How to Turn KPIs into Data Governance Allies?

It is no secret that convincing employees to take on data governance tasks can be challenging. These responsibilities often add to their already busy schedules and objectives. Moreover, the value of data governance often becomes apparent only after reaching a certain threshold—a minimum level of adoption and content is necessary. So, how can organisations encourage widespread adoption of these crucial practices?

The Role of KPIs in Data Governance

One practical approach to enhance and adopt data governance practices within an organisation is to integrate Key Performance Indicators (KPIs) at the departmental level and cascade them down to all employees within the organisation. 

To provide concrete examples, KPIs that organisations might implement could include the measurement of the quality of key data in each data domain, the number of terms documented in the Business Glossary, the number of fully documented reports, and the number of certified data stewards and data owners through internal training. Of course, these are just a few possibilities—there are thousands of potential KPIs to consider.

However, this approach has its challenges. For instance, KPIs have never been a form of intrinsic motivation. Employees might see these metrics as additional burdens rather than opportunities for personal growth or organisational improvement. Moreover, resistance from certain departments where data governance is particularly problematic can hinder the implementation process. 

Yet, there are notable benefits to consider. Firstly, KPIs should serve the primary objective of ensuring the success of the data governance program. In terms of program management and monitoring its health, they enable agile identification of areas needing intervention for success. Moreover, KPIs significantly boost awareness, supporting change management by making data governance highly visible within the organisation. This visibility can drive collective efforts toward better data management.
 

Best Practices for Implementing Data Governance KPIs

If you choose to go ahead, implementing data governance KPIs effectively requires a few checks to avoid falling into pitfalls. 

• SMART KPIs: Ensure KPIs are specific, measurable, achievable, relevant, and time-bound. Unrealistic or misaligned KPIs can demotivate employees and derail data governance efforts. 
• Continuous Evaluation: Regularly review and update KPIs to reflect changes in business objectives or data management technologies.
• Employee Involvement: Involve employees in KPIs' development and implementation phases, which ensures their buy-in and understanding of the goals. 
• Clear Communication: It is crucial to clearly communicate KPIs' importance and objectives, maintain transparency about the progress, and make necessary adjustments based on feedback. 
• Empowered Data Stewards: Data stewards and owners must understand KPI goals and be adept at collecting, analysing, and communicating them. They should also be skilled in key data management techniques such as data modelling, quality management, and security practices.
   

Beyond Setting Metrics

While KPIs provide a structured approach to measuring data governance success, two additional elements should also be considered for effective implementation: a collaborative culture and advanced technology.

There are numerous technologies available to assist organisations in implementing data governance, such as data management tools, data quality software, and data security solutions. Using these tools correctly can make a significant difference in the success of the data governance program. However, they should be used wisely from the data management program cockpit. None of these tools provide KPIs or dashboards specifically for data governance program management. This oversight is crucial to address, as effective program management relies on having a clear view of KPIs and dashboards tailored to data governance.

Finally, by breaking down silos and encouraging cross-functional cooperation, organisations can optimise data management efforts and achieve better outcomes. Promoting a collaborative culture across departments ensures that data governance efforts are both effective and efficient. 
 

Conclusion

Data governance KPIs are a powerful tool for driving organisational change and improving data quality, when following best practices and addressing potential challenges. 

To further enrich this discussion, we would love for you to share your experience. Do you have examples of companies or industries where implementing data governance KPIs has led to significant improvements? How do you recommend overcoming resistance from departments that are hesitant to adopt data governance practices? 

Looking forward to hearing your thoughts on the matter!