Glossary of Key Terms

Merging and optimizing data and workflows across two or more disparate applications.

A software-driven process that allows organizations to analyze raw data from multiple sources and use the resulting information to make informed business decisions.

Connecting multiple cloud-based business systems with each other and with on-premises applications to create a single, cohesive infrastructure.

Integrations across systems using a visual interface to deploy rather than modifications to the codebase; allows nontechnical users to make changes and create reports without IT assistance.

Includes all of the methodologies, metrics, processes, and systems used to track and manage business performance at the enterprise level.

Extracting insights from one or more data sources that can be used to identify patterns, monitor performance, drive decisions, and shape business outcomes.

Improving overall data quality by correcting or deleting incorrect, inaccurate, irrelevant, and missing data.

The strategies, policies, processes, and technologies used to ensure business data stays in compliance with regulations and adheres to corporate rules

Implementation of strategic policies and procedures that allow organizations to control their business data across systems.

A measure of the utility of data to serve an intended purpose based on characteristics including accuracy, completeness, consistency, and reliability.

The process of creating consistency among data records from source to a target and ensuring harmony of the data over time.

Process of collecting, replicating, and transmitting large datasets from one system to another.

Systematic checks that are built into a system to ensure the data being entered and stored is accurate and has logical consistency.

Data layer that integrates data from across multiple data sources for analysis and business intelligence.

Centralized repository that stores aggregated structured data from disparate sources to support reporting and analytics.

Combination of tools, strategies, and processes that support capturing, managing, storing, and delivering data throughout its lifecycle.

Processes and tools that monitor performance across the enterprise that allow stakeholders to analyze, understand, and report on business data.

Software designed to improve efficiency through orchestration and coordination of business strategies and operations.

A consistent and uniform set of identifiers and extended attributes used to describe the core entities of the enterprise, including customers, suppliers, hierarchies, and chart of accounts.

Creation of a single source of truth for master data from across the business’s internal and external data sources and applications.

Structured reference data that helps sort and identify attributes of information assets and add the context needed to govern systems and data.

Management of policies and processes that ensure metadata can be integrated, accessed, maintained, and analyzed across the organization.

Integrated management of all domains or data types in a single, centralized platform.

Software that is delivered via the internet or the cloud rather than being physically installed on-premises.

Upstream or downstream application where data changes that receive data updates after changes have been verified, standardized, and rationalized.

Data that cannot be stored in a traditional relational database because it does not conform to a predefined data model and lacks identifiable structure or architecture.