Informatica CDGC Foundation key terms

A searchable inventory of data assets, such as tables, files, and reports, that records what each asset contains, where it lives, who owns it, and how it may be used.

Information that describes data rather than holding its values, including column names, data types, row counts, creation dates, and descriptions attached by stewards.

Structural and operational details harvested automatically from systems, such as schemas, data types, primary keys, storage location, and load timestamps, used mainly for analysis and administration.

Descriptive context added by people, such as definitions, ownership, sensitivity classification, and usage notes, that makes technical assets understandable to non-technical users.

Any managed object registered in the catalog, such as a table, view, column, report, dashboard, file, or application, that can carry ownership, classification, and relationships.

The catalog record representing a scanned object from a source system, holding its technical properties and links to related resources such as parent schemas and downstream consumers.

A governed collection of business terms with agreed definitions, synonyms, and owners, used to give shared meaning to data across systems and teams.

A single entry in a business glossary containing a definition, owner, status, and links to related assets, so users can see which data implements the concept.

A link created between a glossary term and a data asset or column, showing where a business concept is physically implemented in the environment.

A record of how data moves and transforms from source to target, showing upstream origins and downstream consumers so impact and root causes can be traced.

The direction toward the sources a data asset depends on, such as tables and processes that feed it, used when investigating where values or errors originate.

The direction toward the assets and reports that consume a given data asset, used to judge who is affected when the source changes or fails.

The practice of using lineage and relationships to determine which reports, applications, and teams would be affected by a proposed change to a data asset.

Working backward through lineage to find the original asset or process that produced a data defect observed in a downstream report or dashboard.

A configured connection and job that reads structure and properties from a source system, such as a database or BI tool, and loads them into the catalog.

The registered system definition, including connection details and credentials, that a scanner uses to reach a data source and extract metadata.

A scheduled or on-demand run that extracts metadata from a source, compares it with catalog contents, and adds, updates, or removes resource records.

A scan mode that processes only objects changed since the previous run, reducing load and runtime compared with a full rescan of the source.

A scan that reads every object in a source regardless of prior runs, useful after structural changes or when catalog records may have drifted out of sync.

A person accountable for a domain or set of assets who maintains definitions, classifications, ownership, and quality, and who approves catalog changes.

The accountable role for a data domain or asset who approves access, sets policy, and accepts responsibility for how the data is used and protected.

A logical grouping of related data assets and glossary content, often aligned to a business area, that organizes stewardship, policy, and reporting.

Assigning a sensitivity label, such as public, internal, confidential, or restricted, to an asset so handling, access, and protection rules can follow.

Data whose exposure would cause harm or legal obligation, such as personal identifiers, financial records, or health information, and which therefore needs stricter controls.

Replacing or obscuring real values with altered ones so users can work with realistic data without seeing sensitive originals, often applied per column.

The set of rules and practices governing lawful collection, use, retention, and disclosure of personal data, including responding to subject requests and honoring consent.

The degree to which data meets defined expectations for completeness, accuracy, consistency, timeliness, and uniqueness, measured through rules and scores.

A defined check, such as a null test, range check, or referential check, that runs against data and records pass or fail results for monitoring.

A category used to organize quality expectations, such as completeness, accuracy, consistency, timeliness, uniqueness, and validity, each measured by relevant rules.

A numeric summary, often a percentage, aggregating rule results over a period so stewards can trend quality and compare assets or domains.

Relatively static code sets, such as country codes, currency codes, or status values, used to categorize and validate other data across systems.

The discipline of defining, approving, versioning, and distributing shared code sets so that systems use consistent values instead of locally maintained variants.

The decision rights and processes that define who may define, change, access, and use data, supported by policy, stewardship, and measurement.

A documented rule stating required or prohibited behavior for data, such as retention limits or access conditions, that governs assets and processes.

The controlled process for requesting, reviewing, approving, and recording modifications to data assets or definitions, informed by lineage and impact analysis.

Analyzing actual values in a dataset to reveal patterns, null rates, distinct counts, ranges, and anomalies that inform quality rules and classification.

Finding relevant data assets by keyword, filter, tag, or relationship, so users can locate trustworthy sources before building reports or analyses.