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analytics program enablement for growth-mindset organizations

Why “Data Scientist” is a Useless Designation

Looking for a data scientist is a little like going on a unicorn hunt.

Looking for a data scientist is a little like going on a unicorn hunt.

Are you trying to hire a “data scientist” for your organization? You might want to think twice before you decide to place that job ad.

“Data scientist” is either a meaningless designation or a descriptor for a person who will prevent your organization from realizing the full value of the data that it currently owns. Here’s why.

Data scientists typically approach the problem from the wrong direction.

Most “data scientists” typically focus on building technically superior models. There’s nothing wrong with building a better rocket ship, but first you’d best make sure that the rocket is actually pointed in the right direction.

No “optimized model” has ever aligned with business objectives. No business has ever generated a significant benefit from merely building a better algorithm.

In fact, many so-called “data scientists” pooh-pooh strategic assessment and project planning as “fluff” that distracts them from the “real work” of writing ever-more complicated code.

Unfortunately, strategic assessment and project planning happen to be vital if you’re ever going to extract any value from your data.

The term (as most organizations use it) describes a unicorn.

It is impossible–or at least, exceedingly rare–to find all of the skill-sets of a so called “data scientist” as most companies envision the position within a single human being. When organizations talk about “data scientists” they typically mean someone who:

  • Has a collection of advanced analytical skills
  • Has vast IT experience
  • Has and effectively uses a broad range of managerial soft skills
  • Can oversee analytic processes at the project level.

This mythical human somehow has managerial acumen and technical skill all rolled up into one brilliant, convenient package. Someone like this might exist in the sense that anything is possible…kind of like the way unicorns might exist in a universe where anything is possible. Since most organizations don’t have the time or money to embark on a unicorn hunt it’s smarter to take a step back and to think about who or what can actually achieve what the organization hopes to achieve by hiring a “data scientist” in the first place.

Anyone can call themselves a data scientist.

Granted, if you are dead set on acquiring a certain skill set then it’s awfully hard to fake having the technical skills. However, there is simply no formal definition for the term, which means no certifications, no degrees, and no quality controls. An unemployed MBA can legally hang out his “data scientist” shingle tomorrow. Often, amateurs do just that, to the detriment of the organizations they attempt to help.

Your existing employees can probably give you what you need.

Believe it or not, your existing employees probably have what it takes to help you derive outstanding value from the data you possess. In fact, training strategic thinkers who are close to the problems your organization is facing is often the first step. Sending key employees to a vendor-neutral training regimen which takes just a few short weeks can help you  begin transforming your data into actionable intelligence that offers solid benefits to your business. Doesn’t that sound far better than hiring an overpriced theoretical analytic specialist who is largely incapable of taking your organization where it needs to go?

 

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