Apogee Systems

Business glossary

Short definitions for terms you will hear in data governance, architecture, and delivery conversations.

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134 terms

Architecture(16)

Business architecture: How an organization shapes, designs, and runs the capabilities that support its business functions. It sits within enterprise architecture and should stay aligned with data and technology architecture so strategy, processes, and systems move together. Common outputs include capability and process models, business objectives, and architecture roadmaps.; Related terms: Data model, Policy
Canonical data model: A logical data model treated as the authoritative reference for a subject area—ranked above other logical views and used to translate between them. It establishes a shared structural pattern across differing perspectives, especially in integration and messaging where systems need a common, source-neutral shape for data exchange.; Related terms: Data model, Data dictionary, Metadata, Enterprise data integration
Conceptual data model: A high-level, simplified view of the data concepts that matter to a business process. It is mainly a communication tool between data modelers and process owners to agree on core ideas before detailed design, and it should align with the organization's semantic model where one exists.; Related terms: Data model, Canonical data model, Business metadata
Context diagram: A diagram that shows a scope of work—major activities, inputs, outputs, and dependencies—so teams agree what is in and out of bounds for a program or capability.; Related terms: Data architecture
Data architecture: The strategy and practice of how data is identified, defined, and structured to meet enterprise goals. It works alongside business and technology architecture (sometimes called integration architecture) and typically covers domains, glossaries, logical and physical requirements, metadata, models, ontologies, and taxonomies.; Related terms: Business architecture, Data model, Metadata, Enterprise data model; Related artifacts: Architecture policies
Data architecture function: The organizational function that defines and delivers the data content strategy for a scoped set of data. It connects business needs to technical implementation by specifying how data is identified, defined, accessed, and kept aligned to agreed meaning across the enterprise—including processes, operating models, modeling standards, domain design, and a shared view of data semantics.; Related terms: Data architecture, Business architecture, Logical data model
Data mesh: A decentralized approach where domain teams own data products with shared standards and federated governance, rather than relying on a single central pipeline alone.; Related terms: Federated governance, Data management operating model, Data architecture, Data domain
Data model: A declarative description of data structure for a chosen scope, perspective, and level of abstraction—how identities, attributes, relationships, and cardinality are defined. Common levels are conceptual, logical, and physical; rigorous rules apply, but scope and content choices make modeling as much craft as science.; Related terms: Conceptual data model, Logical data model, Physical data model; Related artifacts: Data models, Enterprise data model
Data modeling: The practice of defining structures, relationships, and rules for data at conceptual, logical, and physical levels.; Related terms: Data Modeling and Design, Data model, Enterprise data model, Logical data model
Data Modeling and Design: Defining conceptual, logical, and physical data structures, naming standards, and model governance so business meaning stays aligned with implementation.; Related terms: Data model, Data modeling, Logical data model, Enterprise data model
Dimensional modeling: A modeling style that organizes facts and dimensions for efficient analytical queries.; Related terms: Data warehouse, Data dimension, Business intelligence, Data Warehousing and Business Intelligence
Enterprise architecture: The strategy and practice of designing business, data, and technology capabilities together so they support organizational goals. It typically comprises business architecture, data architecture, and technology architecture as integrated subcomponents.; Related terms: Business architecture, Data architecture, Technology architecture, Context diagram
Enterprise data model: A consolidated view of core entities and relationships agreed at enterprise scope.; Related terms: Data architecture, Data model, Canonical data model, Data Modeling and Design; Related artifacts: Enterprise data model
Logical data model: A technology-neutral data model that captures the attributes and relationships needed to support a business process. It reflects the business elements the process depends on, though it may not spell out every requirement for each element in full detail.; Related terms: Data model, Conceptual data model, Data dictionary
Physical data model: How data meaning, relationships, and attributes are realized in a specific technology implementation—typically the physical form of a logical data model. It should follow logical attributes when possible, but may add or split elements for storage, performance, or operational needs such as breaking apart compound fields or adding system-managed columns.; Related terms: Logical data model, Data model, Data dictionary
Technology architecture: The strategy and practice of designing physical infrastructure to meet business and data needs. It is part of enterprise architecture and should align with business and data architecture, covering hardware, system software, locations, configurations, standards, protocols, and how platforms connect to applications and databases.; Related terms: Business architecture, Data architecture, Policy

Data management(34)

