Are your data models supporting data governance? Most data professionals have been involved in data governance programs in our careers. These programs range from light governance to staffed, well-funded C-level offices to ensure data quality and data protection for the enterprise.
No matter what level of data governance you have been involved with, data models provide a vital resource for successful governance programs. In this work, we look at the following data modeling features that need to be part of the data modeling portfolio of an enterprise to ensure compliance with better data governance processes.
We complete data governance tasks faster with better data models. When teams are working toward the same goal, we complete both individual and collaborative tasks faster. Logical data models that have had strong engagement data stewards comprise hundreds of hours of decisions made by business users and data professionals. The results can save a significant amount of data governance time.
Collaborative tasks are easier. When there is less contention and more trust among team members, tasks are easier to complete because there are fewer distractions. Happier teams have better outcomes.
Data model quality is data quality. The quality of data models has a direct impact on data quality. Data models, as requirements and specifications for data, are the standards against which we measure data quality.
Barebones data models, often just diagrams of databases, do not aid in better data governance.
How mature data governance is in an organization affects their ability to succeed. Good data modeling also affects how and where we complete data governance work. Collaboration, usability, completeness all form vital components of data governance efficiency. Within this harmonious ecosystem, as data stewards define standards of data, data modelers can ensure that data assets deliver against those standards.
Are you new to data governance? An organization starting its path towards formal data governance might enhance its logical data models with necessary metadata around data stewards and sensitivity levels.
This organization might also start a business glossary and implementing a data model and standards portal. New data governance initiatives can leverage such knowledge from mature data modeling assets.
Do you need more mature data governance? A more mature organization with a formal, enterprise-wide data governance program might manage these items in other systems and publishing the results to their data models and portals. They might also document new data governance items in their models, then publishing those out to their formal data governance tools. Where we execute the work is less important than the fact that the activities are happening and being recorded then shared.
Leverage your data models to support data governance. No matter where your organization fits in these maturity models, your data models play a crucial role in ensuring your data governance is engaging and successful.
Read the 16-page whitepaper “Five Data Modeling Tips for Better Data Governance” by Karen López to learn more about how better data models result in better data governance.