Data Scientist: Important New Role Or Trendy Job-Title Inflation?

The big data universe revolves around this seemingly new role called “data scientist.” For IT professionals who are just now beginning to explore big data, the notion of a data scientist may seem a bit trendy, hence suspect. How does it differ from such familiar jobs as statistical analyst, data miner, predictive modeler, and content analytics specialist?

Yes, data scientist is a trendy new job title to emboss on your business card. But it’s also a very useful new term for referring to a wide range of advanced analytics functions that heretofore have had no consensus category label. The term recognizes that advanced analytics developers, like scientists generally, spend their careers exploring new data for powerful insights that may not be obvious on first glance.

Indeed, one might define a data scientist as someone who uses statistical algorithms and interactive exploration tools to uncover nonobvious patterns in observational data. This definition is broad enough to encompass a wide range of data scientists doing various types of analyses against many data types. The tools may be usable by any intelligent person, or they may be so specialized and abstruse that you practically need a Ph.D. in higher mathematics to get started. The underlying algorithms may be limited to the most common multivariate regression approaches or may include the latest advances in artificial intelligence and machine learning. The exploration may be highly visual, or it may also involve trial-and-error iteration through complex statistical models.

But don’t fool yourselves into thinking data scientists must live in ivory towers. In fact, far more data scientists work in the business world than in the halls of academia or think tanks. Data scientists can prove to be one of your most strategic assets in the competitive wars. If you can uncover new patterns in customer sentiment before your rivals, then you can address them before the competition can even cobble together a clue. If you can adjust the media mix behind your digital marketing campaign in real time to ensure the right messages get to the right audiences, you can save the campaign, and possibly your company, from going down the tubes. And by iterating through behavioral analytics models on your customer experience platform, you can conduct real-world “experiments” that help strengthen satisfaction and retention.

Data scientists can be your core brain trust driving these and other applications of advanced analytics.

Comments

Data Scientist

Hi,

I vote: "Not-So-Trendy Job Title Inflation."

What you're describing is what good analytics and informatics experts have been doing for decades.

My Best,

Frank Guerino
Chairman
The International Foundation for Information Technology (IF4IT)

Let's define "trendy" then

Frank:

Sure, data science is something that analytics/informatics pros have been doing for eons. And job-title inflation isn't trendy, seeing as how the business world figured out long ago that it's key to career self-aggrandizement (and often, as a way of elevating people without having to pay them more). But, when well-established quant/analyst positions suddenly start to get renamed to something that suggests they're now in the august company of Copernicus, Newton, Einstein, and the like, that's clearly a trend. And given that it's an ongoing trend that bestows some semblance of prestige, that's pretty much the definition of "trendy."

Jim

Data Scientist Trends

Hi Jim,

Your point is well taken. I checked "Google Insights for Search" and it appears that the search trend for "Data Scientist" has been steadily on the rise for about 7 years or so. Maybe, that rising trend and its supporting data is a piece of the answer you're looking for.

I hope this helps.

My Best,

Frank Guerino, Chairman
The International Foundation for Information Technology (IF4IT)

Clearly, the title is in vogue....doing study on that now

Frank:

I'm working on a Forrester study regarding which "BI" roles are expanding in importance, in terms of job titles, responsibilities, etc. In the broadest definition of "BI" (ie., basic analytics plus advanced analytics), the "data scientist" role (however defined) is definitely on the upswing. Hope to be able to do some primary research to verify that (educated) observation in 2012.

Effectiveness of Data Scientists

Hi Jim,

I wonder how effective Data Scientists will be given that it has a clear dependency on how enterprises perform Data Management and Information Management. Notice that the trend for both is downward.

In the end, your BI is only as good as your Data/Information solutions. Will Data Scientists have to take on the challenges of better Data and Information Management, too, in order to get to getter BI?

My Best,

Frank Guerino, Chairman
The International Foundation for Information Technology (IF4IT)

"The trend for both is downward"?

Frank:

Explain the comment "how enterprise perform Data Management and Information Management. Notice that the trend for both is downward." I'm not sure how to respond to this comment unless you clarify what you mean.

Jim

Important Visibility for an Existing Role

Your points are well-taken. I work for a company where a significant portion of the revenue stream is tied to our expertise in data analytics. The role, under various names, has been here for decades for us. But it is altogether positive when the visibility increases and the general industry discussion agrees that the depth and breadth of the scientific contributions to analytics methods has increasing business value.

I do agree with Frank's comment that data quality and data governance can be essential enablers for success in analytics. But given the ubiquity of BI projects and the thorough integration of cleansing & standardization into the general population of database administrators, I don't see a decrease but an increase in data quality efforts, albeit spread into many other disciplines.

But I strongly resonate with the word "role" when describing data scientists. We are not discussing a totally new discipline nor do we imagine that a single magical superhero can now handle the full task of identifying, collecting, cleansing, modeling, productionizing, socializing and deploying analytics in an enterprise. Instead, we are giving due recognition that one of the essential roles is the scientist with specific expertise in handling observational data including the exploration and modeling which leads to new descriptive and predictive models of the behavior that we're observing. That's a core role to any hard scientist, and now we begin to recognize its central importance also to business.