The scoreboard, and what is not on it. The hard targets, how often each is actually
looked at, and the quiet signals that never reach a dashboard.
Data Quality Incident Resolution Time
The average time it takes for you to investigate and resolve identified data quality issues.
Target · 90% of critical incidents resolved within 3 business days If a critical data quality alert comes in on Monday, you'll have it investigated, root cause identified, and a resolution plan in place by Wednesday afternoon. For example, a sudden drop in customer records in a key table is flagged, and you identify a failed ETL job within 24 hours.
Business Glossary & Data Dictionary Coverage
The number of key business terms and technical data elements that have been clearly defined and documented in our data catalogue.
Target · Add 50-75 new, certified terms/elements per quarter By the end of Q2, you'll have worked with the Marketing team to define and document 'Customer Lifetime Value' and its underlying data points in the data catalogue, making sure it's linked to the correct tables and columns.
Data Stewardship Task Completion Rate
How consistently you complete assigned data stewardship tasks, like reviewing data quality reports or validating new data definitions.
Target · 95% completion rate for assigned tasks within their due dates You'll have five data quality reports to review each week, and you'll consistently sign them off or flag issues before Friday. For instance, you'll check the weekly 'Product Category' data quality report, ensuring all new product entries have valid categories and escalating any anomalies.
Data Lineage Documentation Accuracy
The accuracy and completeness of the data lineage you document for specific data assets.
Target · 100% accuracy for assigned critical data elements (CDEs) When documenting the lineage for our 'Customer ID' CDE, you'll trace it from the CRM system through our data warehouse transformations to the final reporting layer, ensuring every step and system is accurately recorded and verified.
Proactive Issue Identification
How well you spot potential data issues before they become major problems, rather than just reacting to alerts.
- You'll regularly bring up potential data quality risks in team meetings, suggesting preventative measures. You'll flag inconsistencies you notice during routine data profiling, even if no one's formally reported them yet. People will say, 'Oh, good catch, I hadn't thought of that!'
Clarity of Communication
Your ability to explain complex data governance concepts and standards in a way that technical and non-technical people can easily understand.
- Your documentation is easy to follow, even for new joiners. When you present, people ask fewer clarifying questions than usual. You can explain the difference between a business glossary and a data dictionary to a marketing executive without them glazing over. Colleagues will often ask you to 'translate' technical jargon for them.
Collaboration & Consensus Building
How effectively you work with different teams to agree on data standards and resolve conflicting definitions.
- You'll successfully mediate discussions between two teams who disagree on a data definition, leading to a documented agreement. Team members from other departments will willingly participate in your data stewardship meetings. You're seen as someone who helps bridge gaps, not just enforce rules.
Contribution to Data Governance Standards
Your input into improving our internal data governance processes, templates, and guidelines.
- You'll propose practical improvements to our data definition templates or data quality rule creation process. You'll volunteer to pilot new approaches for documenting data lineage, sharing your learnings with the team. Your suggestions are often adopted because they genuinely make things easier or clearer.
