Use management to initiate, not stop, access to data for analysis


In this way, management is planned and implemented to create a competitive advantage by addressing policy compliance, security, access and usability. This in turn accelerates the availability of information and increases its usability for distributed team members – while maintaining centralized control over risks. Although common data management practices present barriers to business, this model integration can overcome those barriers.

Both data management models present challenges

Companies are struggling to manage data at scale and in the cloud. In a recent Forrester Research study, nearly three-quarters of decision makers said most of their organizations have not yet managed their data in the cloud. 80 percent say they have trouble managing data. A whopping 82 percent cite forecasting and control costs as a challenge in their data ecosystem, and 82 percent say confusing data governance policies are difficult.

Meanwhile, the amount of data companies must manage is mushrooming, and more users are clamoring for more access. “Now you have a lot more data coming from a lot more sources stored in a lot more places,” said Patrick Bark, senior director of product management at Capital One Software.

Organizations want to make this data accessible to more business teams, enabling new insights and added business value. But many struggle to balance the need for centralized management of data in the cloud — which ensures overall management but can hinder data access — with a decentralized model that gives business lines greater control and access to data and analytics. Decentralization, however, has its disadvantages. Different groups may not align on management policies. Certain data or types of data can be stuck in silos, unavailable to all. Machine learning engineers may not have access to the data they need to build advanced analytics tools.

“Your teams want full and instant access to the data and the tools they choose,” says Bark. “You can’t manage everything centrally without being a big bottleneck or hiring data engineers, and you can’t fully distribute the management responsibilities without incurring significant data risks.”

The best of both worlds

However, there is a way to combine centralized and decentralized approaches into a new data management model through data management federation. Doing so allows businesses to understand the benefits of each without compromising.

Capital One, for example, adopted this model when the company closed its data centers and moved operations to the public cloud. The company implemented cloud data storage to make data available to business teams at scale, but realized it needed to focus on data management.

“If you don’t have good governance controls, not only do you have policy management risks, but it’s very quick to spend much, much more money than you need to,” says Bark. “We know that in order to maximize the value of our data, especially the quantity and diversity of data scales, stakeholders involved in activities such as data publishing, data use, management need to create a unified experience through integrated governance. Managing data and underlying infrastructure, so that they all work together seamlessly.



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