The term "data scientist" was first used as early as the 1970s. But the underlying methodology of the role has its roots in the financial services industry of the 1980s, when companies faced high data acquisition and storage costs.
Since then, a lot has changed in technology. But while some tech roles have faded away or morphed into different positions, the data scientist role has withstood the test of time. In fact the position appears to be more critical to the enterprise than ever.
Today, organizations are successfully applying data scientists to a number of sectors to help make smarter decisions for engaging customers, streamlining processes and allocating resources.
As the volume and importance of data has grown, so has the need for the data scientist.
"Most businesses will confess that while they have access to huge amounts of pivotal consumer (and) company data, they still struggle to take meaningful action," said Mark Schwarz, vice president of Data Science at Square Root. "This is where data scientists come in. Data scientists are capable of identifying actionable opportunities within data sets at a level that transcends typical analysis."
In fact, recent data from Square Root showed that despite spending up to 20 hours per week collecting, analyzing and reporting on data, nearly one in three companies fail to act on their collected data.
Moving the needle
The role of the data scientist within an enterprise can vary. In some scenarios, data scientists analyze data stored within the confines of their own business. In others, they examine data that will move the needle for their business' customers. This helps a business by enhancing customer experience and working to improve both sales and retention rates, according to Badri Raghavan, chief data scientist for FirstFuel.
"In other words, data scientists help both the top and bottom lines for an organization," Raghavan said.
One of the greatest challenges organizations face today is making sense of the tsunami of information available. Many find themselves stuck in the "data for data's sake" trap—capturing every piece of available information and consequently becoming overwhelmed by the sheer volume of what they need to analyze. This is part of the reason that the role of data scientist is becoming increasingly pivotal.
"Data scientists can start to bring real business value by adopting a Deep Data framework," said Raghavan. "This approach is based on the premise that a small number of information-rich data streams, leveraged properly, can yield more value than masses of low-value data."
"By taking a Deep Data approach, businesses can better understand their own business or their customers' pressing issues , and offer actionable, scalable and customized insights while crucially enhancing the value of the economic investment in data," Raghavan said.
One of the primary responsibilities of a data scientist is to lead organizations through creative, structured and data-focused approaches to change, according to Schwarz.
"In order to do this, you must be able to clearly define problems, intelligently test them and effectively and strategically communicate the results in such a way that drives action and moves the business forward," Schwarz said. "Data scientists are able to pinpoint and measure how and which changes are helpful for organizations, through the analysis of heavy volumes of information, data sets and structured problems."
Data scientists can deliver tremendous value to a business by making sense of what the information means for every level of the organization, said Greg Arnette, co-founder and CTO of Sonian. But it's the fact that the tech landscape has shifted so dramatically over the last few years that has enabled data scientists to truly find their collective stride.
"The fact that many businesses have moved to cloud solutions means (data scientists) can now focus less on the nuts and bolts of technology and instead focus on more value-added opportunities," said Arnette.
Derek Schoettle, general manager of IBM Analytics Platform Services, agreed.
"Data has rapidly become the most valuable currency in today's enterprise, evolving the role of the data scientist," said Schoettle. "New solutions in the market have opened the door for new data handlers such as developers, business leaders and analytics architects to step in and do more with their data with fewer resources."
"This evolution will allow for more collaboration on data science initiatives across the enterprises resulting in increased strategic business decisions from data analysis," Schoettle said.
Continuing the evolution
Going forward, it's unlikely that the torrents of data organizations now deal with will shrink. More likely, they'll continue to rapidly grow. The need for data scientists to make sense of it all will, therefore, likely grow as well, and the industry will see a continued evolution of the data scientist role.
"Companies such as Google and Facebook (are) constantly finding new ways to elicit and respond to user feedback in new and innovative ways," said Schwarz. "We're beginning to see more business verticals outside of technology use this approach to data analysis to drive change."
"The auto and retail industries for example, are starting to tune their business decisions and user interactions in the fine-grained, predictive ways that Google and Facebook do," Schwarz said.
"I think we're just at the beginning of this journey where IT reinvents itself for the enterprise," said Arnette.