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Which Businesses Really Need A 'Data Scientist' Posted on Jun 13 - 2018

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Yes, the quotes are intentional. I’m a marketer, so I understand that rebranding might drive up the price, so some programmers and analysts are calling themselves data scientists. What that means is that business people, from executives to HR to line managers, should have a better understanding of the claim and reality.

The entire purpose of business software systems has always been to provide business decision makers the KPI’s they need to make informed decisions. That was the goal with mainframes and reports on ribbon paper, and it remains the goal on today’s smartphones. Yet data scientist is a fairly new term; what changed?

It’s not the Algorithms but the Computer Power

While the speed of individual computers has massively grown since I was a computer operator in the 1980s, it’s not that which has created the large jump in computing power in the last decade. Clustered computing, the ability to share the workload between large numbers of computers, is what has exponentially grown the ability to perform far more complex calculations much faster.

The algorithms used have, therefore, being more complex in concert with those advances. Forget the “What took six weeks now takes six minutes” aspect, what wasn’t even reasonable to consider running on a computer is now reasonable.

Most of the algorithms running on computers these days aren’t new, they weren’t developed for computing. They existed as mathematical exercises or computing science theory. Now that theory has been coming into practice.

Companies Don't Create Their Own Accounting Standards

Ok, Enron and other companies showed that, yes, they sometimes do. However, in the US we have GAAP and other standards exist in other nations. A team of very experienced people sat down and figured out something that everyone can apply without understanding the details that were studied in the creation of the standards.

The same is true with algorithms. A few mathematicians and computer scientists can think up complex new ways to analyze information. A larger, but still focused group, can implement those algorithms into software systems, and the far larger business world at large can leverage the analysis. View More


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