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10 do’s and don’ts of data governance

Kelle O'Neal Data governance

Ever feel like you’re spinning your wheels when it comes to implementing a successful (and sustainable) data governance program at your organization?

Don’t worry, you’re not alone.

Many companies repeatedly initiate data governance efforts because their first attempt fell flat or because it’s challenging to sustain the program and demonstrate value over time, particularly within a changing business and personnel environment.

Read on for practical tips to help you focus efforts and limited resources on overcoming common pitfalls and adopting proven best practices for long-term success. 

1. Don’t govern data from within IT

Though IT is often the first to identify the need for data governance, Business is generally the primary creator, fixer and user of that data. Data governance tends to be much more successful when the decision-making regarding the data occurs from within Business.

2. Do recruit a change leader on the Business side

The executive sponsor should be someone who understands the initiative, who believes in it, and fully supports it. He or she must be able to effectively communicate with and engage other leaders in support of the changes. 

3. Don’t govern data in silos

When data issues arise within an individual business unit or line of business, it’s understandable that the tendency may be to address them within that unit. And while this approach of confining the data governance program within an individual unit may satisfy that unit’s internal governance needs, more problems can arise when—and because—data is shared across different business groups.

4. Do establish enterprise data governance

Data governance is most successful when an organization governs data as an enterprise asset. The cross-functional steering committee can ensure shared definitions are consistent across groups and that value is created across the enterprise, promoting the guiding principle of "thinking globally but acting locally." 

5. Don’t assume everyone understands the value of data

While some stakeholders in an organization are highly involved in or at least aware of all that goes into fixing data errors and appreciate the value of that data to the organization, others may be immune to it. Only seeing the cleaned data, but not the resources consumed to get it to that trusted state, they may have less appreciation for its value.

6. Do communicate early and often about the impact of poor data quality and the benefits of governance

Whether your data governance program has been recently deployed or has already matured into a going concern in your organization, consistent and impactful communication plays a critical role in translating data value into business value. The change leader and sponsor identified in the second best practice should play a central role in this communication.

7. Don’t use meaningless metrics

A metric that is meaningful to one group can be meaningless to another. Metrics have no value if they are not aligned to the interests of stakeholders, so ensure there is some way of measuring how improvements to data (and to the governance of that data) are helping them progress toward their goals and be sure to translate the value statement into their own language.

8. Do measure impact and progress

It is important to measure (and communicate!) both impact and progress, and to translate those metrics into business value. It’s not enough to merely track the metrics; you should also understand how the progress metric, such as a reduction in data errors, translates into improvements in the business, a KPI.

9. Don’t treat data governance as a project 

Most companies are project-driven. They identify what they want to accomplish, plan the approach, acquire funding and resources, and plot a timeline with milestones before executing. But treating data governance as a project creates the expectation that there is a finite timeline with an endpoint, finite funding and finite participation. Data governance cannot be sustained without ongoing resources and support.

10. Do embed data governance into your culture and operations

Ensure from the start that your operating model fits the culture of your company. Simplicity and usability are essential for acceptance, and it’s critical for organizations to fully integrate data governance into their operations so it continues to receive necessary funding and attention.

 Implementing a successful data governance program at your organization is no small task, but the benefits of making more data-driven decisions will be felt across every area of the business. ­­­Following these do’s and don’ts should set your data governance program on a path to success. 

Data integrity is a key component of effective data governance. Good data integrity means accurate and consistent data.

Contact us to help ensure your data integrity