Big data: High-volume, high-velocity, and often varied data that exceeds comfortable limits of traditional relational platforms.; Related terms: Big Data and Data Science, Data lake, Data science, Data architecture
Big Data and Data Science: The practice of working with large-scale, often semi-structured data and the methods used to explore, model, and extract insight from it. It spans strategy, architecture, governance, and tooling that complement traditional warehousing.; Related terms: Big data, Data science, Data lake, Data architecture
Data asset: Any body of data the organization owns and treats as valuable in its own right. Assets can take many forms—databases, documents, files on digital media, websites, audio, video, and similar sources.; Related terms: Data catalog, Metadata, Data owner, Data management; Related artifacts: Data asset inventories
Data attribute: The logical representation of a single, indivisible data element—the smallest unit of meaning modeled before it is grouped into entities or implemented physically.; Related terms: Data model, Data dictionary, Logical data model, Data element
Data catalog: An organized inventory of an organization's data assets, powered by metadata to support management, discovery, and governance. Teams use it to find assets and to collect, organize, access, and enrich the metadata that describes them.; Related terms: Metadata, Data asset, Data steward, Metadata repository; Related artifacts: Data asset inventories
Data classification: The practice of grouping data into categories so it can be managed and used effectively. Classification supports access decisions, prioritization, control execution, and alignment to specific use cases—often across several taxonomies at once, such as security, privacy, confidentiality, access level, or data domain.; Related terms: Metadata, Policy, Data catalog
Data consumer: A process, application, or stakeholder that receives or uses data from a producer. Consumers set requirements and quality expectations and need confidence the data is fit for purpose and used in line with governance, data management, and risk policies.; Related terms: Data producer, Data owner, Policy
Data dictionary: A technical reference that describes how data is structured in a database or repository—field names, data types, relationships, constraints, and other details engineers and analysts need to store, integrate, and manage data.; Related terms: Data model, Logical data model, Metadata, Technical metadata
Data dimension: In a data warehouse context, a category used to describe and filter analytical data—such as time, geography, product, or customer—so facts and measures can be analyzed from different perspectives.; Related terms: Data model, Metadata, Data catalog
Data domain: A named, logical category of data the organization treats as strategically important—not a physical database or repository. Also called a data subject area or data family, a domain groups related data (internal or external) such as customer, product, pricing, or risk data so it can be defined, inventoried, and governed consistently.; Related terms: Data architecture, Data catalog, Data owner
Data ecosystem: The full environment of data and data-related capabilities that matter to an organization—repositories, processing and handling systems, distribution services, analytics platforms, and the people and roles that produce, consume, and govern that data.; Related terms: Data architecture, Data catalog, Metadata
Data element: A unit of data treated as indivisible in context (per ISO/IEC 2382). It is the physical realization of a business element—the documented data need from a business process—captured in a field where values can be stored, inspected, and measured. A data element may align to zero or more business elements.; Related terms: Data attribute, Data dictionary, Business metadata
Data engineering: The work of building and running the non-physical parts of data infrastructure so data is fit for purpose—typically under the data architecture plan. It focuses on harmonizing meaning across repositories (domains, glossaries, models, metadata, ontologies, and taxonomies) so definitions stay precise, consistent, and transparent, whether meaning is industry-standard or enterprise-specific.; Related terms: Data architecture, Data dictionary, Metadata
Data entity: A modeled concept representing a person, place, or thing in the business or system world. Entities are described by their data attributes—the fields that capture facts about them.; Related terms: Data attribute, Data model, Data element
Data lifecycle: A model of the stages a data asset passes through from creation or acquisition to destruction. Organizations choose a lifecycle framework that fits their needs; stages often cover creation, capture, storage, movement, maintenance, use, archive, and disposal.; Related terms: Data asset, Policy, Metadata, Information lifecycle management
Data management: The discipline of developing, running, and overseeing plans, policies, programs, and practices that protect and control data while increasing the value of data and information assets across their lifecycles.; Related terms: Data governance function, Data lifecycle, Policy, Data management principles; Related artifacts: Data management standards
Data management principles: Foundational statements that guide how an organization treats data as an asset, manages quality, and aligns data work to business value.; Related terms: Data management, Data strategy, Policy
Data normalization: The business-oriented process of aligning data values to agreed definitions, formats, and rules—not the database-design technique of normalizing tables to reduce redundancy.; Related terms: Data quality, Data dictionary, Standard
Data producer: A process, application, or stakeholder that supplies data to one or more downstream consumers.; Related terms: Data consumer, Data flow, Data lineage, Stakeholder
Data science: Disciplines that use statistics, machine learning, and experimentation to extract insight from data.; Related terms: Big Data and Data Science, Big data, Machine learning model, Data lake
Data strategy: A formal statement of business objectives and priorities, the data scope needed to achieve them, and the principles for how that data will be defined, managed, and used.; Related terms: Data management, Data architecture, Policy, Data management principles; Related artifacts: Data strategy
Data traceability: The ability to follow a data construct back to the more abstract construct it came from—for example, a physical column to a logical attribute, then to a business term and underlying concept. Related to, but narrower than, data lineage and provenance.; Related terms: Traceability, Data lineage, Data provenance, Logical data model
Derived data: Data created by combining, calculating, or deriving from other data rather than captured directly at source.; Related terms: Data lineage, Data element, Data model
Document and Content Management: Managing unstructured and semi-structured content through capture, metadata, lifecycle controls, and policies that complement structured data.; Related terms: Information lifecycle management, Controlled vocabulary, Records, Metadata
Golden record: Also called a master record, golden copy, or single version of the truth—the one verified, officially designated version of data for a subject, validated as fit for purpose and intended for use across applications under policy.; Related terms: Authoritative data source (ADS), Master data, Data quality, Match-merge
Information lifecycle management: Policies and processes that govern how information is created, classified, retained, and disposed.; Related terms: Data lifecycle, Records, Document and Content Management, Policy
Machine learning model: A trained artifact that scores or predicts from data features.; Related terms: Data science, Big Data and Data Science, Data ethics, Metadata
Master data: Critical entities, relationships, and attributes shared enterprise-wide and foundational to core processes and systems. Master data provides the stable context around which transactional data is created and managed.; Related terms: Golden record, Transactional data, Data model, Master data management; Related artifacts: Reusable data services catalog
Master data management: Disciplines and tools that maintain authoritative master entities and reference data across systems.; Related terms: Master data, Golden record, Match-merge, Reference and Master Data; Related artifacts: Reusable data services catalog
Match-merge: Processes that identify duplicate records and combine them into a trusted master instance.; Related terms: Master data management, Golden record, Master data, Data quality
Reference and Master Data: Shared reference lists and master entities that anchor transactions and analytics with consistent identifiers and meanings across the enterprise.; Related terms: Master data, Reference data, Master data management, Golden record
Reference data: Data that defines the allowable values other fields may use. The same data element can have different value sets by use case; enterprises often maintain a higher-level compilation. Best practice is one enterprise-wide reference construct that maps between local value sets and preserves consistent meaning across the organization.; Related terms: Data element, Master data, Data dictionary, Canonical data model, Controlled vocabulary
Stakeholder: An interested participant in the data ecosystem—such as a data producer, data consumer, or a process that supports data creation or use.; Related terms: Data ecosystem, Data producer, Data consumer; Related artifacts: Stakeholder engagement records
Traceability: The ability to trace a data item back to its origin and to identify other data items that were inputs to its calculation or derivation.; Related terms: Data lineage, Data provenance, Data traceability, Derived data

