Enterprise Technology Glossary

6,173 results · page 78 of 309

• Data Sanitization

  Data sanitization is a controlled process that permanently removes or alters data on storage media to prevent recovery, used by enterprises to meet security, privacy, and compliance requirements when decommissioning, reusing, or disposing of systems and devices.

• Data Scenario Modeling

  Data scenario modeling is a structured analytical method that uses quantitative models and computational experiments to explore alternative future or hypothetical conditions, helping enterprises support planning, risk management, and decision-making under uncertainty within governed data and analytics environments.

• Data Schema

  Data schema is a formal specification of how data is structured and constrained in a database or data platform, which enables consistent storage, validation, querying, and integration of data across enterprise applications, analytics systems, and governance processes.

• Data Schema Definition Language

  Data schema definition language is a formal syntax used to define the structure, data types, and constraints of data in databases or data formats, enabling validation, interoperability, governance, and controlled change management across enterprise systems and integration architectures.

• Data Schema Drift

  Data schema drift is the unplanned change of data structures, fields, or types between producers and consumers over time, which matters in enterprises because it can disrupt pipelines, degrade analytics reliability, and require governance, monitoring, and tooling to manage.

• Data Schema Registry

  Data schema registry is a centralized service that stores and versions data structure definitions for messages and datasets, allowing enterprises to enforce data contracts, validate compatibility, and manage schema evolution across distributed applications, event streams, and analytics platforms.

• Data Schema Validator

  Data Schema Validator is a software component that checks whether data conforms to a defined schema, enforcing structural and type constraints so enterprises can maintain data quality, uphold data contracts, and reduce integration and ingestion errors across systems.

• Data Science

  Data science is a multidisciplinary field that applies statistical, computational, and domain-specific methods to extract and operationalize knowledge from data, enabling enterprises to build models for prediction, classification, forecasting, and decision support within governed data and analytics environments.

• Data Scrubbing

  Data scrubbing is the process of detecting, correcting, or removing inaccurate, incomplete, duplicate, or inconsistent data so enterprises can maintain reliable datasets for analytics, reporting, integration, and governance across data warehouses, data lakes, operational systems, and master data environments.

• Data Security

  Data security is the set of processes, technologies, and governance controls that protect enterprise data from unauthorized access, alteration, or loss while maintaining confidentiality, integrity, and availability, and supporting compliance, auditability, and reliable use of data across systems and environments.

• Data Security Analyst

  Data security analyst is a cybersecurity role focused on monitoring, assessing, and improving controls that protect organizational data from unauthorized access, misuse, or loss, supporting regulatory compliance, risk reduction, and the confidentiality, integrity, and availability of enterprise information assets.

• Data Security Layer

  Data security layer is an architectural control layer that centralizes and enforces security and governance policies on data access and usage across heterogeneous systems, helping enterprises apply consistent controls, meet regulatory requirements, and monitor data usage without embedding security separately in each platform.

• Data Security Policy

  Data security policy is a documented set of organizational rules and controls governing how data is protected, accessed, and managed, which matters in enterprises because it anchors security architecture, regulatory compliance, risk management, and consistent handling of data across systems and teams.

• Data Security Posture Management

  Data security posture management is a set of processes and tools that continuously assess and monitor data security controls across cloud and hybrid environments to detect misconfigurations, access risks, and policy violations and to support compliance and governance requirements.

• Data Segmentation

  Data segmentation is the process of dividing enterprise data into defined groups based on attributes such as sensitivity, domain, or geography, enabling differentiated access control, governance, and compliance policies that align data handling with security, regulatory, and business requirements.

• Data Serialization Format

  Data serialization format is a defined way to encode structured data as bytes or text so different systems can store, transmit, and reconstruct it reliably, which supports interoperability, performance, and governance in enterprise applications, data platforms, and distributed architectures.

• Dataset Dependency Tracking

  Dataset dependency tracking is the practice of recording and analyzing relationships between datasets, pipelines, schemas, and consuming applications so enterprises can perform impact analysis, support governance and compliance, and manage change across data warehouses, data lakes, and analytics environments.

• Datasets

  Datasets are structured collections of related data values, defined by a schema and metadata, that enterprises use as atomic units for storage, governance, analytics, and AI workloads across databases, data warehouses, data lakes, and other data management platforms.

• Dataset Sharding

  Dataset sharding is a data management technique that divides a large dataset into smaller, independent shards distributed across multiple nodes, enabling horizontal scaling, performance management, and regional or tenant isolation for enterprise databases and analytics platforms.

• Dataset Versioning

  Dataset versioning is the controlled management of immutable dataset states over time, enabling traceability, reproducibility, and governance for analytics and machine learning in enterprise environments. It matters because it supports auditability, rollback, and coordinated change across data-dependent applications and workflows.