Shedding light on the sexiest job of the 21st Century

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This article will be published in the next edition of the IBM Inspire Beyond Today’s Technology magazine (ibm.com/inspire/be)

When Harvard Business Review announced in October 2012 that the role of “Data Scientist” is the sexiest job of the 21^st century, some may have wondered what exactly such a role entailed. Chances are few will still be pondering this question in years to come as the demand for businesses to make sense of “big data” rapidly accelerates.

What does a data scientist do exactly? “A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.”

Damiaan Zwietering, Sales Engineer for Advanced Analytics at IBM, whose own 20-year role in analytics has evolved into that of data scientist, believes there are two key drivers behind the sudden interest in the role: business demand, and the art of the possible given the volume of data and availability of more sophisticated algorithms and analytics.

“Previously we just did not have the computing power and technology to analyze data in a meaningful way. But now it’s both possible and necessary to get more from our data.”

Koen Havlik, Founding Partner at Algoritmica, a company that specializes in helping organizations apply predictive analytics algorithms for improved business results, notes both the surge of interest and shift in what expected from a data scientist role.

“Six months ago there were no job vacancies for this kind of role, certainly not in the Netherlands. But demand is picking up, mainly because there has been a real culture change inside many organizations to become much more data-driven in their approach.”

Part analyst, part artist
Comparing data scientists to the more traditional role of data analysts, Anjul Bhambhri, vice president of big data products at IBM, describes the data scientist as part analyst, part artist. “A data scientist is somebody who is inquisitive, who can stare at data and spot trends, who really wants to learn and bring change to an organization.”

“The data scientist is more linked to the business,” agrees Zwietering. “They act as an intermediary between the business and the data. How can we do things better and faster? It’s more than just analyzing data.”

Given the emphasis on connecting data with the business, it’s not surprising that excellent communication skills are an important requirement for the job: findings and conclusions needs to be explained to the broader business in a compelling and meaningful way.

As demand for the unique skills of the data scientist increases, so too does the need for more sophisticated software. “We stitch analytical tools together to make a business solution,” says Koen Havlik. “There are many solutions on the market and IBM’s SPSS predictive analytics software is one set of tools we can leverage. But what IBM is good at is getting out of the way of the data scientist, which makes the job a lot easier. They let the data scientist use the tools as building blocks to help organizations gain real competitive advantage.”

The data scientist of the future
Both Havlik and Zwietering agree that in terms of business innovations driven by big data the best is yet to come. “In coming years it will enable any organization to understand everything that is happening with their customers and suppliers,” says Zwietering. “Are we seeing a trend? Should something be done about it? What should be done about it?”

Havlik: “It’s crazy that retailers are trying to compete by only increasing scale to improve margins when data offers a new way to gain competitive edge. In the Netherlands we still have a long way to go compared to the US. But all this data can make the business more competitive and remove cost. It just means change to adopt a more data-driven approach.”

“In the beginning we struggled to get attention for this kind of role, but that has really changed in the last two to three years, agrees Zwietering. “It’s nice to know that at IBM we’ve been running ahead of the crowd for some time with this.”