CDGP flashcards

32 cards. Read the term and the definition here. The deck at the top of this page is optional.

Data governance definition

Data governance is the exercise of authority and control over data management, covering planning, monitoring, and enforcement of policies, standards, and decision rights across the organization.

Data owner

A data owner is a business role accountable for a data domain, approving access, defining policies, and ensuring data quality; accountability cannot be delegated to IT.

Data steward

A data steward is a business role responsible for day-to-day data quality, metadata, and policy compliance within a domain, reporting to the accountable data owner.

Data custodian

A data custodian is a technical role that stores, backs up, and secures data according to owner-defined policies, typically within IT operations.

Data governance council

A data governance council is a cross-functional decision body that sets data policy, resolves escalated issues, and prioritizes data initiatives across business units.

Policy hierarchy

Data policies state mandatory rules; standards specify measurable requirements; procedures define steps; guidelines are optional recommendations. Lower levels must comply with higher levels.

Decision rights

Decision rights define who can make which data decisions, such as defining, approving, or changing data, and are documented using RACI or similar accountability matrices.

Data management definition

Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance data assets throughout their lifecycle.

DAMA knowledge areas

DMBOK2 defines eleven knowledge areas including Data Governance, Data Architecture, Data Modeling, Data Storage, Data Security, Data Integration, Document Management, Reference and Master Data, Data Warehousing, Metadata, and Data Quality.

Data lifecycle stages

The data lifecycle includes plan, design, enable, create or obtain, maintain and use, enhance, and dispose or archive; governance controls apply at every stage.

ACID properties

ACID describes transaction guarantees: Atomicity (all or nothing), Consistency (valid state), Isolation (concurrent transactions independent), and Durability (committed changes persist).

CAP theorem

CAP states a distributed data system can guarantee only two of Consistency, Availability, and Partition tolerance at the same time; partition tolerance is usually mandatory.

RPO versus RTO

Recovery Point Objective is the maximum acceptable data loss measured in time; Recovery Time Objective is the maximum acceptable downtime before restoration must be complete.

Master versus reference data

Master data describes key business entities such as customers and products; reference data is a controlled set of allowable values, such as country codes, used across systems.

Data quality dimensions

DAMA's core data quality dimensions include accuracy, completeness, timeliness, validity, consistency, and uniqueness; each is measured with rules and scored against thresholds.

Data accuracy

Accuracy is the degree to which data values correctly describe the real-world object or event they represent, often verified against a trusted source.

Data completeness

Completeness is the proportion of required data values that are present, measured as populated fields divided by expected fields within a defined scope.

Data validity

Validity is the degree to which data conforms to defined syntax, format, type, and business rules, such as a valid email pattern or allowed code list.

Data timeliness

Timeliness is the degree to which data is available when needed and represents the current state within an acceptable lag or latency threshold.

Data profiling

Data profiling is the systematic analysis of data to assess quality, structure, and relationships, producing statistics such as null counts, distinct values, and value ranges.

Metadata definition

Metadata is data about data: it describes the content, context, structure, quality, and lineage of data assets, enabling discovery, understanding, and controlled use.

Business metadata

Business metadata provides business-friendly descriptions, definitions, and rules, such as glossary terms, ownership, and calculations, that help users understand and trust data.

Technical metadata

Technical metadata describes physical structures and processes, including tables, columns, data types, schemas, ETL mappings, and transformation logic used by systems.

Operational metadata

Operational metadata records runtime information such as job logs, execution times, error counts, and access statistics used to monitor data processing and usage.

Data lineage

Data lineage traces data from origin to destination across systems and transformations, showing how each field is derived and supporting impact analysis and audit.

Business glossary

A business glossary is a curated collection of business terms with definitions, owners, and relationships, providing a common vocabulary across the organization.

Conceptual data model

A conceptual data model shows high-level business entities and relationships without attributes or technical detail, used to communicate scope with business stakeholders.

Logical data model

A logical data model defines entities, attributes, relationships, and keys in detail while remaining technology-independent, often normalized and used for design review.

Physical data model

A physical data model specifies tables, columns, data types, indexes, partitions, and constraints for a particular database platform and performance requirement.

Third normal form

Third normal form requires a table to be in second normal form and to have no transitive dependencies, so every non-key attribute depends only on the key.

Star schema

A star schema organizes a central fact table containing measures and keys surrounded by denormalized dimension tables, optimizing query performance for analytics.

Data vault components

Data vault modeling uses hubs for business keys, links for relationships, and satellites for descriptive attributes and history, supporting auditable and scalable integration.