Introduction
Compliance frameworks require organizations to track who changed what data and when. Maintaining data quality prevents costly errors, while managing data lifecycle keeps storage costs under control. In Dataverse environments with sensitive records, these three capabilities - auditing, duplicate detection, and data retention - form the operational backbone of data governance.
Scenario: Compliance gaps and data sprawl at Zava
Zava Pay processes thousands of customer transactions daily across its financial services platform. During a recent PCI-DSS audit, the compliance team couldn't demonstrate who modified payment reconciliation records or when customer account details were last changed. The auditors flagged this as a critical gap.
Meanwhile, Zava's retail stores have been onboarding customers independently for years. The same customer frequently exists three or four times with slight name variations - "G. Diaz," "Gabriel Diaz," and "Gabriel Díaz" - each with different purchase histories. Marketing campaigns go to all duplicates, frustrating customers and inflating costs.
To compound matters, Zava's Dataverse environments contain six years of closed transaction records that nobody accesses but that consume expensive database storage capacity. The admin team needs to address three challenges:
- Enable comprehensive auditing so compliance can prove data integrity for regulatory reviews.
- Detect and manage duplicate records so business teams work with clean, reliable data.
- Implement retention policies that move stale data out of active storage without losing it for compliance holds.
What you learn in this module
This module teaches you to configure and manage the data lifecycle in Dataverse environments:
- Enabling and configuring Dataverse auditing at the environment, table, and column level.
- Routing audit data to Microsoft Purview for long-term compliance retention.
- Creating and publishing duplicate detection rules with appropriate match criteria.
- Configuring long-term data retention policies and bulk delete operations.
By the end of this module, you can implement a data governance strategy that satisfies compliance requirements, improves data quality, and optimizes storage costs.