CDGP key terms

The exercise of authority and control over data, setting decision rights, policies, and accountability so that data is managed as an asset and meets organizational and regulatory needs.

The day-to-day operational responsibility for a data domain, carried out by people who apply governance decisions, resolve quality issues, and maintain definitions within their area of accountability.

An accountable role, usually a business leader, who has decision rights over a defined data domain, approves policies and standards, and answers for the quality and use of that data.

A role responsible for the technical care of data, such as storage, backups, access provisioning, and security controls, executing decisions made by owners rather than setting policy.

A cross-functional body of senior stakeholders that sets priorities, arbitrates conflicts, approves policies, and oversees the governance program's performance across business and technology groups.

A written statement of management's intent and mandatory direction on how data must be created, used, protected, and disposed of, expressed broadly enough to guide standards and procedures.

A specific, measurable, and enforceable requirement that supports a policy, such as a naming convention, allowed value set, or format rule, against which conformance can be tested.

The formal allocation of authority stating who may make which data decisions, such as approving definitions, granting access, or accepting risk, and who must be consulted.

The design describing how governance is organized and executed, including roles, forums, processes, and escalation paths that connect executive oversight to day-to-day stewardship work.

Principles and practices for using data in ways that are fair, transparent, and respectful of people's rights, addressing harms not fully covered by law or contract.

The view that data has measurable value to the organization and therefore deserves the same planning, investment, accounting, and protection as other valued resources.

The documented process by which a data problem that cannot be resolved at its point of origin is raised to higher authority levels with evidence and options for decision.

The stages through which data passes from creation or acquisition through storage, use, sharing, archiving, and eventual disposal, each with distinct management and control requirements.

The core, shared data about key business entities such as customers, products, or suppliers that many processes and systems rely on and that must be consistently defined and maintained.

Controlled sets of permitted values or codes, such as country lists or product categories, used to classify other data and to keep meaning consistent across systems.

Data that records individual business events, such as an order, payment, or claim, capturing the details of what happened, when, and with whom.

The practice of combining data from multiple sources and delivering it consistently to consumers, using techniques such as extraction, transformation, and movement between systems.

A structured description of the knowledge areas, functions, and relationships that make up the discipline of managing data, used to organize responsibilities and plan capability improvement.

The ability of systems and organizations to exchange and interpret data correctly, which depends on shared formats, semantics, and interface agreements.

The discipline of collecting, using, and sharing personal information in line with individual expectations and legal obligations, including consent, minimization, and retention limits.

A named, measurable aspect of quality such as completeness, accuracy, timeliness, or uniqueness, used to define what good data means and to structure measurement.

The analytical examination of data to discover its actual structure, value distributions, patterns, and anomalies, typically used before defining rules or designing a data model.

A testable statement that expresses an acceptable condition for data, such as a required field or an allowed range, which can be automated to detect defects.

A reporting instrument that summarizes measured quality against defined dimensions and thresholds for a data set or domain, making trends and problem areas visible to stakeholders.

A structured technique for tracing a data defect back to the process, system, or human step that produced it, so that the fix prevents recurrence rather than only correcting the symptom.

The activity of detecting and correcting or removing inaccurate, incomplete, duplicated, or malformed values in a data set, ideally combined with changes upstream to prevent reoccurrence.

The ongoing cycle of defining quality requirements, measuring conformance, remediating defects, and improving processes so that data consistently meets the needs of its consumers.

Information about data that describes its meaning, structure, origin, ownership, and usage, enabling people and systems to find, understand, and trust data.

Metadata that gives business meaning to data, including definitions, business names, calculation rules, ownership, and usage guidance written for non-technical audiences.

Metadata describing physical structure and implementation details such as tables, columns, data types, keys, file layouts, and transformation logic used by systems.

Metadata about runtime activity, including job run times, record counts, error logs, and access history, used to monitor processing and support audit and troubleshooting.

A record of where data came from and how it moved and changed across systems and transformations, used for impact analysis, troubleshooting, and regulatory traceability.

A controlled collection of business terms with agreed definitions, owners, and relationships, giving a common vocabulary for data discussions and model design.

An organized inventory of data assets that describes what exists, where it lives, what it means, and who owns it, helping users find and evaluate data for reuse.

The specification of how data is structured, integrated, stored, and used across the enterprise, expressed as models, standards, and roadmaps that guide solution design.

A high-level, technology-independent view of the main business entities and their relationships, used to establish scope and shared understanding before design detail is added.

A detailed but technology-neutral representation of entities, attributes, keys, and relationships that fully describes business rules and can be implemented in different physical designs.

A design-level representation tailored to a specific database or technology, including tables, data types, indexes, and constraints needed to build the structure.

The process of organizing attributes into tables to remove redundancy and update anomalies, typically by ensuring each fact is stored once and depends on the key.

A design approach for analytical data that splits information into measurable facts and the descriptive dimensions that provide context, usually arranged as star or snowflake schemas.