Is your data management system overdue for an overhaul? Benefit plan sponsors don’t need to feel stuck with old systems requiring hours of manual data entry, according to Marc Rind, chief data scientist for ADP.

“I’ve been in data for a long time,” he says. “For generations, the traditional data management approach has been people having to standardize data.”

[Image: Adobe Stock]
[Image: Adobe Stock]

But people in different companies — even different departments of the same company – could have different definitions and means of data. An organization’s governance team would have to come up with one definition for everyone to adhere to.

With new approaches to data science, Rind says, “you’re able to have many different definitions of your data and have them all coded. It’s not about governing the definition of data but more about enhancing and publishing that data.”

With data science, employers and those in HR can see trends much more easily using automated mapping and search capabilities. This will allow them to see trends over time, like what people are choosing for their benefit plans and how benefits impact employee productivity and engagement.

“It builds context around the data,” Rind says. “For employers, they have to not only understand which benefit offerings they have to offer to employees but the effect on retention. They can also see what similar employers are offering and if they are getting higher retention rates.”

Employees can use the data to see what benefits others with similar backgrounds have chosen to get, helping them decide what their perfect healthcare plan looks like. However, they cannot yet see how satisfied people similar to them were with these benefits. Rind says that this feedback loop is important, and will become more prominent for the next generation of data science systems.

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