Enterprise Technology Glossary

6,173 results · page 77 of 309

• Data Realism Metric

  Data Realism Metric is a quantitative measure that compares synthetic, anonymized, or test data to real data to assess fidelity of statistical distributions, relationships, and behaviors, supporting enterprise decisions on whether non-production data is suitable for analytics, modeling, development, or testing use.

• Data Recovery Point Objective

  Data Recovery Point Objective is a defined maximum tolerable amount of data loss, expressed as elapsed time between the last recoverable copy and a disruption, used by enterprises to design and evaluate backup, replication, and business continuity strategies.

• Data Redaction

  Data redaction is a data protection technique that obscures or removes sensitive fields from data, documents, or system outputs while keeping non-sensitive content usable, helping enterprises limit exposure of regulated or confidential information and support compliance and least-privilege access policies.

• Data Relationship Mapping

  Data relationship mapping documents how data entities and attributes connect and interact across systems and domains in an enterprise, enabling traceability, impact analysis, and governance for architecture design, regulatory compliance, security controls, and data quality management.

• Data Reliability Framework

  Data reliability framework is a structured approach that defines policies, processes, controls, and technical practices to ensure enterprise data remains accurate, complete, consistent, timely, and trustworthy, enabling dependable analytics, regulatory reporting, and operations across complex data platforms and pipelines.

• Data Reliability Score

  Data reliability score is a quantitative metric that represents how trustworthy a dataset or data pipeline is based on measured quality, consistency, timeliness, and system reliability attributes, supporting governance, consumption decisions, and operational monitoring in enterprise data environments.

• Data Replication

  Data replication is the controlled copying and maintenance of data across multiple systems, sites, or regions to support availability, resilience, and performance objectives in enterprise environments, while aligning with defined recovery, governance, and operational requirements.

• Data Replication Controller

  Data replication controller is a component that manages and monitors how data is copied and synchronized across storage systems or sites, helping enterprises enforce availability, consistency, and recovery objectives within storage, database, and disaster recovery architectures.

• Data Replication Service

  Data replication service is a software or cloud capability that copies and synchronizes data across multiple systems or locations to support availability, disaster recovery, and scale-out access, which matters for enterprise reliability, resilience planning, and distributed data architectures.

• Data Residency

  Data residency is the defined geographic location where an organization’s data is stored and processed, used by enterprises to comply with jurisdictional laws, regulatory requirements, and contractual obligations that govern where data may reside and how it may be managed.

• Data Residency Control

  Data residency control is the set of technical and governance mechanisms an enterprise uses to ensure data is stored, processed, and accessed only within permitted jurisdictions, enabling compliance with data localization, privacy, and contractual requirements across cloud, hybrid, and on-premises environments.

• Data Residency Policy

  Data residency policy defines an organization’s rules for where data may be stored and processed geographically and under which jurisdictions, so that architectures, cloud regions, and data flows align with applicable laws, contracts, and internal governance requirements for data location and cross-border transfers.

• Data Retention

  Data retention is the policy and technical practice that defines how long organizations keep data and under what conditions, guiding storage, deletion, and archival decisions to meet legal, regulatory, security, and operational requirements in enterprise environments.

• Data Retention Policies

  Data retention policies define documented rules for how long an enterprise keeps specific categories of data and how it stores, archives, and deletes them to meet legal, regulatory, and business requirements for compliance, risk management, and storage governance.

• Data Retention Policy

  Data retention policy is a documented rule set that defines how long an organization keeps different data types and how it archives or disposes of them to meet legal, regulatory, security, and operational requirements in enterprise environments.

• Data Retention Schedule

  Data retention schedule is a documented policy that specifies how long an organization keeps defined data categories and what actions it performs at the end of that period, supporting compliance, cost control, and structured data lifecycle management in enterprise environments.

• Data Rights Automation Engine

  Data rights automation engine is a software capability that automates the intake, routing, and fulfillment of data subject rights and related data access requests, enabling organizations to apply regulatory policies consistently, coordinate actions across data systems, and maintain auditable compliance records.

• Data Risk Assessment

  Data risk assessment is a structured process enterprises use to identify, analyze, and evaluate risks to data confidentiality, integrity, and availability, providing a documented basis for selecting controls, meeting regulatory expectations, and prioritizing security, privacy, and data governance decisions.

• Data Sampling

  Data sampling is the statistical selection of a subset of records from a larger dataset to estimate characteristics of the full population. It matters in enterprise environments because it manages cost, performance, and scalability constraints in analytics, observability, and data platforms.

• Data Sampling Engine

  Data sampling engine is a component that programmatically selects subsets of data from larger sources based on defined sampling rules and rates, helping enterprises manage storage, cost, and performance while preserving usable data for monitoring, analytics, and governance.