Enterprise Technology Terminology: D

517 results · page 7 of 26

• Data Lifecycle

  Data lifecycle is the structured sequence of stages through which enterprise data passes, from creation and use to archival and deletion, enabling organizations to align storage, security, governance, and compliance controls with how data is handled over time.

• Data Lifecycle Management

  Data lifecycle management is a policy-based approach for governing how enterprise data is created, stored, used, protected, retained, archived, and deleted so that technical handling aligns with business, legal, compliance, cost management, and information security requirements.

• Data Lifecycle Security

  Data lifecycle security is the coordinated application of policies, controls, and technical safeguards that protect data confidentiality, integrity, and availability at each lifecycle phase, enabling enterprises to manage risk, support compliance, and maintain consistent protection across systems and environments.

• Data Lineage

  Data lineage is the recorded lifecycle of data as it moves across enterprise systems, capturing its sources, transformations, flows, and destinations. It matters because it supports governance, regulatory compliance, troubleshooting, and controlled change management for analytics and operational data platforms.

• Data Lineage Tracking

  Data lineage tracking documents and maintains an auditable record of how data originates, moves, transforms, and is used across enterprise systems, enabling traceability for governance, compliance, change impact analysis, and operational troubleshooting in complex data and analytics environments.

• Data Lineage Visualization

  Data lineage visualization is the graphical representation of data flows, transformations, and dependencies across an organization’s data environment, used to trace data origins, support impact analysis, and document end-to-end data paths for governance, audit, and operational troubleshooting.

• Data Locality

  Data locality is the placement and coordination of data and computation so processing occurs close to where data resides or within defined regions, enabling enterprises to manage latency, resource usage, regulatory constraints, and governance requirements across distributed and cloud environments.

• Data Locality Awareness

  Data locality awareness is the capability of systems and data platforms to identify where data resides across regions and infrastructure so they can optimize workload placement while enforcing data residency, sovereignty, security, and governance requirements in enterprise environments.

• Data Locality Optimization

  Data locality optimization is the practice of arranging data placement and compute execution so processing occurs close to stored data, reducing data movement and latency. It matters in enterprise architectures for improving performance, resource efficiency, and compliance-aware data placement across large-scale systems.

• Data Localization Framework

  Data localization framework is a policy and architectural construct that defines how an enterprise stores, processes, and routes data to meet jurisdictional residency, sovereignty, and cross-border transfer requirements, enabling compliant operation of applications and data platforms across multiple legal environments.

• Data Localization Requirement

  Data localization requirement is a legal or regulatory rule that compels specific data to be stored or processed within a defined jurisdiction, affecting how enterprises design infrastructure, choose cloud regions, manage cross-border data flows, and demonstrate compliance to regulators.

• Data Loss Prevention

  Data loss prevention is a security discipline and toolset that monitors and controls sensitive data across endpoints, networks, and cloud services to reduce unauthorized disclosure, support regulatory compliance, and provide governance over how enterprises handle and move protected information.

• Data Management

  Data management is the organized set of processes, architectures, and controls an enterprise uses to collect, store, govern, secure, and maintain data across its lifecycle so that operational systems, analytics, and regulatory reporting can rely on accurate, consistent, and auditable information.

• Data Management Body of Knowledge

  Data Management Body of Knowledge is a vendor-neutral reference framework and guidebook from DAMA International that defines standard concepts, roles, and processes for enterprise data management, supporting governance, architecture, quality, security, and compliance across an organization’s data and information assets.

• Data Manipulation Language

  Data Manipulation Language (DML) is the part of a database language that handles inserting, updating, deleting, and retrieving stored data, and it matters in enterprises because it underlies transactional applications, reporting, analytics, and governed access to operational data.

• Data Mapping

  Data mapping is the process of defining correspondences between data elements in different systems or datasets so that data can be integrated, transformed, and governed consistently, which supports interoperability, regulatory compliance, modernization efforts, and reliable analytics in enterprise environments.

• Data Mapping Engine

  Data Mapping Engine is a software component that defines and executes structured mappings between heterogeneous data schemas and formats, enabling repeatable data integration, migration, and transformation while supporting governance, reuse, and maintenance of field-level mapping logic across enterprise systems.

• Data Mapping Schema

  Data mapping schema is a formal specification that defines how data elements in one system or format correspond to those in another, enabling consistent integration, migration, and exchange of data in enterprise environments with multiple applications, databases, and data platforms.

• Data Mart

  Data mart is a subject-focused subset of a data warehouse that contains curated, structured data for a specific business domain or department, enabling targeted analytics, reporting, and governance within enterprise data platforms and business intelligence environments.

• Data Masking

  Data masking is a data protection technique that alters sensitive data into de-identified or pseudonymous values while preserving structure, enabling development, testing, analytics, and data sharing on realistic datasets while limiting exposure of personal, financial, or regulated information in enterprise environments.