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.
The General Data Protection Regulation (GDPR) contributed significantly to data governance’s escalating prominence. In fact, erwin’s first report on “the state of data governance” issued ahead of the regulation’s effective date in May 2018 found that 60% of organizations considered regulatory compliance to be their biggest driver of data governance.
However, our most recent analyses indicate enterprises are shifting to a more mature and robust view of data governance’s benefits and importance. Better decision-making took the top spot in our second such study, with 62% of respondents citing it as the primary driver behind their data governance initiatives. And the “2021 State of Data Governance and Empowerment” report shows that data security and data quality are now the primary drivers.
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.
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.
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 solutions, including erwin Data Catalog and erwin Data Literacy, help you see your data in a whole new light. Beyond compliance, the benefits of collaborative data governance are numerous and include: