Measuring the performance of a data governance program is difficult as it involves not only tools but the plan of new responsibilities, processes, and expectations. Therefore, there is a need to consider different metric types that reveal data governance complexity in distinct ways. Metrics are not just hard to grasp, but are a crucial aspect of a data governance program.
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How do metrics reveal data governance value?
Metrics can reveal whether you are aligned with business strategy or not. It ensures that silos in the business process are identified and aligned. Metrics can consistently clarify the significance and value of data governance initiatives. You will find that over time the definition of alignment and relevance will change as strategy evolves, which means even your metrics will change.
Not every metric is KPI or Key Performance Indicator, so it is essential to identify the type of crucial metrics. First identify your problems, goals, and impact, which will help to choose the type of metrics.
Progress and impact
The two main perspectives to measure are – progress and impact. Progress towards defined goals and the impact of that progress can be measured. Start with progress measurement.
How to measure progress metrics?
- People – Consider the tasks and activities associated with assigning, aligning, and onboarding personnel in the data governance program. In addition, the people who are already trained and already participating. Track the number of data owners identified, number of resolved problems, number of approved projects, and program adoption rate.
- Processes – Consider measuring the processes that can optimistically affect your goals. Understand how processes are formed, how to implement and execute them. Execution depends on adoption, so find out if the people understand the new processes or are they using it or working with outdated ones. Metrics will be accessible from tracking approved & implemented standards, data consolidated processes, consistent data definition, and how the definitions are applied within different processes.
- Technology – Data integrity will define the technology implementation progress. Numbers from metrics like data sources incorporated, mastered data usage, unique identifiers [KPI], and duplicate products can be used.
- Data – Data progress includes looking at the common data entities and how they are managed or documented. Data quality dimensions help to identify process efficiency. Data quality metrics will affect productivity.
How to measure impact metrics?
Establish impact metrics by asking questions to uncover problems, set goals to settle the problems, and define ways to measure impact to attain the goals. Prioritize the problems and clarify each one through discussion. Concentrate on what change you desire to see and think if the change will make a difference. Keep track of the change and soon you will get a measurable outcome. The outcome is the impact of data change done to resolve a business silo.
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