Enterprise Technology Glossary

6,173 results · page 76 of 309

• Data Privacy Impact Assessment

  Data privacy impact assessment is a structured, documented process that evaluates planned or existing personal data processing for privacy risks, defines mitigations, and supports compliance with privacy and data protection laws in enterprise projects, architectures, and data governance activities.

• Data Privacy Management Platform

  Data privacy management platform is an integrated software system that centralizes and automates privacy workflows, including data discovery, mapping, consent handling, and data subject requests, so enterprises can manage regulatory compliance, documentation, and privacy policies across diverse business systems and data environments.

• Data Privacy Policy

  Data privacy policy is an organization’s formal governance document that sets rules for collecting, using, storing, sharing, and protecting personal data, enabling compliance with privacy laws, guiding technical controls, and standardizing how systems and processes handle personal information across the enterprise.

• Data Privacy Vault

  Data privacy vault is an architecture pattern and controlled repository that stores and protects sensitive data in an isolated environment, exposes only tokens or pseudonymous references to other systems, and supports centralized enforcement of privacy, security, and regulatory requirements in enterprises.

• Data Processing Unit

  Data processing unit is a specialized data-path processor that offloads networking, storage, and security tasks from server CPUs in data centers, enabling hardware-accelerated infrastructure services, workload isolation, and consistent delivery of virtualized network and storage functions across enterprise and cloud environments.

• Data Profiling

  Data profiling is the systematic analysis of enterprise data assets to compute statistics and metadata about their structure, content, and quality, enabling organizations to assess data fitness, support governance and compliance, and design more reliable integration and analytics processes.

• Data Profiling Service

  Data profiling service is a software or managed capability that analyzes datasets to summarize their structure, content, and quality. It matters in enterprises because it supports accurate integration, analytics, governance, and compliance by revealing data characteristics, anomalies, and quality issues.

• Data Protection

  Data protection is the combination of policies, processes, and controls that safeguard enterprise data from unauthorized access, alteration, loss, or destruction and help organizations meet security and privacy obligations across on-premises, cloud, and hybrid environments.

• Data Protection Officer

  Data Protection Officer is a designated compliance and governance role that oversees an organization’s adherence to data protection laws, monitors personal data processing activities, and serves as the contact point for regulators and data subjects in enterprise environments.

• Data Provenance

  Data provenance is the recorded history of data origin, movement, and transformation within and across systems, which organizations use to support traceability, compliance, auditability, and reproducibility in enterprise data platforms and governed analytics environments.

• Data Provenance Chain

  Data provenance chain is an ordered record of the origins, custody, and processing history of data across systems, used by enterprises to support traceability, governance, compliance, and reproducibility of analytics, reporting, and machine learning outputs.

• Data Pseudonymization

  Data pseudonymization is a data protection process that replaces direct identifiers in datasets with artificial identifiers while preserving a controlled technical means to re-link records to individuals. It matters because it reduces privacy risk and supports regulatory-compliant data use and sharing in enterprises.

• Data Quality

  Data quality is the degree to which data meets defined requirements for accuracy, completeness, consistency, timeliness, validity, and uniqueness for a given use, enabling reliable analytics, compliance, and operations across enterprise data platforms and governed data ecosystems.

• Data Quality Assessment

  Data quality assessment is a structured process that measures datasets against defined quality dimensions and rules to determine their suitability for enterprise reporting, analytics, operations, and compliance, providing quantifiable metrics that support governance, remediation, and risk management across data platforms.

• Data Quality Dashboard

  Data quality dashboard is a visual interface that presents quantitative measures of data quality across enterprise datasets, enabling monitoring of completeness, accuracy, consistency, timeliness, and related dimensions for governance, risk management, regulatory reporting, and reliable analytics and operational decision-support use cases.

• Data Quality Metrics

  Data quality metrics are quantitative measures that evaluate how well enterprise data meets defined quality dimensions and requirements, enabling organizations to monitor data fitness for use, quantify data-related risk, and support governance, compliance, and analytics reliability across systems and workflows.

• Data Quality Policy

  Data quality policy is an organization-wide directive that defines how data quality is measured, controlled, and remediated, ensuring data used in operations, analytics, and compliance activities meets defined standards of accuracy, completeness, consistency, timeliness, and reliability across its lifecycle.

• Data Quality Rule

  Data quality rule is a formally defined, machine-executable condition that checks whether data meets specified quality requirements, such as accuracy, completeness, and consistency, in alignment with business and regulatory policies in enterprise data platforms and governance programs.

• Data Quality Score

  Data quality score is a quantified metric that summarizes how well a dataset satisfies defined data quality dimensions, used by enterprises to monitor data fitness for use, manage data-related risk, and support governance, analytics, and operational decision-making.

• Data Quality Service

  Data Quality Service is a software capability or managed service that profiles, monitors, and remediates data quality issues so enterprise data meets defined standards for accuracy, completeness, consistency, timeliness, and validity in regulated, analytic, and operational environments.