Data Governance Glossary | Linedat

Glossary

Key Data Governance concepts explained clearly and practically

15 essential terms across data governance, quality, compliance and architecture.

Data Governance is the framework of policies, processes, and roles that ensures data quality, security, and correct use across an organization.

A Data Catalog is a centralized inventory of all of an organization's data assets, with metadata, descriptions, and lineage.

Data Lineage is the traceability of data from its origin to its destination, showing all transformations and dependencies along the way.

Data Quality measures whether data is accurate, complete, consistent, and up to date. Learn how to implement effective quality rules.

PII is data that identifies a person: name, email, national ID, IP address. Learn how to detect and manage it for GDPR and DORA compliance.

A Business Glossary defines the official meaning of business terms, eliminating ambiguity in the use of data across an organization.

A Data Steward is operationally responsible for the quality, documentation, and compliance of data within a specific domain.

Data Mesh is a decentralized architecture where each business domain is responsible for its own data products.

A Data Contract is a formal agreement between a data producer and consumer that defines schema, quality, SLAs, and responsibilities.

The GDPR (General Data Protection Regulation) is the European data protection regulation. Learn its key principles and how to implement compliance.

DORA is the European digital resilience regulation for the financial sector. Learn its requirements and how data governance helps.

Metadata management organizes data about your data: schemas, descriptions, lineage, and classifications in a centralized system.

Column-level lineage tracks how each individual field is transformed and propagated through pipelines, views, and dashboards.

Impact analysis evaluates what will break before making changes to data, tables, or columns. It prevents downstream incidents.

MCP is an open protocol that connects AI models with data sources and tools in a standardized and secure way.