WSJ – Why Do We Need Data Science When We’ve Had Statistics?

Logo for the Wall Street Journal CIO Journal

On May 2, 2014, the WSJ CIO Journal published an insightful article by Irving Wladawsky-Berger – a recent VP for IBM in technical strategy and innovation.  The article entitled, ‘Why Do We Need Data Science When We’ve Had Statistics for Centuries?’ makes insightful comparisons to the emergence of the field of ‘computer science’ (as opposed to leaving it as a sub-discipline to traditional academic disciplines including Engineering and Physics).  He makes a compelling case of why Data Science should be a unique discipline, and why you should know this to take advantage of the opportunities of Big Data.

In God We Trust; All Others Bring Data – W. Edwards Deming

As CEOs and senior executives begin to consider the signifiant opportunities of Big Data, they are realizing that they can vastly expand their customer segmentation models.  Accordingly, they are realizing that they have a substantial skills gap internally with Data Scientists.

Defining Data Science and the Capturing the Opportunities of Big Data

Data Science is a fancy way of stating the conversion of data, into actionable knowledge.  Notably though, data scientists skills transcend the skills of statisticians in that they incorporate a vast array of skills from disciplines including math, statistics, computer programming, constructing integrated data platforms and advanced computing.

As a helpful reference, Mr. Wladowsky-Berger mentions “one of the best papers on the subject is Data Science and Prediction by Vasant Dhar, professor in NYU’s Stern School of Business and Director of NYU’s Center for Business Analytics. The paper was published in the Communications of the ACM in December 2013.”

Succinctly, this WSJ contributor to the CIO journal explains the logic of why Data Science should be a stand-alone discipline outside of statistics.  He explains that:

“In short, it’s all about the difference between explaining and predicting.  Data analysis has been generally used as a way of explaining some phenomenon by extracting interesting patterns from individual data sets with well-formulated queries. Data science, on the other hand, aims to discover and extract actionable knowledge from the data, that is, knowledge that can be used to make decisions and predictions, not just to explain what’s going on.”

The Time is Now to Rethink the Enterprise & Leverage Big Data

If your business has not yet realized the opportunities that reside in accessing data from different operational silos, the time is now.  If your business has not yet considered the opportunities of reorganizing departments to take advantage of the opportunities within Big Data, the time is now.

To conclude, if you buy-into the value of establishing Data Science as its own discipline, you may want to ask yourself the question that Mr. Dhar asks – “Unlike database querying (e.g. statistics), which asks What data satisfies this pattern (query)? discovery asks What patterns satisfy this data?,” notes Mr. Dhar.

Therein lies the opportunity of transcending rearview mirror decision making, no longer being limited by the insights of intuition, and expanding your segmentation models to predict the behavior of the next generation of customers and prospects.

To read the full article on the Wall Street Journal web site, click here.

By Nick Mavrick

You can find Nick Mavrick on Google+

Intelligent Response specializes in managing and securing Strategic Marketing and Digital Advocacy projects from start to finish in Washington DC.

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