Strong data analytics is a digital business imperative — and it all begins with smart data governance practices and an emphasis on quality and context.
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.
Since then, Navient executives committed themselves to supporting a strong data governance program as a key part to a successful analytics effort, Koch says. Navient’s governance program includes long-recognized best practices, such as standardizing definitions for data fields and ensuring clean data.
It assigns ownership for each of its approximately 2,600 enterprise data elements; ownership goes either to the business area where the data field first originated or the business area where the particular data field is integral to its processes.
The company also has a data quality program that actively monitors the quality of fields to ensure high standards are constantly met. The company also launched a Data Governance Council (in 2006) and an Analytics Data Governance Council (in 2017) to address ongoing questions or concerns, make decisions across the enterprise, and continually improve data operations and how data feeds the company’s analytics work.
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source http://news.statii.co.uk/why-data-analytics-initiatives-still-fail/
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