A strained Data Science analogy

January 21, 2013
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

In the sponsored article Data Science: Buyer Beware at Forbes, SAP's Ray Rivera takes a dim view of Data Science. According to Rivera, Data Science is a "management fad" in the mold of Business Process Reengineering, and casts data scentists as self-ordained "gurus" whose mission is to stand between the "ignorant masses" that need access to data and a company's valuable data stores. He likens data scientists to the icemen of the olden days, keen to provide a handcrafted service instead of the newfangled automated solution: 

I don’t want no iceman
I’m gonna get me a Frigidaire …
I don’t want nobody
Who’s always hangin’ around.

If you've been following my writings about data science on this blog or in my webinar on the Rise of Data Science, you'll know I find this viewpoint to be total bunk. (So does Melinda Thielbar, who offers an excellent critique of Rivera's post from the perspective of a practicing data scientist.) First, Data Science definitely isn't a management process, and it's certainly not a fad: statistical analysis, one of the three components of Data Science, has been used in companies for more than 100 years, and the advent of Big Data and all of its applications has only solidified its importance in recent years. Secondly, acting as a gatekeeper to data is the antithesis of Data Science: a data scientist's main focus should be on liberating data by creating data apps that provide on-demand access to data analysis, while implementing the unique expertise that data scientists provide. 

There's much more I could say about this, but my thoughts are captured in detail in this podcast at the IBM Big Data Hub. In my conversation with David Pittman we also cover whether Data Science is "sexy" (note: there's no such thing as a calendar on the theme of "Guys and Gals of Data Science"), and how the R language is an ideal platform for creating data apps. You can listen to the podcast at the link below.

IBM Big Data Hub: Rebuffing "Buyer Beware" Attitude on Data Science

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