Data governance scorecard is another tool for measuring and monitoring the progress of your data governance program. For a quick reminder, the first tool I covered was the maturity model.
What is a data governance scorecard?
A data governance scorecard is a collection of agreed-upon baseline and metrics reported on a regular basis, usually monthly, quarterly, and/ or yearly, to the data governance program sponsor and stakeholders.
- It is defined at enterprise or business level, usually depending on your operating model
- It is regularly measured and reported
- Can be split into individual business data steward scorecards or project level scorecards
If you want to know about other evaluation tools, please read the data governance maturity models article, too.
A data governance scorecard can be quite detailed as it includes various categories, each with their own objectives and Key Performance Indicators (KPIs). There is no standard into what these categories should be as it depends on your own implementation, industry, culture, and business needs. That being said, here are the categories I recommend tracking against, as they are agnostic to a data governance maturity model and can be used in parallel or adapted to any model you choose to utilize:
- Purpose: Measures the data skills, training, and overall organizational maturity of its data governance program
- Sample objectives: Increase data skills, increase data governance awareness & maturity
- Sample KPIs: # of certified data stewards, employee satisfaction rate
2. Data infrastructure
- Purpose: Measures the progress on physical data storage, systems and applications
- Sample objectives: Reduce legacy databases, improve master data usage
- Sample KPIs: # of systems using master data, % reduction in total cost of ownership
3. Data controls
- Purpose: Measures the organization’s compliance over its data standards, including the completeness of its metadata
- Sample objectives: Automate data controls and audits, increase compliance to data standards
- Sample KPIs: Compliance test results, % decrease in policy failure
4. Data quality
- Purpose: Measures improvements in data quality dimensions
- Sample objectives: Improve data quality dimensions
- Sample KPIs: % improvement in data completeness, data accuracy rate
- Purpose: Tracks the return of investment of data governance at project and program level
- Sample objectives: Ensure costs are in line with budget
- Sample KPIs: Total budget to actuals, % of operating budget savings
In the scorecard template below, I’ve outlined a more complete set of objectives and potential KPIs you can choose to track. For each category I recommend providing an yearly measurement, with initial targets for each of the next three years. Please keep in mind this is a living document which you can improve at any time.