Enterprise Technology Terminology: D

517 results · page 8 of 26

• Data Mediation Platform

  Data mediation platform is an intermediate software layer that standardizes, enriches, and routes data between heterogeneous systems, enabling consistent formats, centralized transformation logic, and policy enforcement for billing, analytics, and compliance use cases in complex enterprise environments.

• Data Memory

  Data memory is the hardware or software-managed working storage that holds data values, variables, and intermediate results during program execution, which matters in enterprise computing for performance, reliability, and security of applications, platforms, and in-memory processing architectures.

• Data Mesh

  Data mesh is an enterprise data management approach that assigns domain teams ownership of data as a product, supported by a self-serve data platform and federated governance, to manage distributed data, quality, and access across complex organizational and technology environments.

• Data Mesh Architecture

  Data mesh architecture is a decentralized data management approach that assigns domain-based ownership for data products while enforcing shared standards, governance, and self-serve platform capabilities, enabling enterprises to manage analytics and AI data across many domains with consistent controls and interoperability.

• Data Migration

  Data migration is the controlled transfer of data between systems, formats, or environments within an enterprise, conducted through defined processes to preserve integrity, security, and usability while supporting system changes, cloud adoption, consolidation efforts, and regulatory and governance requirements.

• Data Migration Service

  Data migration service is a software-based capability that automates and governs the movement of data between systems or environments, enabling secure, controlled transitions during modernization, consolidation, or cloud adoption while maintaining data quality, integrity, security controls, and business continuity requirements.

• Data Minimization

  Data minimization is a privacy and data protection principle that limits personal data collection, processing, and retention to what is adequate, relevant, and necessary for defined purposes, helping enterprises meet regulatory requirements and reduce exposure of unnecessary personal information.

• Data Mirroring

  Data mirroring is a data protection method that maintains near-real-time duplicate copies of data on separate systems or sites, enabling high availability, controlled failover, and disaster recovery to support enterprise continuity, resilience objectives, and regulatory or contractual uptime requirements.

• Data Mobility Platform

  Data mobility platform is an integrated software or software-defined system that manages secure, policy-governed movement and placement of data across heterogeneous storage, cloud, and edge environments, supporting workload portability, governance, and compliance in enterprise and hybrid cloud architectures.

• Data Modeling

  Data modeling is the formal process of defining and documenting how data entities, attributes, and relationships organize within information systems so enterprises can implement consistent schemas, governance, integration, analytics, and controls across operational and analytic data platforms.

• Data Modeling Framework

  Data modeling framework is a structured set of methods and artifacts that organizations use to design, document, and govern data models across conceptual, logical, and physical layers, supporting consistent data definitions, change management, and alignment between business requirements and implemented data structures.

• Data Modeling Layer

  Data modeling layer is an abstraction layer in enterprise data architectures that defines and exposes consistent, business-ready logical data models, allowing organizations to standardize metrics and entities while insulating analytics and applications from changes in underlying data sources and storage.

• Data Movement Minimization

  Data movement minimization is an architectural and governance approach that reduces how much and how often enterprise data is transferred between systems and locations, helping control cost, latency, attack surface, and regulatory exposure while preserving required processing and analytics.

• Data Networks

  Data networks are interconnected digital communication systems that move data between devices, applications, and locations using standardized protocols, enabling enterprise connectivity, application delivery, and secure access to computing and data resources across campuses, data centers, WANs, edge sites, and cloud environments.

• Data Normalization

  Data normalization is the process of organizing and transforming data to reduce redundancy, enforce consistency, and align values to common scales, enabling reliable storage, analysis, and machine learning across enterprise databases, data warehouses, and analytics platforms.

• Data Obfuscation

  Data obfuscation is a data protection technique that alters or masks data to limit unauthorized disclosure while keeping it usable for defined enterprise purposes. It matters because it enables analytics, testing, and data sharing while constraining exposure of sensitive or regulated information.

• Data Observability

  Data observability is a set of practices and tools that monitor and analyze the health of enterprise data and data pipelines, supporting reliable analytics, governance, and compliance by providing continuous visibility into data quality, reliability, and pipeline behavior.

• Data Observability Platform

  Data observability platform is enterprise software that monitors and analyzes the health, quality, and reliability of data across pipelines and storage systems, enabling organizations to detect anomalies, manage incidents, and maintain service levels for analytics, reporting, and machine learning workloads.

• Data Ontology

  Data ontology is a formal semantic model that defines enterprise data concepts, attributes, and relationships in a machine-interpretable way, enabling consistent meaning, interoperability, and reasoning across heterogeneous systems for integration, analytics, governance, and regulatory or policy-aligned data use.

• DataOps

  DataOps is an organizational practice that applies agile, DevOps, and process control principles to how enterprises build and operate data pipelines and analytics, enabling more reliable, automated, and governed delivery of data needed for reporting, decision support, and machine learning.