Using governance to spur, not stall, data access for analytics

In this way, governance is planned and implemented to create competitive advantage, addressing policy compliance, security, accessibility, and ease of use in a comprehensive and frictionless manner. This, in turn, speeds up data availability and increases usability for distributed team members – while maintaining central control over risk. Although common data management practices present obstacles for companies, this combination of models can overcome those obstacles.

Both models of data governance pose challenges

Companies struggle to manage data at scale and in the cloud. Nearly three quarters of decision makers in A A recent survey by Forrester Research They say they don’t yet manage most of their organization’s data in the cloud. About 80 percent say they find it difficult to manage data at scale. 82 percent indicated that forecasting and controlling costs is a challenge in their data ecosystem, and 82 percent say confusing data governance policies are a difficulty.

In the meantime, the amount of data companies have to manage is growing, and more users are demanding more access to it. “You now have more data coming in from many other sources and it is stored in many other places,” says Patrick Barch, senior director of product management at Capital One Software.

Organizations want to make this data accessible to more teams, enabling new insights and greater business value. However, many struggle to balance the need for centralized management of data in the cloud – which ensures comprehensive governance but can impede access to data – with a decentralized model that gives lines of business more control and access to data and analytics. However, decentralization has its drawbacks. Different teams may not align with governance policies. Certain data or types of data can get stuck in repositories, not available to everyone. Machine learning engineers may lack access to the data they need to create advanced analysis tools.

“Your teams want complete and immediate access to the data and tools of their choice,” says Barch. “You can’t centrally manage everything without becoming a big bottleneck or hiring an army of data engineers, and you can’t completely decentralize management responsibility without taking big data risks.”

The best of both worlds

However, there is a way to combine centralized and decentralized approaches into a new data management paradigm through the Data Management Consortium. Doing so enables companies to realize the advantages of each, without the disadvantages.

For example, Capital One embraced this model while the company shut down its data centers and moved operations to the public cloud. The company implemented a cloud data vault to make data widely available to teams, yet it recognized that it needed to pay attention to data governance.

“Without good governance controls, you not only have the risks of running policy, but you also risk spending a lot more money than you intend, and more quickly,” Barch says. “We knew that maximizing the value of our data, especially in terms of the quantity and diversity of data metrics, would require creating integrated experiences with integrated governance that enabled the various stakeholders involved in activities such as data deployment, data consumption, data control and management of the underlying infrastructure, to work together seamlessly.”

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