Enterprise Technology Terminology: D

517 results · page 13 of 26

• Data Sharing Agreement

  Data sharing agreement is a formal contract that governs how organizations exchange, access, and use data under defined legal, security, and privacy conditions. It matters in enterprise contexts because it documents roles, safeguards, and permitted purposes for inter-organizational data flows.

• Data Source Monitor

  Data source monitor is a software component that tracks the health, performance, availability, and security-relevant activity of enterprise data sources, providing metrics and alerts that support observability, incident response, risk management, and compliance for database and data platform environments.

• Data Sovereignty

  Data sovereignty is the principle that data is governed by the laws and regulatory requirements of the jurisdiction where it is collected, stored, or processed, which matters to enterprises for compliance, cloud architecture design, vendor selection, and cross-border data management.

• Data Sovereignty Policy

  Data sovereignty policy is an organization’s formal rule set for managing where data is stored and processed under specific national or regional laws, guiding architecture, cloud deployment, and controls to maintain compliance with jurisdictional and cross-border data requirements.

• Data Staging

  Data staging is the controlled intermediate environment and process where enterprises land, validate, cleanse, and transform data from multiple sources before loading it into data warehouses, data lakes, or other analytic platforms, supporting governance, quality assurance, and reliable reporting.

• Data Stewardship

  Data stewardship is the role-based discipline for managing and overseeing data assets so they remain accurate, secure, compliant, and usable across the enterprise. It assigns accountable stewards to enforce data policies, standards, and controls across systems, processes, and business domains.

• Data Stewardship Role

  Data stewardship role is a formally defined position that manages data quality, governance, and authorized use for specific data domains or datasets, supporting consistent definitions, regulatory compliance, and dependable analytics in enterprise environments.

• Data Storage

  Data storage is the process and technology stack that records, preserves, and retrieves digital data on physical or virtual media for use by enterprise applications and users, forming a core dependency for operations, analytics, compliance, and resilience strategies.

• Data Stream

  Data stream is a continuous, ordered flow of data records generated and transmitted over time for real-time processing or analysis. It matters in enterprises because it underpins event-driven architectures, monitoring, and analytics that depend on current, time-ordered information flows.

• Data Streaming Fabric

  Data streaming fabric is an architectural layer that provides a unified, governed substrate for publishing, routing, and consuming real-time data streams across distributed systems, supporting event-driven workloads, real-time analytics, and integration between operational and analytical platforms in enterprise environments.

• Data Striping

  Data striping is a storage technique that splits data into units and distributes them across multiple disks or devices to increase aggregate throughput and balance I/O load, which supports enterprise performance objectives for databases, analytics, virtualization, and other data-intensive workloads.

• Data Subject Access Request

  Data Subject Access Request is a formal request under data protection law by an individual to an organization for confirmation and access to personal data processed about them, which requires structured discovery, review, and disclosure workflows in enterprise environments.

• Data Synchronization

  Data synchronization is the governed process of keeping data values aligned across multiple systems, databases, or applications, ensuring consistency and currency for shared datasets so enterprises can maintain coherent records and predictable behavior across distributed architectures and environments.

• Data Synchronization Service

  Data Synchronization Service is a software capability that maintains consistent data across multiple systems or replicas by detecting changes and propagating updates under defined rules, which matters in enterprises that operate distributed applications, hybrid environments, and regulated data management practices.

• Data Taxonomy

  Data taxonomy is a structured classification scheme that organizes enterprise data into defined categories and hierarchies to support consistent labeling, governance, access control, and reuse across systems, enabling alignment between business domains, security policies, and metadata-driven data management practices.

• Data Telemetry Pipeline

  Data Telemetry Pipeline is a structured process that collects, transports, and prepares machine-generated telemetry data, such as logs, metrics, and traces, for monitoring, analytics, and governance in enterprises, enabling observability, incident analysis, capacity planning, and compliance-related investigations.

• Data Throughput

  Data throughput is the measured rate at which a system, network, or interface successfully moves or processes data per unit of time, which matters in enterprises for planning capacity, evaluating performance, and validating that infrastructure supports application and security requirements.

• Data Tiering

  Data tiering is a data management method that classifies data by access and performance needs and assigns it to different storage tiers, helping enterprises control storage costs while meeting defined service levels, retention requirements and governance policies across environments.

• Data Timeliness Metric

  Data timeliness metric is a quantitative measure of how current and promptly available data is relative to defined business or technical requirements. It matters because enterprises rely on it to validate whether data pipelines and platforms deliver information within expected time frames.

• Data Tokenization

  Data tokenization is a data protection technique that replaces sensitive values with non-sensitive tokens while preserving data format, allowing enterprises to operate on tokenized data, reduce regulatory exposure, and limit locations where clear-text regulated or personal data is stored and processed.