Data Governance, Quality, and Controls
Data Governance, Quality, and Controls
Governance is the system of decision rights and accountability for data. Know the roles: the data owner is accountable for a domain and sets policy; the data steward implements standards and resolves quality issues day to day; the data custodian operates the technical storage and backups. Confusing owner and steward is a common trap.
Know the control families. Data classification tags data as public, internal, confidential, or restricted, and drives handling rules for PII, PHI, and card data. Privacy regulations such as GDPR and CCPA impose lawful-basis, consent, and deletion requirements; HIPAA governs protected health information. Security controls include encryption at rest and in transit, masking, tokenization, role-based access control, and audit logging. Retention schedules define how long data is kept, and destruction must be verifiable.
Data quality has dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Match the scenario to the dimension - a missing customer ID is completeness, a value of "13" in a month field is validity, and two systems disagreeing on an address is consistency. Related concepts include master data management, metadata, data lineage, data catalogs, and data dictionaries.
Traps: treating anonymization and pseudonymization as identical (pseudonymized data can be re-linked with a key); assuming encryption satisfies retention policy; and forgetting that governance includes change management and audit trails.
Worked example: a report shows 4% of patient records with blank insurance IDs. That is a completeness defect owned by the data steward, remediated at the source and tracked with a quality scorecard.