Governance(39)

Authoritative data domain: Also called an authorized data domain, a data domain that the data management governing body has formally designated, verified, approved, and enforces.; Related terms: Data domain, Data governance function, Policy
Capability maturity model: A staged model for assessing how capable and repeatable an organization's practices are.; Related terms: Data management maturity, Data Management Maturity Assessment, Metrics
Certified Data Management Professional (CDMP): A widely recognized industry certification for data management professionals, covering governance, quality, modeling, and related disciplines.; Related terms: Data management, Data governance function
Change management: Structured approaches to help people adopt new data policies, tools, and roles.; Related terms: Data Management and Organizational Change Management, Data governance function, Data stewardship
Chief data officer: An executive or senior leader accountable for enterprise data strategy, governance, and value realization.; Related terms: Data governance function, Data management operating model, Data strategy, Policy; Related artifacts: Organization and accountability
Critical business element (CBE): A business element judged materially important to one or more processes. Criticality is a business requirement approved through governance; a data element is only critical when it belongs to a CBE. Designation often triggers stronger quality controls, metadata evidence, and risk treatment—some organizations treat critical and key as synonyms, others rank critical higher.; Related terms: Data element, Data quality, Data owner
Data custodian: An individual responsible for implementing and maintaining one or more data controls—such as access, retention, or quality checks—so that policies and standards are applied consistently in systems and processes.; Related terms: Data owner, Data steward, Policy, Data risk
Data ethics: Principles and practices for fair, transparent, and responsible handling of data about people and organizations.; Related terms: Data Handling Ethics, Policy, Data risk, Privacy by design; Related artifacts: Ethical use statements
Data governance council: A cross-functional body that sets priorities, resolves issues, and sponsors standards for the data program.; Related terms: Data governance function, Data stewardship, Policy, Chief data officer
Data governance function: The function that defines and implements standards, controls, and best practices for the data management program in line with strategy. It builds a data control environment—executive-backed policies for how data is acquired, shared, integrated, and used; standards applied across the lifecycle; clear accountability; audit monitoring; plus operating models, procedures, metrics, and training.; Related terms: Policy, Standard, Data owner, Data stewardship; Related artifacts: Governance charters, Governance scorecard
Data Handling Ethics: Principles and practices for ethical collection, processing, and use of data, including bias, privacy, and building a trustworthy data culture. It underpins governance and informs decisions across the data program.; Related terms: Data ethics, Policy, Data governance function, Privacy by design
Data Management and Organizational Change Management: Leading adoption of data policies, tools, and roles through communication, sponsorship, and structured change methods so governance and architecture investments take hold.; Related terms: Change management, Data governance function, Data management, Data stewardship
Data management maturity: How capable and repeatable an organization's data practices are across governance, quality, architecture, and operations.; Related terms: Data Management Maturity Assessment, Capability maturity model, Metrics
Data Management Maturity Assessment: Methods for evaluating how capable and repeatable an organization's data practices are, setting improvement targets, and planning roadmaps for the data management program.; Related terms: Data management maturity, Capability maturity model, Metrics, Data management
Data management operating model: How people, processes, and decision rights are organized to run data management—centralized, federated, or hybrid.; Related terms: Federated governance, Chief data officer, Data steward, Domain; Related artifacts: Funding and business case, Program roadmaps
Data Management Organization and Role Expectations: How operating models, roles, and organizational design sustain data management, including centralized, federated, and hybrid patterns.; Related terms: Data management operating model, Federated governance, Chief data officer, Data steward
Data masking: Techniques that obscure sensitive values while preserving usefulness for test or analytics.; Related terms: Data security, Personally identifiable information (PII), Data classification
Data owner: The role with overall accountability for the meaning, content, quality, and distribution of a defined set of data or domain. The owner ensures data is defined, produced, maintained, delivered, and consumed to organizational standards—even when day-to-day curation is performed by others.; Related terms: Data steward, Data domain, RACI matrix, Data governance function
Data retention: Rules that define how long data and records must be kept before archive or destruction.; Related terms: Information lifecycle management, Records, Policy, Data lifecycle
Data risk: The potential for loss tied to how data is handled, protected, and used—spanning areas such as regulatory and compliance exposure, reputation harm, breaches, and data loss.; Related terms: Policy, Data governance function, Data classification; Related artifacts: Risk and control policies
