Definitions: Data Governance and Data Domains

It is important to have rules and to follow them. But the reason for the rules and the benefits they provide are more important than having boxes to check. The data governance program at the University of Rochester will be about providing guidance and frameworks to help people do the right thing with data instead of focusing on making sure no one ever does the wrong thing. Responsibility and accountability are very important parts of data governance but so are opportunity and efficiency. The balance of understanding who has the authority to do what with data with the insight of what can be achieved when more people have access and understanding of more data is a prime goal for the data governance program.

Technology is a key enabler to all data activities but successful data governance requires the separation of the data content from the platform or system where it resides. The concept of “employee” exists whether data about employees is in a payroll system or somewhere else. Consistent definitions for key university data elements need to be created and used where ever the data live so data governance conversations will focus on the content not the platforms.

All data need to be governed but not all data need the same type or level of governance. Key data assets for the university that underlie critical business processes will have stricter controls and more tightly focused definitions than local data that are created for occasional ad hoc analytics. Research data require a different kind of governance entirely but they do need governance.