Alation Glossary

AI accuracy is the proportion of a model’s outputs that match a defined ground truth for the task at hand.

AI agents combine AI with automation to make decisions, work collaboratively, and complete tasks with minimal human intervention.

AI governance is the framework of policies, regulations, and best practices that ensure artificial intelligence (AI) is developed, deployed, and managed responsibly.

Active metadata is a continuously updated, graph-based layer that unifies technical details, business context, and real-world usage signals to improve discovery, governance, trust, and AI readiness.

An Agentic Knowledge Layer is a unified, metadata-driven foundation that enables AI agents to access, understand, and act on enterprise data with precision and trust. 

Alation Compose is an intelligent SQL editor integrated with a data catalog to provide real-time guidance, collaboration, and self-service as SQL experts and business users write, share, and optimize queries without compromising compliance and data governance.

BCBS 239, formally known as the Basel Committee on Banking Supervision's (BCBS) standard number 239, is a global regulatory framework established in January 2013.

Cloud migration refers to the process of moving digital assets like data, workloads, IT resources, or entire applications from an on-premises or legacy infrastructure to a cloud computing environment.

Critical Data Elements (CDEs) are the essential data points that enable an organization to operate efficiently, make informed decisions, and comply with regulatory requirements.

A data catalog is a centralized repository that stores metadata about an organization’s data assets. It helps users find, understand, and trust the data they need. Serving as an organized inventory, it captures details like data sources, formats, quality, lineage, and ownership.

Data classification is the practice of evaluating and organizing data into categories for more efficient retrieval, management, security, and more.

Data culture is an organizational mindset that supports, nurtures, and enables data-driven decision-making.

Data curation is the active and continuous management of data throughout its lifecycle.

Data governance ensures that data is accurate, secure, usable, and compliant. It provides a framework to manage data throughout its lifecycle, from creation to deletion. The goal is to create a trusted data foundation that supports decision-making, innovation, and competitive advantage.

Data intelligence is a system to deliver trustworthy, reliable data. Very simply put, it is intelligence about data.

Data lineage provides a comprehensive view of data’s journey, tracing its origin and documenting its movements from creation and ingestion to transformation, reporting, and beyond.

Data Mesh is a revolutionary approach to data architecture that addresses the limitations of traditional, centralized data management systems.

A data pipeline is the sequence of processes used to collect, transform, process, and deliver data between sources and destinations, typically to store, analyze, or report on the data.

A data product is a curated data asset that is discoverable, reusable, governed, and designed to generate business value, making it easy for data consumers (workers, applications, AI models, etc.) to find, trust, and leverage appropriate data.

Data quality is commonly defined as the measure of how well data meets expectations around accuracy, validity, completeness, consistency, and more.

Data search and discovery is the process of locating, identifying, and understanding data assets within an organization to facilitate informed decision-making.

A data steward oversees specific data assets within an organization, ensuring they are properly managed and utilized.

The General Data Protection Regulation (GDPR) is a European Union (EU) law that governs how organizations collect, use, store, and protect citizens’ personal data and defines individuals’ rights over their personal information.

Metadata is information that describes other information or, more simply, data about data.

Model governance is the set of policies, roles, and controls that ensure AI and analytical models are built, validated, deployed, monitored, and retired responsibly throughout their lifecycle.

Self-service analytics empowers people across an organization to discover, access, and analyze data without assistance.

Semantic consistency ensures that data is conceptually meaningful and uniformly understood across departments, systems, and stakeholders within an organization.