Data security: Safeguards that protect data from accidental or deliberate unauthorized access, use, modification, disclosure, destruction, or theft.; Related terms: Data risk, Policy, Data classification, Data masking
Data sensitivity classification: A label applied to information within a data asset that states how strictly the asset must be controlled and protected, based on how sensitive the content is or how important it is to the organization or data subjects.; Related terms: Data classification, Data security, Policy
Data steward: A role responsible for day-to-day quality, metadata, and standards for a data domain on behalf of the data owner.; Related terms: Data owner, Data catalog, Data stewardship, Business glossary; Related artifacts: Organization and accountability
Data stewardship: Operational accountability for metadata, quality, and standards within a domain, often executed by stewards on behalf of owners.; Related terms: Data steward, Data owner, Data governance function, Business glossary; Related artifacts: Governance processes
Domain: In data management operating models, the organizational level where teams run activities for a given data domain—below enterprise, region, and operating unit in a typical hierarchy.; Related terms: Data domain, Data owner, Data architecture; Related artifacts: Storage and domain criteria
Federated governance: A model where global standards and oversight coexist with domain-level execution of data management.; Related terms: Data management operating model, Data governance council, Domain, Data stewardship
Function: In data management, an operational organizational capability with defined responsibilities—such as the data quality function or data governance function—that executes part of the data program.; Related terms: Data governance function, Data quality, Data management
Guideline: Recommended best practices that help implement a standard or interpret policy, or fill gaps where policy is silent. Guidelines are advisory—compliance is not enforced.; Related terms: Policy, Standard; Related artifacts: Capability approaches
Metrics: Quantitative measures used to judge performance, efficiency, or progress for processes, projects, or business objectives.; Related terms: Data quality, Data governance function, Data quality dimensions, Data quality scorecard; Related artifacts: Program metrics, Quality metrics, Risk and analytics metrics
Personally identifiable information (PII): Any information that can identify a person directly or indirectly. Treated as a sensitive data classification so private personal information is protected, with requirements shaped by privacy regulators worldwide.; Related terms: Data classification, Data security, Policy
Policy: A high-level directive that states management goals and expectations for legal, regulatory, and organizational requirements. Policies align to enterprise principles and set mandatory direction for how data is governed and used.; Related terms: Standard, Guideline; Related artifacts: Enterprise data policy, Analytics governance policy, Policy adoption records
Privacy by design: Embedding privacy controls and minimization into processes and systems from the start.; Related terms: Data ethics, Data Handling Ethics, Personally identifiable information (PII), Policy
Procedure: A detailed step or task within a process that explains how to perform a specific activity. Procedures are the granular instructions that sit below higher-level process definitions.; Related terms: Policy, Standard, Data governance function
Process: An ordered set of steps that, when performed in sequence, produces an intended outcome.; Related terms: Procedure, Policy, Data governance function; Related artifacts: Analytics processes, Communications process, Data quality processes, Governance processes
RACI matrix: A responsibility matrix for process stakeholders: Responsible (does the work), Accountable (owns the outcome), Consulted (provides input), and Informed (kept updated). Extended variants add further accountability categories.; Related terms: Data owner, Data steward, Process
Records: A subset of data retained because it documents information—including format and timing—required for legal, regulatory, or operational purposes. Examples include trade confirmations, board minutes, and risk reports. Underlying data elements are the components used to create or capture that information in a form suitable for processing and analysis.; Related terms: Data asset, Data element, Data lifecycle, Policy, Information lifecycle management; Related artifacts: Audit and compliance records, Behavior and adoption records, Content repository and records, Executive sponsorship records
Regulatory compliance: Demonstrating adherence to laws and regulations that apply to data handling and reporting.; Related terms: Policy, Data risk, Data governance function, Records
Standard: A rule or set of rules that define the expected actions required to comply with associated policies. Standards are subject to compliance audits.; Related terms: Policy, Guideline, Procedure, Data governance function; Related artifacts: Data management standards, Analytics standards
Subject matter expert: An individual with recognized authority in a business process, data manufacturing process, or application. SMEs inform requirements, definitions, quality rules, and remediation when specialized knowledge is needed (often referred to as an SME).; Related terms: Data steward, Data owner, Stakeholder, Process

