Five Pillars of Data Governance Readiness: Organizational Support

It’s important that business leaders foster organizational support for their data governance efforts.

The clock is counting down to the May 25 effective date for the General Data Protection Regulation (GDPR). With the deadline just a stone’s throw away, organizations need to ensure they are data governance-ready.

We’re continuing our blog series on the Five Pillars of Data Governance (DG). Today, we’ll explore the second pillar of data governance, organizational support, and why it’s essential to ensuring DG success.

In the modern, data-driven business world, data is an organization’s most valuable asset, and successful organizations treat it as such. In this respect, we can see data governance as a form of asset maintenance.

Take a production line in a manufacturing facility, for example. Organizations understand that equipment maintenance is an important and on-going process. They require employees using the equipment to be properly trained, ensuring it is clean, safe and working accordingly with no misuse.

They do this because they know that maintenance can prevent, or at the very least postpone repair that can be costly and lead to lost revenue during downtime.

Organizational Support: Production Lines of Information

Despite the intangible nature of data, the same ideas for maintaining physical assets can and should be applied. After all, data-driven businesses are essentially data production lines of information. Data is created and moved through the pipeline/organization, eventually driving revenue.

In that respect – as with machinery on a production line and those who use it – everybody that uses data should be involved in maintaining and governing it.

Poor data governance leads to similar problems as poor maintenance of a production line. If it’s not well-kept, the fallout can permeate throughout the whole business.

If a DG initiative is failing, data discovery becomes more difficult, slowing down data’s journey through the pipeline.

Inconsistencies in a business glossary lead to data units with poor or no context. This in turn leads to data units that the relevant users don’t know how to put together to create information worth using.

Additionally, and perhaps most damning, if an organization has poorly managed systems of permissions, the wrong people can access data. This could lead to unapproved changes, or in light of GDPR, serious fines – and ultimately diminished customer trust, falling stock prices and tarnished brands.

Facebook has provided a timely reminder of the importance of data governance and the potential scale of fallout should its importance be understated. Facebook’s lack of understanding as to how third-party vendors could use and were using its data landed them in hot PR water (to put it lightly).

Reports indicate 50 million users were affected, and although this is nowhere near the biggest leak in history (or even in recent history, see: Equifax), it’s proof that the reputational damage of a data breach is extensive. And with GDPR fast approaching, that cost will only escalate.

At the very least, organization’s need to demonstrate that they’ve taken the necessary steps to prevent such breaches. This requires understanding what data they currently have, where it is, and also how it may be used by any third parties with access. This is where data governance comes in, but for it to work, many organizations need a culture change.

A Change in Culture

Fostering organizational support for data governance might require a change in organizational culture.

This is especially apparent in organizations that have only adopted the Data Governance 1.0 approach in which DG is siloed from the wider organization and viewed as an “IT-problem.” Such an approach denies data governance initiatives the business contexts needed to function in a data-driven organization.

Data governance is based primarily on three bodies of knowledge: the data dictionary, business glossary and data usage catalog. For these three bodies of knowledge to be complete, they need input from the wider business.

In fact, countless past cases of failed DG implementations can be attributed to organizations lacking organizational support for data governance.

For example, leaving IT to document and assemble a business glossary naturally leads to inconsistencies. In this case, IT departments are tasked with creating a business glossary for terms they often aren’t aware of, don’t understand the context of, or don’t recognize the applications or implications for.

This approach preemptively dooms the initiative, ruling out the value-adding benefits of mature data governance initiatives from the onset.

In erwin’s 2018 State of Data Governance Report, it found that IT departments continue to foot the bill for data governance at 40% of organizations. Budget for data governance comes from the audit and compliance function at 20% of organizations, while the business covers the bill at just 8% of the companies surveyed.

To avoid the aforementioned pitfalls, business leaders need to instill a culture of data governance throughout the organization. This means viewing DG as a strategic initiative and investing in it with inherent organizational and financial support as an on-going practice.

To that end, organizations tend to overvalue the things that can be measured and undervalue the things that cannot. Most organizations want to quantify the value of data governance. As part of a culture shift, organizations should develop a business case for an enterprise data governance initiative that includes calculations for ROI.

By limiting its investment to departmental budgets, data governance must contend with other departmental priorities. As a long-term initiative, it often will lose out to short-term gains.

Of course, this means business leaders need to be heavily invested and involved in data governance themselves – a pillar of data governance readiness in its own right.

Ideally, organizations should implement a collaborative data governance solution to facilitate the organization-wide effort needed to make DG work.

Collaborative in the sense of enabling inter-departmental collaboration so the whole organization’s data assets can be accounted for, but also  in the sense that it works with the other tools that make data governance effective and sustainable – e.g., enterprise architecture, data modeling and business process.

We call this all-encompassing approach to DG an ‘enterprise data governance experience’ or ‘EDGE.’ It’s the Data Governance 2.0 approach, made to reflect how data can be used within the modern enterprise for greater control, context, collaboration and value creation.