Bianca WrightData scientists are increasingly sought after in the nearshore environment as the needs of SMAC (social, mobile, analytics and cloud) create demand for those who can deal with big data. Although it has roots in the 1960s, the term data science was first used in 1996 at the biennial conference of the International Federation of Classification Societies (IFCS) in Kobe, Japan, but has really only gained widespread recognition in the past few years.
Job boards are filled with ads for data scientists – even a casual search on a database like Monster.com yields more than 1,000 openings – and the role seems to have been acknowledged as crucial for those in the customer engagement space and broader tech circles in general. Yet there is still confusion about the skills set and the role that data science offers.
Beyond Statistics
Those who conflate statistics with data science are missing a key part of the puzzle. In a column for IMS Bulletin, Hadley Wickham, Chief Scientist at RStudio and Adjunct Professor of Statistics at Rice University, explained how data science and statistics differ. “Statistics is a part of data science, not the whole thing. Statistics research focuses on data collection and modelling, and there is little work on developing good questions, thinking about the shape of data, communicating results or building data products,” he wrote. “Attempting to claim that data science is ‘just’ statistics makes statisticians look out of touch, and belittles the many other contributions outside of statistics.
Capgemini’s Director of Analytics, Divya Kumar, said that data science is revolutionizing the consumption of information in businesses similar to how search revolutionized the way content was consumed over the Internet.
“The business case for outsourcing has moved from cost arbitrage to value creation focused on optimizing operations, and enabling business decisions and transformation,” she said, adding that data scientists are invaluable resources vendors can embed in a client’s business operation to accelerate these outcomes, as the vendor is likely already managing significant portions of the client’s data.
Aaron Beach, data scientist at SendGrid, explained that fundamentally the techniques and technologies employed by data scientists are not new. “What is new is the confluence of wider understanding of these techniques and the ability to implement them in software quickly,” he said. Beach said that at SendGrid the data scientist role co-exists with that of the ‘engagement scientist’, enabling seamless integration between data analysis and the deployment of that analysis in marketing strategies.
“In-house engagement scientists can serve as an important line of communication between outsourced data scientists and in-house marketing teams. That being said, engagement science doesn’t have to be a role: it can be, but the most important aspect of the discipline is to form a much-needed bridge between data science, engineering and marketing,” he said.
Steven Hall, Partner, Emerging Technologies at Information Services Group, noted that analytics is playing a critical role in business process improvement, customer segmentation, and identifying new revenue streams. “The ability to dissect this data and provide valuable insights to clients or improve operations is a key value proposition for Service providers,” he said. “In many cases, these data scientists are the critical link between the business and technology – essentially addressing the question – so what?”
Different Approaches
In the outsourced environment, approaches to the role of the data scientist differ. Kumar noted that the business models are still evolving with both sides experimenting approaches. Vendors are taking two approaches, she said. ”One is to scale up the value chain of services by offering data science as a separate service. The second is to embed it into their regular operations with a focus on business outcomes,” Kumar added.
Clients on the other hand, are leveraging it in different ways. According to Kumar, some retain control and utilize it as an “on-demand” service, or designate a specific team to collaborate with the outsourcer vendor, while others leave it to the vendor to drive the initiative so they can dedicate more time to assessing the business outcomes derived from it and optimize business decision making.
Hall added that in many cases the clients and Service Providers are aligning data scientists to the large analytics and big data projects to drive the statistical analysis and insights to be gleamed from the reams of data collected. “We are seeing segmentation though as Data Scientists begin to develop expertise in vertical domains,” he said.
As an example, Hall explained that analyzing customer spending patterns for a bank or credit card firm and devising the right algorithms to determine patterns is a very different problem than analyzing large amounts of data from a jet engine to determine maintenance issues or future engineering enhancements. “Both require deep understanding of data querying, statistical analysis, and data correlation, but they also require vertical expertise to draw correct conclusions,” he said.
Emerging Opportunities
Opportunities for vendors to offer data science skills as an outsourced service are significant. Kumar said: “It is challenging for every client to have a dedicated data science team since the skill sets are very specialized, and scale is very low. It makes a lot of sense for the vendor to invest in these skills. It can have a larger data science team with diverse skill sets, and deploy them based on client needs, thus benefiting from economies of scale.” She added that the vendor is able to create value for the client by blending their knowledge of the client business, best practices from other clients and the expertise to mine it all using data science.
All agree that these skillsets are absolutely critical in today’s environment. “The level of data being collected and analyzed is unparalleled and growing at remarkable rates,” Hall said. “Organizations invest in Data Scientist to drive actionable intelligence from this data. The key is to align the technical expertise data scientists bring with the vertical expertise. From a Service Provider, the challenge is to demonstrate exceptional value through analytics that increase the top line or process improvement that impacts the bottom line. As with all new technologies and roles, it is imperative to stay aligned with the core business drivers.”
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