When executives at medical benefits management company eviCore Healthcare started thinking about how to take better advantage of real-time and predictive analytics in 2013, they wanted to avoid building an analytics team structure that would stand separate from eviCore's business units.

In particular, Matt Cunningham, an executive vice president who is in charge of operational improvement initiatives, knew from his prior experience as head of IT that building an IT-centric analytics infrastructure can lead to data silos that make it hard to share meaningful information. His goal was to "hand the business back to the business" through the increased analytics efforts. "We should be able to drive the analytics and decision making to the lowest level possible," Cunningham said.

For that reason, eviCore eschewed much of what's often associated with advanced analytics programs. Cunningham hired a team of four analysts, all of whom have master's degrees rather than Ph.Ds. Their primary areas of expertise are in economics and specific business lines like finance. Only one has a background in math.

The company also skipped the market-leading advanced analytics tools, such as software from SAS Institute and IBM's SPSS, instead opting for software from startup Alpine Data Labs. Cunningham said the choice of tools was important in avoiding getting "locked into data science."

Stopping the hunt for analytics unicorns In reality, there has been little effective integration of good data modeling against complicated data at the business level. Matt Cunninghamexecutive vice president, eviCore Healthcare It's a strategy that other companies are also adopting today. As the price for data scientists continues to rise and their ranks remain insufficient to satisfy demand, businesses like eviCore are looking for ways to leverage their data and build out a big data team without shelling out big bucks for what have been described as data scientist "unicorns." For Cunningham, it also has to do with being realistic about what data can accomplish. He said he's always skeptical when a software vendor tells him about all the great things big data analytics can deliver. There's no shortage of promise when it comes to streaming analytics and unstructured data analysis, but the issue Cunningham raises is how that ties into the business. "In reality, there has been little effective integration of good data modeling against complicated data at the business level," he said. As a result, Cunningham is more focused on structuring his analytics team to derive tangible value from specific data analysis projects. Currently, he said, the team is working to build better analytical models to predict which medical benefits claims should be paid by insurers and which shouldn't. The goal is to shorten the time it takes to get an answer on coverage when a healthcare provider submits a claim on behalf of a patient. This involves running claims data through a model that looks at factors like the medical diagnosis code, the specialty of the doctor submitting the claim and other clinical information.