Isn't it true? Isn't this what most of the business folks and CXO's expect from their data science teams? Yes in fact, this is what we have been told in nearly all of conferences we attend. Thumping case studies, feverish pitch makes one believe in the story and you walk out with a true sense of achieving same magic for you.

I too am believer in this pitch and that defines our very existence. Everyone including me would like to hire that magician who could talk business, has strong problem solving skills, has in depth knowledge of applied mathematics and statistics and finally explain stuff with simplicity and patience of monk to rest of us. Such a person may exist but how many of us can find one or need one or let's say even afford one? What should we do?
This Whole( magician) is greater than sum of its parts, namely, business folks, statisticians, data processing guys in this case. We can live with less I suppose for the moment and try to maximize what we can get with sum of parts. To get the wheels running its very important for you as a business leader to play your part well. Its you who need to connect the dots for your team, phrase right questions, question outcomes. Some basic understanding of statistics is definite plus as it will help you to converse in language of mathematics with your team and question the technical approach and outcomes. I wish you best in this journey with rewarding outcomes. Do share your thoughts with me!!

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Tags: analytics, big, data, predictive


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Comment by Chandrasekhara S. ("C.S.") Ganti on December 1, 2014 at 2:03pm

Vikas Kamra,  thanks for your nice write /  a wonderful expose of the topics / people , disciplines  involved in the Data Science .. However, lest others forget or be too quick -- the old disciplines are still needed.. One may / would like to forget them As I crudely tell to some who walk with a chip on the shoulder  or later day saints -- Your new methods  do they still  result : 2+2=4 ; That is to the extent I get to reality.

However, that is not to say with both Stats and OR at Graduate level degrees, I have tremendous respect most of the later day disciplines including AI  and Expert Systems .. But I would go on the limb here -- and I was to be registered for a third MS in CS but could not afford the time. So, I am not scared anything thrown at me. Yes , one needs to have practice in programming. AI languages LISP and PROLOG were not that great. But yet we developed a system with PROLOG -- and presented at an Int'l Conference. The bottom line is Semantics before Sytnax .. Logic al problem solution process is definitely the critical scenario and tool set comes afterwards. One perspective. QED 

Comment by Vikas Kamra on December 1, 2014 at 8:48am

That's nice analogy Vincent and I am in total agreement with your view. The definition of data scientist is a big debate in itself. Real data scientist may bring chaos and who would prefer that. 

Comment by Vincent Granville on December 1, 2014 at 8:22am

Real data scientists are to real insights (signal) what self-proclaimed data scientists are to unfiltered data (noise). To put it differently, in big data, signal is buried under noise. The same applies to data scientists. Those truly worth their salt are practitioners who actively participate in the decision process. In some companies, this is not possible, because of organizational structure, corporate culture, and mindsets.

It reminds me when I was younger, applying for jobs. All requested "creative talent". It turned out that this was a lie, and that in fact they were interested in people opposed to change, the yes-people. Companies hate disruption, and real data science can bring disruption. Too bad, the real data scientists will work for competitors - typically well funded disruptive startups - or turn entrepreneurs. And then maybe, this is indeed good news. That's the only way to change the business world and create useful novelty.

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