Quality(9)

Accuracy: In data management, the degree to which data matches facts from an authoritative source or original record. It reflects precision against business rules and is one of the core data quality dimensions—covering wrong values, stale records, and mismatched precision (such as truncated decimals).; Related terms: Data quality dimensions, Data quality, Data profiling
Critical data: Data elements or domains whose quality or availability would materially harm processes if wrong or missing.; Related terms: Critical business element (CBE), Data quality, Data owner, Data risk
Data profiling: Also called data quality analysis or assessment, the process of examining and scoring a data source to judge whether it is fit for purpose—typically by checking quality dimensions, spotting anomalies, and understanding variance.; Related terms: Data normalization, Metadata, Data steward, Data quality scorecard; Related artifacts: Data profiling assessment
Data quality: Both a measure and a function: (1) the qualitative and quantitative conditions that show whether data is fit for purpose in a business process, often assessed on dimensions such as accuracy, completeness, conformity, consistency, coverage, timeliness, and uniqueness; and (2) the function that defines requirements, validation and remediation processes, operating models, escalation, metrics, reporting, and training so data used in operations stays fit for purpose.; Related terms: Data profiling, Data owner, Standard, Critical data; Related artifacts: Quality metrics, Data quality SLAs, Data quality processes
Data quality dimensions: Also called dimensions of quality, the categories used to score how fit for purpose data is. A common set includes accuracy, completeness, conformity, consistency, coverage, timeliness, and uniqueness.; Related terms: Data quality, Uniqueness, Data profiling, Standard
Data quality scorecard: A summarized view of quality metrics and status used to communicate performance to stakeholders.; Related terms: Data quality, Metrics, Data profiling, Data quality dimensions
Root cause analysis: Structured investigation to find underlying reasons for data defects or incidents.; Related terms: Data quality, Data profiling, Data quality scorecard, Data steward
Service level agreement: A commitment between providers and consumers on measurable service outcomes—often applied to data delivery timeliness and quality.; Related terms: Data quality, Metrics, Data consumer, Data producer
Uniqueness: In data management, a measure of whether each record or attribute appears only once—the goal is a single authoritative instance. It is one of the seven data quality dimensions. Failures include the same security stored under different identifiers or spellings, or one instrument modeled as both equity and debt in the same database.; Related terms: Data quality dimensions, Data quality, Golden record, Master data

