Data Governance, Quality, and Controls
This chapter covers keeping data trustworthy and secure. You will learn the dimensions of data quality, master data management, privacy and access controls, and data classification and retention.
Data quality dimensions
Quality is assessed across several dimensions. Accuracy is whether values are correct, completeness is whether all required values are present, consistency is whether data agrees across systems, and timeliness is whether data is current enough for its use. Measuring these dimensions helps organizations trust their data and target improvements where they matter most.
Master data management
Master data management (MDM) establishes a single, authoritative source for core business entities such as customers, products, and suppliers. By ensuring every system references the same trusted definition, MDM reduces conflicting records and improves the reliability of analysis and reporting across the organization.
Privacy, security, and access controls
Sensitive data such as personally identifiable information (PII) must be protected. Role-based access controls restrict who can view or modify data based on their role, encryption protects data at rest and in transit, and masking hides sensitive fields from users who do not need them. These controls support both security and regulatory compliance.
Classification and retention
Data classification labels data by sensitivity, such as public, internal, or confidential, so the right controls can be applied. A retention policy defines how long each type of data is kept and when it must be securely disposed of, balancing business and legal needs against the risk and cost of holding data longer than necessary.