How Waterline Helped Fannie Mae Achieve Full Data Governance

The California Consumer Privacy Act (CCPA) goes into effect this January. But according to several news reports, most organizations won’t be ready. Many, in fact, haven’t even begun working on it.

Meanwhile, many organizations (about half according to some reports) are still trying to comply with GDPR. The problem with meeting these regulations is the sheer enormity and complexity of the task.

Data Governance: A Gargantuan Problem

Fannie Mae, for one, sucks in more than 10 million data sets per day. As the data pours in, it’s tagged in a painfully slow and error-prone manual process that leaves most data miscategorized, lost, and impossible to track.

Fannie Mae is a $110B company—the fifth largest financial services company by revenue—serving the people who house America. They are, of course, a leading source of financing for mortgage lenders.

But like many legacy organizations, Fannie Mae has been hamstrung by data that has been severely fragmented by data silos. This has made governing and accessing high quality data extremely costly and time-consuming.

Proper Data Governance Must Include Fast Ingestion of High Volume Data

Realizing it needed to transition to a more agile data environment that allowed access to data in more timely fashion, Fannie Mae sought to create a modern data environment that ensured the right data got to the right person at the right time. Traditionally, it would take weeks or even months before data could be generated and loaded into the data lake.

The organization needed:

  • To efficiently pre-populate data lake with all necessary dataset properties
  • An API based solution in order to automate
  • Hi volume daily ingestion of datasets

DataOps + Waterline Data = Fast Delivery of Fully Governed Data

By following a DataOps methodology—which included implementing Waterline’s AI-drven Data Catalog—Fannie Mae could process greater than 10m files/day with all the associated properties and custom search properties to deliver fully governed data quickly and efficiently. By also working to modernize behaviors within its legacy culture, Fannie Mae was able to fully automate and accelerate the cataloging and searchability of its data.

This means dramatically reducing the cycle time to data analytics for improved customer experience and a dramatically re-imagined organization that uses data in cutting-edge ways to maintain a leadership position in the big data era!

Check Out Our Case Study

We’re very proud of our groundbreaking work with Fannie Mae, which raises the bar for all financial services firms. To learn more, check out the full case study on our work with Fannie Mae here.