It seems like it was only yesterday that a new breed of data-driven businesses arrived on the scene. In the interceding years, a number of these firms – Amazon, Apple, Google, Facebook – have gone on to achieve extraordinary market capitalizations, while challenging incumbent businesses whose market dominance has lasted generations. The impact of this disruption is just beginning to be fully realized.
According to NewVantage Partners’ 2018 Executive Survey, published earlier this year, an overwhelming majority (79.4%) of Fortune 1000 executives who were surveyed reported that they fear disruption from agile, data-driven competitors. In addition, 54.4% of these executives stated that the inability to be nimble and compete on data and analytics represented the biggest competitive threat facing their businesses today. It is evident that Fortune 1000 companies have come to the realization that to compete in the 21st Century they must adapt to a new order where data-driven competitors with highly agile, data-savvy business cultures are seizing the competitive initiative.
Fannie Mae’s Modern Data Initiative
Scott Richardson is Chief Data Officer of Fannie Mae, which is formally known as The Federal National Mortgage Association (FNMA). Founded in 1938 as part of the New Deal, the mission of Fannie Mae has been to provide liquidity, stability, and affordability to the U.S. housing market. Like most longstanding corporations, Fannie Mae has legacy environments characterized by data silos. This circumstance makes it challenging and expensive to access high quality data in a timely manner. In a world of increasingly data-driven competitors, firms like Fannie Mae face the challenge of making the transition to agile, more flexible and responsive data environments or running the risk of competitive disadvantage.
Hired by Henry Cason, Fannie Mae’s visionary first Chief Data Officer, Richardson joined their recently formed Enterprise Data organization in 2014 after spending over a dozen years at Capital One. Unlike its traditional banking competitors, Capital One forged a name for itself since its founding in 1988 by employing advanced data and analytics techniques to revolutionize the credit card industry and demonstrate how a leading financial services firm could compete on data and analytics. Data and analytics had been in the founding DNA and lifeblood of Capital One from its inception. Richardson, who had been instrumental in creating Capital One’s first enterprise data warehouse, hoped to bring that same data-driven mindset to transform Fannie Mae into becoming a more data-driven competitor.
Along with his colleague Kevin Bates, a Fannie Mae veteran and neuroscientist by training, and a highly capable data governance, development, and operations team, they sought to undertake a wholesale data transformation to create what they describe as a “modern data infrastructure” supported by “industrial scale data”. To achieve this ambitious objective, Fannie Mae needed to build a centralized data operation. And to realize such an ambitious undertaking, they asked for and received CEO and Management Committee sponsorship. The goal of this effort would be to establish a centralized data function where they could manage data to a high-level of quality, consistency, and timeliness across the enterprise. “The right data, with the right quality, at the right time”, as Richardson puts it.
Reducing the Cycle Time to Data Analytics
Borrowing from the Development Operations (DevOps) movement, which was created to ensure automation and monitoring that shortens development cycles and deployment frequencies, Richardson and Bates are following a DataOps methodology to reduce the cycle time for the firm’s data analytics, from data preparation through reporting. From a process and methodology perspective, DataOps applies Agile software development, DevOps, and the statistical process control used in lean manufacturing, to data analytics. As they describe it, Fannie Mae’s new data platform is now “managing data with the same level of criticality as developing applications”. Foundational to their approach is a common data model, an Agile methodology aligned to a single governing model, and modular architecture designed to “automate everything that you can”.
One step Fannie Mae has taken to achieve their DataOps capability has been to deploy a dynamic data platform that accelerates data flow through the automation of data movement and self-service. By accelerating the testing of a major common securitization platform environment, Fannie Mae could refresh and restore its test data environment in a matter of minutes with an immediate reduction of 28 days of cycle processing. This drove significantly faster testing cycles for a very complex technology and process transformation effort, and has enabled Fannie Mae to support loan origination and servicing activities for its partners and customers with greater flexibility. Data flow solutions from firms such as Delphix have been deployed by Fannie Mae and a number of leading financial services firms as they seek to transform and virtualize their data development environment.
Business Adoption Key to Success
For firms that undertake ambitious data transformation initiatives, there is a common recognition that changing behaviors in a legacy culture requires time and patience. The process should be thought of as a journey. Companies that are successful focus on quick wins and demonstrate incremental progress. Richardson and Bates cite the value of “pushing value into production at least monthly”. They describe their effort as the culmination of a multi-year process. Noting initial wins in the early days of the transformation effort, they cite these wins as a critical factor in establishing positive momentum. One of the innovations that Fannie Mae instituted was to establish a community of Business Data Officers, to ensure that data is fully owned and cared for by business leaders, and that new initiatives consider the creation, ongoing quality, and effective usage of data from the outset.
Today, Fannie Mae has established a modern data environment characterized by near real-time updates, which results in a richer and more granular real-time customer experience. “Data is now viewed as a business asset” noted Richardson. He continues, “We are engaged in thinking about business strategy through the lens of furthering our mission and improving the customer experience with data”. Bates adds, Fannie Mae is now “encouraging experimentation at scale with data”, with the result that the company now has the means as well as an “appetite for reimagining new businesses and business processes”. Iteration and learning fast -- this is what it means to compete on data and analytics in the competitive Era of Big Data.
