Prematurely heralding or launching data governance is a common mistake many early adopters learned the hard way. Companies that fail to launch data governance successfully typically find the second go around a tough sell. We have multiple anecdotes from clients who say: “We still need data governance, but we can’t call it that anymore.”

To help avoid this pitfall, this article presents five points to ponder when evaluating (or re-evaluating) your organization’s readiness and willingness for data governance.

#1: Can you define it?

Baseline defines data governance as the “organizing framework for establishing strategy, objectives, and policies for corporate data.” Other commonly acknowledged definitions reference frameworks, processes, and decision rights as well. However, the term data governance has also been applied to a smorgasbord of other methods and processes including information management, data management, data stewardship and data quality.

That being said, don’t get too wrapped up in the hype. It is less important to reconcile the myriad definitions than it is to have a single, sanctioned definition that resonates with your enterprise. At the end of the day, the best definition – as well as the scope and complexity of your data governance program – depends squarely on your corporate culture and the problem being solved. This brings us to our next question.

#2: Can you clearly articulate the need, pain, or problem?

A clear definition establishes a common vocabulary and grounding. A definition does not paint the picture of why your company needs data governance, the gains to be made or pitfalls and risks that governance will avert.

Clearly defining the problem space and articulating the benefits of a data governance program are crucial to gain support and funding. Data governance can be applied to a myriad of business problems and strategies from the need for regulatory reporting and compliance to business intelligence (BI), customer relationship management (CRM), and master data management (MDM). Avoid the impulse to justify data governance in the context of any and all candidates. The scope of the program will be too overwhelming.
 Instead, can you identify a single pain point or priority project that can initially benefit from or requires data governance? Even better, can you identify how the initial work can be leveraged and extended for subsequent initiatives? If the answer is “No,” a formal data governance program (vs. a project-oriented activity) may not be warranted.

Also remember that data governance is a means to an end, not process for process’ sake. Data governance is typically justified as a key enabler for corporate strategies such as MDM or BI or as a method to mitigate risk (in the case of regulatory or other compliance drivers). In fact, the extent to which data governance is aligned with a strategic initiative or highly visible tactical project is a key predictor of success.

#3: Are the (to-be) governors and governed in agreement?

A mature governance program requires collaboration. Requisite participants must minimally agree on the problem and participate in the solution. Governance efforts perceived as an organizational power grab or mechanism to shift or shirk responsibility are doomed to fail.
Take the following real-life problem statements from each side of the business-IT divide:

• Business Sponsor: “IT seems to make it up as they go. How many times do we have to tell them what ‘revenue’ is? Data governance will make IT accountable for delivering what we’ve asked for (…finally).”
• IT Sponsor: “The business is never satisfied with the (data/dashboard/scorecards) we deliver. They don’t believe the data or think it is wrong but can’t tell us why. Data governance will make the business accountable for the definition and quality of the data (…finally).”
  

In both these cases, the sponsoring organization sought data governance as a method to forcibly correct a perceived lack of process or accountability in a counterpart. Data governance was a viable solution to problems plaguing both BI programs. However, neither program was able to gain traction initially due to the way the problem and solution were framed.

#4: Can you execute?

Data governance processes and the information policies and standards they generate are useless as thought or paper exercises. For example, consider a clearly articulated usage and access policy for sharing and protecting a customer’s personally identifiable information (PII). To give data governance teeth, the enterprise must:

• Implement appropriate procedures and methods to secure PII data within applications.
• Validate requests for access to PII data based on a user’s need to know and intended usage.
• Monitor and validate compliance on an ongoing basis.
• Define a process for arbitrating disputes and resolving compliance breaches.
  

Similarly, consider the development of well articulated definitions for common corporate key performance indicators (KPIs) by duly appointed, cross-functional business constituents. The effort expended is a wasted exercise if the output is not communicated to the community at large or doesn’t translate into your BI analytic and reporting applications.

Defining, executing and enforcing information policies and standards will not happen overnight. A deliberate, iterative road map and plan as well as organizational commitment and fortitude are required.

This brings us to the last question.

#5: Will anybody care?

At first blush, data governance (somewhat by definition) is often perceived as additional bureaucracy or overhead. Therefore, careful communication and marketing matter.

Not to put too fine a point on it, but you need to put sponsors in a position where they can’t say no. For example: “We need a consistent definition and method for calculating externally reported financial metrics.” Can you think of a CFO who will disagree with this statement? Assuming the CFO’s head nods, data governance provides the method to define and enforce metric definitions. The end result is regulatory compliance and avoidance of (significant) fees and penalties imposed when violations occur.

A word of warning: visionaries draw the correlation between data governance and an existing or looming pain point before their cohorts recognize the problem and/or agree to the solution. The organization may not be ready to step up to the plate immediately. In some cases, more education is required. In others, the organization may not have experienced a level of pain or exposure that makes the need for data governance self-evident and unassailable. If this resonates with you, consider approaching data governance in a more tactical or “bottom-up” approach by embedding some initial capabilities within an existing project or process. Alternatively, keep searching for those data-enabled business issues that will capture the attention and cooperation of a few key business and/or technical stakeholders.

Sometimes, this means biding your time.

Conclusion

Data governance remains a tough nut to crack for many companies. Assessing your need and readiness with a realistic and sober eye is the first step to determining whether your enterprise (not just you) is really ready for data governance.

Were you able to answer ”Yes” to the questions posed above? If so, welcome to the fray! If not, additional discovery work may be required before launching your data governance program.