Why data analytics initiatives still fail

Strong data analytics is a digital business imperative — and it all begins with smart data governance practices and an emphasis on quality and context.

Why data analytics initiatives still fail
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Executives talk about the value of data in generalities, but Michele Koch, director of enterprise data intelligence at Navient Solutions, can calculate the actual worth of her company’s data.

In fact, Koch can figure, in real dollars, the increased revenue and decreased costs produced by the company’s various data elements. As a result, she is well aware that problems within Navient’s data can hurt its bottom line. A mistake in a key data field within a customer’s profile, for instance, could mean the company can’t process a loan at the lowest cost.

“There’s money involved here, so we have a data quality dashboard where we track all of this. We track actual and potential value,” she says.

An early data-related initiative within Navient, an asset management and business processing service company based in Wilmington, Del., illustrates what’s at stake, says Barbara Deemer, chief data steward and vice president of finance. The 2006 initiative focused on improving data quality for marketing and yielded a $7.2 million ROI, with returns coming from an increased loan volume and decreased operating expenses.

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