Big Data

How to succeed as a data scientist even if you aren't an analytic

Although analytics are a natural fit for big data jobs, other personality types can be great data scientists. Get the specifics.

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Believe it or not, you don't have to be an analytic to be a data scientist.

I've spent the better part of my adult life hanging around people who are either current or future data scientists. The early years of my consulting practice were filled with opportunities to build data solutions (data warehouses, operational data stores, data-based web applications, etc.), and my professional network was filled with all sorts of people. Granted, there were similarities--we all loved data and the prospect of building a data solution that would make someone's life better--but not everyone had the personality of a data scientist.

Here's advice on pursuing and enjoying a data science career if you're not an analytic, but you're one of these personality types: Driver, Expressive, or Amiable.

SEE: How to build a successful data scientist career (free PDF) (TechRepublic)

What makes an analytic best suited to data science?

First, let's explore what makes an analytic an analytic.

When trying to understand personalities, my favorite technique is Social Styles, because it's based on observed behavior instead of psychoanalysis. Social Styles looks at two dimensions of behavior: assertiveness and responsiveness. Assertiveness is the degree to which a person behaves in a direct or forceful manner; responsiveness is the degree to which a person reacts to their own emotions or the emotions of others.

Behaviorally, analytics are less responsive and less assertive than other personality types, which is ideal behavior for a data scientist for several reasons. They're very emotionally controlled; the ability to compartmentalize emotions provides a clean and unobstructed environment for logical thinking. They're also curious and open-minded. These qualities provide them with the patience and persistence that's necessary in problem-solving scenarios.

Data science for drivers

Drivers are very independent and emotionally controlled, but they are much more assertive than analytics. They take calculated risks and, above all, get things done.

A lot of leaders and managers are drivers. When I focused more on management consulting and less on technical consulting, I found myself working more with drivers.

A driver is a great addition to a traditional data science team because they can get things moving along. But to be a good data scientist, the driver needs to slow down and think more. The driver needs to define progress in terms of thoughtfulness to succeed instead of completing deliverables. The compulsion to keep moving forward often drives impulsive behavior--data science requires the patience to think things through. I've been in many situations when I thought a solution was ready to go, only to find critical logic flaws during a third or fourth review.

SEE: Hiring kit: Data architect (Tech Pro Research)

Data science for expressives

Expressives are people who are assertive yet do not have the emotional control of drivers. They are flamboyant, big picture thinkers who are willing to take risks to achieve their objectives. They enjoy novel approaches, take spontaneous actions, and make decisions and act quickly.

Expressives are a lot of fun to be around--they are often the life of the party. They're also fantastic on a data science team, as they can often find creative solutions to tricky problems.

But, expressives are the behavioral opposite of analytics, so they must make some sizable adjustments to fit into the data science world. Like drivers, they need to slow down and think more--this is often done effectively by keeping an open mind and brainstorming with others.

Expressives need to bear in mind that most analytics are introverts, so it's important to time their interactions properly. Also, the expressive must not let their emotions cloud their thinking; they should spend more time reviewing decisions to make sure there's no emotional bias.

Data science for amiables

Amiables wear their emotions on their sleeve as well, but they're a lot less assertive than drivers and expressives. They are sympathetic to the needs of others and quite in tune to what's happening below the surface. They are great, trustworthy friends, and they tend to bring out the best in people. Amiables can usually get at the heart of difficult, interpersonal problems. For this reason, they're excellent to have on a data science team, as they tend to foster great teamwork.

Their sensitive nature is problematic when they're data scientists. Data science requires logical thinking, and emotions can wreak havoc in this area. The core responsibility of a data scientist is to develop and analyze logical data solutions; anything that slows that process down--like emotional interference--is a challenge that must be overcome. It's not healthy to shut down this emotional radar--the trick is knowing what to do with the information. It's good to keep a private journal for catharsis, and then focus on completing the logical task at hand.

SEE: Personality clashes stalling your data science team? Try the Myers-Briggs Type Indicator (TechRepublic)

Conclusion

Data science is not reserved only for the analytics. In fact, a data science team is orders of magnitude better when it's comprised of different personalities.

Drivers are good for moving things forward, and you can't build a high-performing team without an amiable or two. Expressives add fun and humor to the team, and they can help dislodge the team from a particularly troublesome problem.

Anyone can be a great data scientist--as long as they make any necessary adjustments for the job at hand. Everyone benefits when there's more personality-type diversity in the data science world.

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