Metadata & semantics(13)

Business metadata: Information that explains what data means and how it is used within business processes—also called descriptive metadata. It gives business stakeholders context, definitions, and usage guidance without requiring technical implementation detail.; Related terms: Metadata, Business architecture, Data dictionary, Technical metadata
Controlled vocabulary: An approved set of terms and definitions used consistently in metadata, search, and content systems.; Related terms: Taxonomy, Data glossary, Reference data, Business metadata; Related artifacts: Business glossary
Data glossary: A specialized glossary for data-related terms, metrics, and classifications. It may overlap with a business glossary but focuses on aligning teams on how data is defined—such as customer segments, revenue calculations, and reporting metrics.; Related terms: Term, Business metadata, Data dictionary, Data domain
Enterprise metadata repository: The organization's authoritative store of metadata—a consistent, trusted place to access definitions and context. Enterprises may operate one central repository or several coordinated ones, with the aim of a rationalized metadata source across the firm.; Related terms: Metadata, Data catalog, Data glossary, Metadata repository
Knowledge graph: A structured network that links entities—people, organizations, places, concepts, and more—through defined relationships. It mirrors how people connect ideas so systems can interpret, analyze, and reason over data more effectively.; Related terms: Metadata, Data model, Business metadata
Metadata: Data that describes other data—supporting consistent definitions, clear relationships, lineage, and operational efficiency. Common categories are business (descriptive), technical (structural), and operational (administrative) metadata.; Related terms: Business metadata, Data catalog, Data lineage, Metadata repository; Related artifacts: Metadata and lineage, Metadata metamodel
Metadata repository: A system that stores and governs metadata definitions, lineage, and technical details for enterprise use.; Related terms: Metadata, Enterprise metadata repository, Data catalog, Technical metadata
Ontology: A hierarchical map of concepts or entities in a domain and how they relate. Organizations often distinguish reference ontologies (shared definitions) from operational ontologies (used in running systems).; Related terms: Knowledge graph, Data architecture, Business metadata, Controlled vocabulary
Operational metadata: Metadata captured from live systems and run-time environments about how processes execute—such as file sizes, load and backup timestamps, job and script details, and runtime statistics.; Related terms: Metadata, Business metadata, Operational data, Technical metadata
Semantic model: A model that defines what matters to a business process and how those meanings relate. Semantic relationships capture relatedness and closeness of meaning—breadth, specificity, overlap, and distinction. Not the same as a semantic data model or the semantic web.; Related terms: Ontology, Business metadata, Data model, Knowledge graph
Taxonomy: A structured classification of concepts arranged in parent–child relationships. A data taxonomy gives the enterprise a common way to organize information so data stays fit-for-purpose—used in modeling, glossaries, metadata, domain registries, data stores, and data flows.; Related terms: Data glossary, Data domain, Metadata, Ontology, Data model; Related artifacts: Risk taxonomy and assessments
Technical metadata: Metadata describing physical structure, formats, and implementation—schemas, columns, jobs, and platform statistics.; Related terms: Metadata, Data dictionary, Operational metadata, Metadata repository
Term: In data management, a word or phrase that names a thing or expresses a concept within a clearly defined context. Things are often modeled conceptually; a collected set of terms is commonly published as a business glossary.; Related terms: Data glossary, Business metadata, Conceptual data model, Semantic model

Operations(23)

