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What Is Data Governance?

As the strategic layer connecting data and business architectures, data governance is everyone’s business

Most organizations use data to make decisions. All intelligent enterprises certainly do. Because data is everyone’s business, the best outcomes happen when data is handled consistently across your organization. That means data serves the dual needs of IT (as the data keepers) and the rest of your business (as the data users). This strategic, collaborative and ongoing practice is known as data governance.

Defining Data Governance

Data governance is one of the fastest growing disciplines, but many organizations struggle to define exactly what it is. According to Dataversity, data governance is “the practices and processes which help to ensure the formal management of data assets within an organization.”

At erwin, we break this definition down further and view data governance as a strategic, continuous commitment to ensuring organizations are able to discover and track data, accurately place it within the appropriate business context(s), and maximize its security, quality and value. Across your entire organization, data must be accessible, consistent and usable to drive accountability and meaningful insights.

However, the relevant processes, practices and contexts for data governance will vary widely from one business to another. This means your organization must arrive at its own unique definition – one that is specific to its needs. The best way to develop this understanding is to consider the primary factors that are driving adoption for your business.

Decision-Making, GDPR Compliance and Other Leading Drivers for Data Governance

In our 2020 State of Data Governance and Automation report, we uncovered a shifting landscape when it comes to the primary drivers of data governance.

Just a couple of years ago, the General Data Protection Regulation (GDPR) contributed significantly to data governance’s escalating prominence. In fact, erwin’s 2018 report on the same topic found that 60% of organizations considered regulatory compliance to be their biggest driver of data governance.

However, our latest analysis indicates that enterprises are shifting to a more mature and robust view of data governance’s benefits and importance. Better decision-making took the top spot, with 62% of respondents citing it as the primary driver behind their data governance initiatives. Analytics (51%) and regulatory compliance (48%) rounded out the top three, while digital transformation (37%) and data standards/uniformity (36%) also proved to be important drivers.





Data Standards
& Uniformity

But adopting data governance is of little benefit without understanding how it should be applied within these contexts. A great place to start when defining an organization-wide data governance strategy is to consider the desired business outcomes. This approach ensures that all relevant parties have a common goal, which has historically been a challenge for data governance initiatives.

The Evolution of Data Governance: DG 1.0 vs DG 2.0

With no set definition and the historical practice of isolating data governance within IT, organizations often have had different ideas of what data governance is – even between departments. With this inter-departmental disconnect, it’s not hard to imagine why data governance has historically left a lot to be desired.

Data Governance 1.0

Past examples of Data Governance 1.0 were mainly concerned with cataloging data to support search and discovery. The nature of this approach, coupled with the fact that data governance initiatives were typically siloed in IT departments – without input from the wider business – meant the practice often struggled to add value.

Without those key inputs, the data cataloging process suffered from a lack of context. By neglecting to include the organization’s primary data citizens – those who manage and or leverage data on a day-to-day basis for analysis and insight – organizational data was often plagued by duplications, inconsistencies and poor quality.

Therefore, Data Governance 1.0 initiatives fizzled out at discouraging frequency.

This is, of course, problematic for organizations that identify regulatory compliance as a driver of data governance. Considering the nature of data-driven business – with new data being constantly captured, stored and leveraged – meeting compliance standards can’t be viewed as a one-time fix. Data governance simply can’t be deprioritized and left to fizzle out.

Data Governance 2.0

We recommend organizations look beyond just data cataloging and compliance as desired outcomes when implementing data governance. In the data-driven business era, organizations should consider a Data Governance 2.0 strategy. This approach transforms data governance into an organization-wide initiative that de-silos the practice from the confines of IT to add value in new areas.

This collaborative take on data governance intrinsically involves data’s biggest beneficiaries and users in the governance process, so historically important functions like data cataloging also benefit from greater context, accuracy and consistency.

It also means that organizations can have greater trust in their data and be more assured of meeting the standards set for regulatory compliance. It means that organizations can better respond to customer needs through more accurate methods of profiling and analysis, improving rates of satisfaction. And it means that organizations are less likely to suffer data breaches and their associated damages.

How Can Data Governance Tools Benefit Your Business?

Because regulatory compliance is a primary driver of data governance initiatives, it’s easy enough to understand why you need to use a comprehensive data governance tool. But it’s also important to understand why you should be using one.

erwin’s data governance solution, the erwin Data Intelligence Suite with erwin Data Catalog and erwin Data Literacy, helps you see your data in a whole new light. Beyond compliance, the benefits of collaborative data governance are numerous and include:

  • Reduced costs for managing, understanding and leveraging data
  • Automation that supports a faster, more accurate data pipeline to fuel insights
  • Massive increases in the value of your organization’s data
  • Standardized systems, policies and procedures for handling data
  • Support for digital transformation and other scaling initiatives

See for yourself why erwin is the data governance company. We’ve been recognized by Gartner, IDC and industry publications for our innovations, particularly in metadata management that’s foundational to mature and sustainable data governance.

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