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Quality & Governance terms
Catalogs, lineage, observability, MDM and the rules around data.
Business glossary A controlled list of business terms and their agreed definitions, kept separate from the technical fields that implement them. Data catalog A searchable inventory of an organization's data assets, with descriptions, ownership and usage attached. Data classification Categorizing data by sensitivity or type so that handling, access, and protection rules can be applied consistently and automatically. Data dictionary A structured reference listing every table, column, and field in a database along with its type, meaning, and constraints. Data drift A change in the statistical properties of input data over time that can silently degrade a deployed model's accuracy. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Data masking Replacing sensitive values with realistic but fake substitutes so data can be used for testing or analysis without exposure. Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Data profiling Automatically scanning a dataset to summarize its structure, value distributions, and quality before it is trusted or modeled. Data provenance The documented history of where a dataset came from and every transformation applied to it before its current state. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Data retention policy A documented rule for how long different categories of data are kept before they are deleted or archived. Data standards Agreed formats, fields, and definitions that let datasets from different agencies or systems be combined reliably. Data stewardship The assignment of named people who are accountable for the quality, definition, and appropriate use of specific data domains. Data validation Checking that data meets explicit, predefined rules for type, range, format, or consistency before it is used or trusted. Master data management (MDM) The discipline of maintaining one authoritative, consistent record for core entities like customers or products. Metadata management The discipline of capturing, organizing, and maintaining data about data so it can be found, trusted, and used consistently. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch. Pseudonymization Replacing identifying data with a reversible token, keeping the mapping back to real identities separate and restricted. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually. Row-level security (RLS) Restricting which rows of a table a user can query, rather than granting or denying access to the whole table.