ACTIVE DATA GOVERNANCE

The ever-growing amount and usage of data proposes challenges for consuming data/insights as they were intended. For example, by aligning common definitions of categories or calculations, meeting retention policy guidelines, and accessing data sensitivity.

This internship would focus on finding ways to incorporate data governance best practices into a workflow minimizing manual effort. Nowadays, many companies use data stewards – people who keep track of what data means, how sensitive it is, and so on. But this process is reactive, meaning someone has to constantly watch over the data and take action. Data governance should be a shared responsibility. When a data analyst gets data from a field with a high-sensitivity label, they should be warned before they decide to use this data in a new dashboard that they share with a business group.

Research should thus focus on where to manage data governance, where to produce actionable feedback, and where to check or approve. To stick with the example of the sensitivity label, this could mean that you keep track of sensitivity labels in Purview, that you use co-pilot to warn an analyst when you retrieve data from a field with a high sensitivity, and that you ultimately need approval before you start using a solution that requires this field with a high sensitivity.