ACID: Atomicity, consistency, isolation, and durability—properties that transactional databases use to guarantee reliable commits.; Related terms: Database administration, Data Storage and Operations, Data platform
Authoritative data source (ADS): An officially designated system or repository—also called a golden source, authorized data source, or publication point—that the governing body has approved as the reliable, accurate source for a data domain, with required use set by policy and standards.; Related terms: Authoritative data domain, Golden record, Policy
Business intelligence: Processes and tools that turn historical and integrated data into reports, dashboards, and analysis for decisions.; Related terms: Data Warehousing and Business Intelligence, Data warehouse, Dimensional modeling, Metrics; Related artifacts: Analytics strategy
Data flow: The movement of data from one point to another at a given level of detail, without intermediate stops at that same level of granularity. It describes how data is transported between systems or steps and is a narrower view than full data lineage.; Related terms: Data lineage, Metadata, Data ecosystem
Data integration: The work of combining and synchronizing data between sources and targets so processes see consistent information.; Related terms: Data Integration and Interoperability, ETL, Enterprise data integration, Data flow
Data Integration and Interoperability: The discipline of moving and combining data across systems so processes can share timely, consistent information. It spans batch and real-time patterns, canonical models, and platform integration architecture.; Related terms: Data integration, Enterprise data integration, ETL, Canonical data model
Data lake: A storage pattern that ingests diverse raw and refined datasets for exploration and analytics, often with flexible schema-on-read.; Related terms: Big data, Big Data and Data Science, Data platform, Data science
Data lineage: Documentation of how data moves and changes from its origin to downstream use—including custody, transformations, and controls. Lineage maps system-to-system flow with metadata so teams can verify what was delivered matches what was consumed, support impact analysis, and manage operational risk. It is broader than data flow alone and distinct from traceability and provenance, though related.; Related terms: Data provenance, Data flow, Metadata, Metadata repository; Related artifacts: Metadata and lineage
Data platform: An integrated stack of technologies for collecting, storing, managing, and analyzing data—hardware and software together—that supports processing, reporting, and business insight.; Related terms: Data architecture, Data catalog, Metadata
Data processing pipeline: The ordered sequence of processing steps that takes source data from intake through transformation to an analytical product or outcome.; Related terms: Data flow, Data lineage, Data platform
Data provenance: The record of a data item's origins, where it has traveled, what influenced it, and how it was transformed. It is related to—but distinct from—data lineage and traceability.; Related terms: Data lineage, Data flow, Metadata
Data Storage and Operations: Database platforms, storage design, backup and recovery, performance, and operational administration that keep data available and durable.; Related terms: Database administration, ACID, Data platform, Data lifecycle
Data transformation: The process of changing both the meaning and format of data when moving it from one system or context to another.; Related terms: Data flow, Data processing pipeline, Data lineage, ETL
Data virtualization: Access patterns that present integrated views without physically moving all source data.; Related terms: Data integration, Enterprise data integration, Data architecture
Data warehouse: An integrated, subject-oriented store optimized for historical analysis and reporting.; Related terms: Data Warehousing and Business Intelligence, Dimensional modeling, Business intelligence, Staging environment
Data Warehousing and Business Intelligence: Analytical stores, dimensional design, and consumption patterns that support historical reporting and decision support.; Related terms: Data warehouse, Business intelligence, Dimensional modeling, Data dimension
Database administration: Operational care of database platforms—capacity, performance, backup, patching, and availability.; Related terms: Data Storage and Operations, ACID, Data platform
Enterprise data integration: Coordinated integration standards, patterns, and platforms applied across the enterprise rather than point-to-point connections alone.; Related terms: Data integration, Data Integration and Interoperability, Canonical data model, ETL
ETL: Extract, transform, and load—the batch pattern for moving data into targets with cleansing and reshaping.; Related terms: Data integration, Data transformation, Staging environment, Data processing pipeline
Operational data: Data that powers day-to-day business processes and systems—as distinct from analytical data used for investigation, modeling, and insight outside core operations.; Related terms: Transactional data, Master data, Data asset
Staging environment: A holding area where data lands in temporary tables immediately after extract, transform, and load (ETL) processing and before it is loaded into a target data repository.; Related terms: Data processing pipeline, Data transformation, Data platform
System of record (SOR): The authoritative source for a specified data element after validation and remediation of exceptions. An SOR repository holds screened, trusted data; for data integrity there should be only one system of record per logical data category.; Related terms: Authoritative data source (ADS), Golden record, Data element, Data quality
Transactional data: Data that records a business event as it happens at a specific point in time—such as a sale, payment, or status change.; Related terms: Data model, Data lineage, Master data

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