How can you scale and flexibly manage data governance metrics and KPIs?

3 Establish data governance roles and responsibilities

A third step to scale and flexibly manage your data governance metrics and KPIs is to establish clear data governance roles and responsibilities for your data stakeholders. This will help you assign ownership, accountability, and authority for your data assets and policies, as well as for your data governance metrics and KPIs. You can use frameworks such as RACI (responsible, accountable, consulted, and informed) or DACI (driver, approver, contributor, and informed) to define your data governance roles and responsibilities and communicate them to your data stakeholders.

4 Review and update metrics and KPIs regularly

A fourth step to scale and flexibly manage your data governance metrics and KPIs is to review and update them regularly. This will help you ensure that your metrics and KPIs are still relevant, accurate, and actionable for your data governance objectives and outcomes. You can use feedback loops, surveys, or dashboards to collect and analyze your data governance metrics and KPIs and identify areas of improvement or change. You can also use agile methods, such as sprints or iterations, to review and update your metrics and KPIs frequently and flexibly.

5 Leverage best practices and standards

A fifth step to scale and flexibly manage your data governance metrics and KPIs is to leverage best practices and standards from your industry or domain. This will help you benchmark your data governance performance and maturity against your peers or competitors, as well as adhere to regulatory or compliance requirements. You can use frameworks such as DAMA-DMBOK (Data Management Body of Knowledge) or ISO 8000 (Data Quality Standards) to guide your data governance metrics and KPIs and adopt best practices and standards from your industry or domain.

6 Customize metrics and KPIs to your context

A sixth step to scale and flexibly manage your data governance metrics and KPIs is to customize them to your specific context and needs. This will help you address the unique challenges and opportunities of your data architecture, as well as the preferences and expectations of your data stakeholders. You can use methods such as segmentation, personalization, or experimentation to customize your data governance metrics and KPIs and tailor them to your context and needs.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?