Data governance · 13 pages
Data governance & metadata
Answer-first explainers for enterprise data governance and metadata — stewardship, master data, lineage, quality, catalogs and the standards behind them — from a body whose whole purpose is metadata standards.
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All 13 data governance & metadata pages
Data governance
Data governance is the exercise of authority, control and shared decision-making over the management of an organisation’s data assets. It defines the policies, roles, standards and processes that ensure data is accurate, available, secure and used responsibly. Governance sets the rules and decides who is accountable; data management does the day-to-day work those rules direct.
DefinitionMetadata management
Metadata management is the practice of creating, storing and maintaining metadata — the data that describes other data, including definitions, structure, lineage, ownership and quality. It makes data consistent, discoverable and trustworthy across an organisation. Effective metadata management is the connective tissue of governance, powering data dictionaries, catalogues and lineage so people can find data and understand exactly what it means.
DefinitionMaster data management
Master data management (MDM) is the practice of creating and maintaining a single, consistent, authoritative view of an organisation’s core shared entities — customers, products, suppliers and similar — across all its systems. It reconciles duplicate and conflicting records into one trusted master record. MDM is governed work: it relies on agreed definitions, matching rules and stewardship so that everyone references the same authoritative data.
DefinitionData stewardship
Data stewardship is the operational role of being accountable for specific data assets — their quality, definitions, classification and policy compliance. Data stewards are the people who execute data governance day to day, curating definitions, resolving data-quality issues and applying the rules the governance framework sets. Stewardship is where governance policy becomes practice: stewards bridge the business meaning of data and its technical management.
DefinitionData dictionary
A data dictionary is a centralised repository of metadata that documents an organisation’s data elements — their definitions, data types, formats, relationships and allowed values. It gives technical and business users a single, authoritative reference for what each field means and how it should be used. Standards such as ISO/IEC 11179 describe how to register and describe data elements consistently, which is the principle a data dictionary puts into practice.
DefinitionData catalog
A data catalog is an organised inventory of an organisation’s data assets, described with metadata so users can search, discover and understand the data available to them. It typically pulls together definitions, ownership, lineage and quality information in one place. By making data findable and its meaning and provenance visible, a catalogue supports both self-service analytics and the governance controls that keep data trustworthy.
DefinitionData lineage
Data lineage is the documented flow, origin and transformation of data from its source through to its point of consumption. It traces where data came from, how it was changed and where it is used. Lineage is essential for trust and audit, for impact analysis when systems change, and increasingly for AI, where knowing the provenance of training data is critical to accountability and compliance.
DefinitionData quality
Data quality is the degree to which data is fit for its intended purpose. It is assessed across dimensions including accuracy, completeness, consistency, timeliness, validity and uniqueness. High-quality data can be relied on for decisions, reporting and analytics; poor-quality data leads to flawed conclusions and operational errors. Because fitness depends on use, data quality is judged against requirements and sustained through governance, stewardship and ongoing measurement.
DefinitionData classification
Data classification is the practice of categorising data by its sensitivity or type — commonly into levels such as public, internal, confidential and restricted — so that appropriate security, privacy and handling controls can be applied. Classification tells the organisation how each dataset must be protected, stored, shared and retained. It is a foundational governance control that underpins access management, regulatory compliance and risk reduction.
DefinitionDublin Core
Dublin Core is a metadata standard maintained by the Dublin Core Metadata Initiative (DCMI). At its heart is a set of 15 core descriptive elements — such as Title, Creator, Subject, Date and Identifier — used to describe digital and physical resources consistently. Its purpose is interoperability: a simple, widely understood vocabulary that lets resources be described and discovered across different systems, domains and communities.
DefinitionData governance framework
A data governance framework is the structured model of principles, roles, policies and processes that operationalises governance in an organisation. It defines who is accountable, what policies and standards apply and how decisions are made and enforced. Established references such as DAMA-DMBOK and DCAM provide proven structures organisations adapt to their own context, turning governance from an abstract intention into a repeatable operating model.
DefinitionReference data
Reference data is the set of permitted values and code sets used to classify or categorise other data — for example country codes, currency codes, units of measure and status lists. It standardises how data is recorded so that systems and reports remain consistent. Reference data is effectively a subset of master data, and it is most reliable when based on recognised external standards and managed under clear governance.
ComparisonData governance vs data management
Data governance is the exercise of authority and oversight over data — the policies, roles, standards and decision rights that determine how data is managed and who is accountable. Data management is the practice of actually collecting, storing, organising, maintaining and using data. In short, governance sets the rules and decides who decides; management does the work those rules direct. Governance is the oversight layer over the doing of data management.
