Does the Data Scientist Have Mojo? #BISUM
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We finish off Saturday sessions with “Does the Data Scientist Have Mojo?” led by Simon Arkell, CEO of Predixion Software, and Jill Dyche.
Tom Davenport called it “the sexiest job of the 21st century.” To which Jill replies with this blog entry “Why I wouldn’t have sex with a data scientist.”
Everybody thinks they need one. Midsize companies are as interested as global enterprises.
While many executives claim to be searching high and low for the right candidates, others have decided that no one role can harness such disparate skill sets, or that data science is simply too expensive to formalize. Is the data scientist a business analyst? A statistician? A subject matter expert?
We examined the evolution of the data scientist role, discussing the problems it addresses—and the new conundrums it introduces.
They will deal with the ‘not your father’s customer name & address’ database.
We discussed what successful data science looks like in the 21st century—and beyond.
Simon said there are not enough data scientists and if every University cranked them out, there still wouldn’t be enough. Data scientists have short tenure due to the demand. Alternative names are Value Architect and Data Negotiator.
The data scientist shines in predictive models. Colin mentioned a lot of data scientists do descriptive models too, with statistical techniques.
For trying to predict which oil wells will fail or which procedures will harm the patient, you need real expertise in the person as well as the software. Data science will need data scientists.
The halo effect of the data scientist is that they are pushing activities forward faster so data can be used more immediately.
Data scientists work in a specific line of business–thus they’re typically domain experts with knowledge of narrow but powerful toolsets.
The data science team functions as a “one-stop shop” for predictive analytics, unstructured data, or complex analytics requirements.
There can be an MDM and Data Governance component to the job!
The data scientist needs to be defined based on the need of the business, not on an industry definition.
Getting companies to act on new insights remains a (cultural) challenge. The data scientist is only as effective as her organizational influence.
Peter mentioned we may need two types of data scientists in the future – one that is data-oriented and one that is more use-oriented.
The delta between a ‘mere’ statistician and a data scientist is the data translation piece.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.