Compliance means conforming to standards and obeying the rules. Some of these rules will require manual methods for oversight and review. However, many will be discrete assertions about data values (such as “the value must be one of the 2-character abbreviations for States of the United States”) and about relationships between data values (such as “the completion date must be later than the start date”). These types of data rules are candidates for automated validation using data profiling and assessment tools. Review the different standards and rules, determine whether they can be automatically validated, and determine the best methods for using tools to monitor and report data standard and rule compliance.

The purpose of data compliance is to ensure that data management and data usage activities operate within the constraints that are prescribed by laws, regulations, contracts, and policies. Much of modern data compliance focuses on laws and regulations regarding collection and use of data, such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and many others. Rapidly expanding regulations are one of the pressing challenges of modern data governance.