DABCC key terms

The operational, day-to-day accountability for data assets, carried out by business and technical stewards who enforce policies, resolve quality issues, and approve changes within their assigned domains.

The decision-making framework of policies, roles, and processes that determines who can take what action on which data, under what circumstances, using which methods.

The accountable executive or business leader who has authority over a data domain, approves access and usage decisions, and accepts responsibility for the data's fitness for purpose.

The technical role responsible for storing, securing, backing up, and operating data systems according to the rules set by data owners and stewards, without owning business meaning.

A formal statement of principles and mandatory rules that directs how data is acquired, used, protected, retained, and disposed of across the organization.

The process of assigning sensitivity labels such as public, internal, confidential, or restricted to data so that controls and handling rules can be applied consistently.

Rules that specify how long each category of data must be kept, where it is stored, and when and how it must be securely destroyed or archived.

A high-level, technology-independent representation of business entities, their relationships, and key concepts, used to align stakeholders before physical design begins.

A detailed model of entities, attributes, relationships, and business rules that is independent of any specific database product but precise enough to guide physical design.

The implementation-level design of tables, columns, keys, indexes, partitions, and storage structures for a specific database platform, derived from the logical model.

The process of structuring relational tables to reduce redundancy and update anomalies by organizing attributes into progressively stricter normal forms.

The deliberate introduction of redundancy into a data model, typically to improve query performance or simplify reporting, at the cost of update complexity.

A dimensional model in which a central fact table containing measures and foreign keys is joined to surrounding dimension tables that describe the business context.

A dimensional model in which dimension tables are normalized into multiple related tables, reducing redundancy but increasing the number of joins required.

A table in a dimensional model that stores quantitative measures and foreign keys referencing dimension tables, typically at a defined grain of business activity.

A table in a dimensional model that holds descriptive attributes used to filter, group, and label facts, such as customer, product, date, or geography.

The level of detail represented by a single row in a fact table, such as one row per order line or one row per customer per day.

A dimension design technique for tracking attribute changes over time, commonly using Type 1 overwrite, Type 2 versioned rows with effective dates, or Type 3 prior-value columns.

A data integration pattern that extracts data from source systems, transforms it to meet target requirements, and loads it into a data warehouse or other target.

A data integration pattern that loads raw source data into a target platform first and performs transformations there, leveraging the target's scalable compute.

An automated sequence of steps that moves and transforms data from sources to targets, including scheduling, error handling, monitoring, and dependency management.

An intermediate storage zone where extracted source data is landed and prepared before transformation and loading into the target warehouse or mart.

A technique for identifying and delivering only the inserts, updates, and deletes that occurred in a source system since the last extraction, reducing load volume and latency.

A loading strategy that processes only new or changed records since the previous run, in contrast to a full load that replaces the entire target data set.

A property of a data process whereby rerunning the same job with the same input produces the same result without duplicating records or corrupting state.

Measurable characteristics used to assess data fitness, commonly including completeness, accuracy, consistency, timeliness, validity, and uniqueness.

The analytical examination of data to discover structure, value distributions, null rates, uniqueness, patterns, and anomalies before defining quality rules or integration logic.

A precise, testable condition that data must satisfy, such as a mandatory field, a valid value range, a referential constraint, or a uniqueness requirement.

The process of detecting and correcting or removing inaccurate, incomplete, duplicated, or malformed records to improve data fitness for downstream use.

The application of consistent formats, codes, and reference values to data, such as normalizing addresses, dates, country codes, and units of measure.

Data that can identify a specific individual directly or in combination with other data, such as names, government identifiers, contact details, or biometric records.

The process of replacing sensitive values with realistic but altered values so that non-production users or analysts can work without seeing the original data.

The irreversible transformation of data so that individuals can no longer be identified, including techniques such as generalization, suppression, and perturbation.

An access control model in which permissions are assigned to roles and users receive access by being placed in the appropriate roles.

The security principle that users and processes should receive only the minimum access rights necessary to perform their assigned tasks, for only as long as needed.

The documented path of data from its origin through transformations to its destination, used for impact analysis, root-cause investigation, and regulatory traceability.

Data that describes other data, including technical structures, business definitions, ownership, lineage, quality metrics, and usage information.

A curated collection of business terms with agreed definitions, owners, and relationships, used to establish shared meaning across systems and reports.

The discipline and supporting technology for creating and maintaining consistent, authoritative records for core entities such as customers, products, suppliers, and employees.

The single, trusted, and authoritative version of a master data entity assembled from multiple sources through matching, merging, and survivorship rules.