The quest for data scientists

Companies are on the hunt to find talented individuals who are able to turn their masses of data into piles of gold. But are they really that important?

Companies are desperately seeking mysterious creatures — data scientists. Some people claim to have seen them in LinkedIn and Target. Perhaps, these were encounters with data scientists from LinkedIn that shop at Target? Or maybe they were Target data scientists who search on LinkedIn for pregnant teens? Either way, the companies are desperate (except for LinkedIn and Target). Why? Because everyone wants to compete in the new, data-driven economy, where Google and Amazon have already figured out “data alchemy” — turning data into gold.

A data scientist symbolises to organisations a gaping hole: a magic that can turn big data into big gold by making sense of vast amounts and multiplicity of senseless bits and bytes (or zettabits and petabytes?). The data scientist is a saviour who (if found) can solve all big data problems, so companies will not have to worry about figuring out how to do it themselves, all they need is to catch two or three really good data scientists, no matter what they are.

I heard a couple of definitions: a data scientist is 1) a data analyst in California or 2) a statistician under 35. Either make 10 per cent above the salary of common data analysts and statisticians, so the latter learn how to position themselves as data scientists. Google shows that web search interest for “data scientist”picked up back in 2010, but the #1 Google search phrase on the subject is “data scientist salary”, which reflects both supply and demand. The second top search is “data scientist jobs”. I confess, I did it too: I copied around two dozens job postings with removed titles and other HR nomenclatures and made a word cloud.

This word cloud is made of the recent job postings for data scientists.

The picture, as well as my more in-depth research, show that companies should look within. Organisations already have people who know their own data better than mystical data scientists — this is a key. The internal people already gained experience and ability to model, research and analyse. Learning Hadoop is easier than learning the company’s business. What is left? To form a strong team of technology and business experts and supportive management who creates a safe environment for innovation. The team members with diverse skills will inspire and enrich each other: their combined knowledge will be the power to develop analysis and bring new insights.

After the team achieves results, compare the size of data with the size of science on the picture. By the way, did you notice that he word large is greater than the word big in the word clud but both are relatively insignificant?

Svetlana Sicular is a research director at Gartner and primarily handles inquiries in the areas of data governance, enterprise information management strategy and big data. Before joining Gartner, Ms Sicular was a leader of Visa Data Authority, where she was responsible for data interoperability, global oversight of critical data components, and international standards that support Visa's global data.

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