school misfit who was barely passing algebra, yet could calculate and rattle off Major League Baseball batting averages for dozens of players as they changed day by day. The classroom math put him to sleep; the real-world application motivated him and demonstrated abilities he didn't even know he had.

I didn't particularly like math and took no more than I needed to get through high school and college. Yet in graduate school, I had a statistics class required for my degree in organization psychology. I had the fortune to have a professor for introductory statistics who, though a prominent researcher in his field of quantitative sociology, was driven to be a world-class teacher.

His lectures were clear and entertaining. Recordings and computerized learning modules were available at the library (yes, this was pre-internet, kids). Bottom line, he left a statistics phobic student, which I and many others in the class were, absolutely no excuse not to acquire the subject material.

I discovered I liked statistics. Statistics, it turned out, was simply a quantitative representation of real life, a way to make decisions in the face of uncertainty, much like navigating the messiness of life. I ended up taking most of my electives in statistics and considered pursuing a Ph.D. in it (I didn't). A 2016 Gartner Group study described a hierarchy of data related roles:

1. Data scientist: A large corporation will have several dozen of these — "Quants" with advanced degrees in advanced statistical and mathematical methods.

2. Business analyst: Approximately 5 percent of employees, they are tasked with diving deep into the numbers for particular functions and "trolling for patterns" or answering questions posed by executives or functional leaders.

3. Business power user: As decision support tools become increasingly intuitive and the desire to find answers in data always rises, the business power user rises in importance. These professionals, across business functions, are trained to be somewhat more technical than their peers, and to seek answers in data sets. Most queries are basic enough technically be answered by these power users.

4. Dashboard users: The bulk of employees do not themselves dig deep into data. They rely on "dashboards" with a handful of key performance indicators, (KPIs), much like a person driving a car. This holds true for senior executives and workers in the field, though the KPIs are completely different.

Companies encourage the expansion of power users, as the desire for data analysis is ever expanding. Without power users, organizations require hundreds of analysts to support business users, and even then, there is considerable lag time as the queue of internal analytics customers expands.

The problem and solution of enabling users to answer most of their own questions is reminiscent of AT&T's dilemma with telephone switchboard operators a century ago, when calls were connected by hand at central switchboards. Automated switchboards were critical; without them, forecasts at the beginning of the 20th century predicted, that the trend of growth of telephone use would eventually require everyone being a switchboard operator.

So if you want to be more than a dashboard user and less than a data scientist, aiming to be a power user is a reasonable yet substantial ambition. Every function requires someone who can "dig under the hood" or diagnose what an engine light signifies. There are five key elements of growth on the analytics continuum by which an individual can add value:

1. Domain expertise: deeper knowledge of the function you work in will allow you to ask better questions and seek stronger answers.

2. Intelligence/curiosity: Some people go through life asking: "Why?" In the analytics world, it is a premier skill.

3. Engineering rigor: Whether having an engineering background or building basic competencies in statistics, asking disciplined questions rather than broad ones will lead to useful answers from the data.

4. Technology skills: IT expertise is less critical than ever before, but the ability to code, whether in SQL, Python or VBA, is still a competitive advantage for a power user.

5. Data depth: Understanding how to competently extract data sets for analysis is as valuable as any of the above factors.

Bottom line, we can all be a little bit of a data scientist, as, in the words of Peter Drucker, IT slowly becomes more focused on the I (information) and less on the T (technology).

Isaac Cheifetz is an executive search consultant focused on leadership roles in analytics and digital transformation. Go tocatalytic1.com to read past columns or to contact him.