In this way, governance is planned and executed to create competitive advantage, addressing policy compliance, security, accessibility, and usability in a frictionless and comprehensive manner. This in turn speeds up the availability of the data and increases its usability to distributed team members—while maintaining centralized control over risks. Although common data governance practices present hurdles for businesses, this blending of models can potentially overcome those hurdles.
Both data governance models pose challenges
Companies are struggling to manage data at scale and in the cloud. Nearly three quarters of decision makers in a recent Forrester Research poll say they do not yet manage most of their organization’s data in the cloud. Some 80 percent say they have difficulty governing data at scale. A whopping 82 percent cite forecasting and controlling costs as a challenge in their data ecosystem, and 82 percent say confusing data governance policies are a difficulty.
Meanwhile, the volume of data companies must manage is mushrooming, and more users are clamoring for more access. “You now have much more data coming from many more sources being stored in many more places,” says Patrick Barch, senior director of product management at Capital One Software.
Organizations want to make this data accessible to more business teams, enabling new insights and more business value. Many struggle, however, to balance the need for central governance of data in the cloud—which ensures comprehensive governance but can bottleneck data access—with a decentralized model that gives lines of business more control over and access to data and analytics. Decentralization, however, has its own disadvantages. Different teams may not be aligned on governance policies. Specific data or types of data can get stuck in silos, not available to all. Machine learning engineers may lack access to the data they need to build advanced analytics tools.
“Your teams want full and instant access to the data and the tools of their choice,” says Barch. “You can’t manage everything centrally without becoming a huge bottleneck or hiring an army of data engineers, and you can’t completely decentralize the management responsibility without incurring significant data risk.”
Best of both worlds
There is a way, however, to combine centralized and decentralized approaches into a new model of data governance through federation of data management. Doing so enables businesses to realize the advantages of each, without the disadvantages.
Capital One, for example, adopted this model while the company shut down its data centers and moved operations onto the public cloud. The company implemented a cloud data warehouse to make data widely available to business teams, yet realized it needed to be attentive to data governance.
“Without good governance controls, you not only have the policy management risk, but you also risk spending much, much more money than you intend, much faster,” says Barch. “We knew that maximizing the value of our data, especially as the quantity and variety of that data scales, was going to require creating integrated experiences with built-in governance that enabled the various stakeholders involved in activities like publishing data, consuming data, governing data and managing the underlying infrastructure, to all seamlessly work together.”