Enterprise Technology Terminology: D

517 results · page 6 of 26

• Data Hall Layout

  Data hall layout is the planned arrangement of racks, aisles, and supporting power and cooling infrastructure inside a data center room, used by enterprises to meet capacity, availability, efficiency, safety, and compliance requirements for hosted IT equipment.

• Data Impact Analysis

  Data impact analysis is a structured assessment that evaluates how changes to data or data processing affect data quality, security, privacy, compliance, and operations, enabling enterprises to make informed architectural and governance decisions and to document risk treatment for regulators and auditors.

• Data Infrastructure

  Data infrastructure is the combined hardware, software, and network environment that enables enterprises to collect, store, process, secure, and access data. It matters because it underpins analytics, operations, governance, and compliance across on-premises, cloud, and hybrid technology estates.

• Data Ingestion

  Data ingestion is the controlled process that collects and transports data from diverse sources into enterprise data platforms for storage, processing, and analytics, enabling governance, traceability, and timely availability of information for reporting, compliance, and operational and analytical workloads.

• Data Ingestion Pipeline

  Data ingestion pipeline is a set of processes and components that move data from multiple internal and external sources into target platforms for analysis and operations, while enforcing reliability, governance, and data quality required in enterprise environments.

• Data Ingestion Service

  Data ingestion service is a software or cloud service that collects and loads data from various internal and external sources into enterprise data platforms, enforcing formats, controls, and monitoring so organizations can operate governed, reliable pipelines for analytics and AI workloads.

• Data Integration

  Data integration is the process of combining and reconciling data from multiple enterprise systems into a unified, consistent view, enabling reliable analytics, reporting, and operations across data warehouses, data lakes, and applications in on-premises, cloud, and hybrid environments.

• Data Integration Hub

  Data integration hub is a centralized data exchange platform that manages publication, subscription, and distribution of data among multiple systems using standardized interfaces, metadata, and governance, enabling controlled, reusable data sharing for analytics, operations, compliance, and cross-domain collaboration in enterprises.

• Data Integration Platform

  Data integration platform is enterprise software that connects disparate data sources and targets, manages extraction and transformation, and delivers consistent datasets for analytics, applications, and governance, enabling controlled, repeatable data movement and combination across on-premises, cloud, and hybrid environments.

• Data Integrity

  Data integrity is the property that data remains accurate, complete, consistent, and unaltered except through authorized processes, and it matters in enterprises because it underpins reliable transactions, analytics, regulatory compliance, and trustworthy security and governance practices across systems and data pipelines.

• Data Integrity Monitor

  Data Integrity Monitor is a control that observes and compares data, files, or configurations against baselines or policies to detect unauthorized or unexpected changes, supporting security monitoring, change control, regulatory compliance, and the reliability of enterprise information systems.

• Data Integrity Verification

  Data integrity verification is the process and control framework that confirms enterprise data remains accurate, complete, and unaltered from its expected state, supporting security, regulatory compliance, reliable analytics, and trustworthy operations across storage, processing, and transmission environments.

• Data Intelligence

  Data intelligence is the practice of applying analytics and artificial intelligence methods to enterprise data to discover, manage, and deliver actionable information for decisions, supporting governance, compliance, risk management, and operational and strategic planning across data platforms and business domains.

• Data Interoperability Layer

  Data interoperability layer is an architectural construct that provides shared schemas, semantics, and mediation services so heterogeneous systems can exchange and use data consistently, supporting multi-system integration, governance, and cross-organization data sharing in complex enterprise and regulated environments.

• Data in Transit

  Data in Transit is digital information moving between systems or locations over networks or communication channels and matters because enterprises must protect its confidentiality, integrity, and authenticity during transmission to meet security, compliance, and operational requirements.

• Data Knowledge Graph

  Data knowledge graph is a graph-based semantic data layer that represents enterprise entities, relationships, and context in a machine-interpretable model, enabling unified data integration, governance, and analytics across heterogeneous systems for architects, data platform owners, and security and technology leaders.

• Data Labeling

  Data labeling is the process of assigning structured annotations or tags to raw enterprise data so machine learning models and analytics systems can use it reliably, which supports model training quality, governance controls, and operational consistency across AI and data platforms.

• Data Lake

  Data lake is a centralized repository that stores large volumes of raw, structured and unstructured data in its native format, enabling enterprises to support analytics, machine learning, and governance use cases across diverse datasets from multiple systems and sources.

• Data Lakehouse

  Data lakehouse is a data management architecture that unifies data lake storage with data warehouse-style governance and SQL analytics, allowing enterprises to run BI, reporting, and machine learning on a single, governed repository of structured and unstructured data.

• Data Leakage Detection

  Data leakage detection is the set of monitoring and control capabilities that identify unauthorized exposure or movement of sensitive data, enabling enterprises to enforce protection and compliance policies across networks, endpoints, cloud services, and business applications.