Mini-Post -- IBM IOD -- Data Scientist

This is the first in a series of mini-posts based on what I’ve been hearing at IBM IOD.  They are mini-posts because there are too many thoughts here worth at least mentioning, and hence no time to develop the thoughts fully.

One key difference with past vendor data-related presentations is the prominence of the “data scientist.”  I wish folks hadn’t chosen that term; I find it confuses more than it enlightens, giving a flavor of scientific rigor, data governance, and above all emphasis on unmassaged, unenlightening data.  Rather, I see the “data scientist” more as an analyst using Big Data to generate company-valuable informational insights iteratively, building on the last insight – “information analyticist” for short.  Still, it appears we’re stuck with “data scientist”.

The reason I think users ought to pay attention to the data scientist is that in business terms, he or she is the equivalent of the agile developer for information leveraging.  The typical data scientist, as presented in past studies, goes out and whips up analysis after analysis to pursue cost-cutting or customer-insight-using insights.  This is particularly useful to the CMO, who is now much more aware of the need to understand the customer better and get the organization in sync with company strategy – because they are often entirely unmotivated to do so now as a result of cost-cutting focuses.

Effectively, a focus on the data scientist as the spearpoint of a Big Data strategy ensures that such a strategy is far more likely to be successful, because it will be based on the latest customer data rather than senior executive opinion.  If vendors truly want Big Data to be successful, the data scientist role in an organization is one that they and the firms themselves badly need to encourage.