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Learn data intelligence key terms, approaches, and best practices for success. Every definition held to the same standard as the platform.

36 · Terms

A-Z Sections

Last Updated: Nov 10, 2026

A08 Entries

AI AccuracyAI accuracy is the proportion of a model’s outputs that match a defined ground truth for the task at hand.AI AgentsAI agents are software systems that pursue a goal by reasoning about which steps to take, using tools to act on data and applications, and adjusting as conditions change. Where a chatbot answers, an agent acts, so its results are only as reliable as the data and context it works from.AI GovernanceAI governance is the framework of policies, regulations, and best practices that ensure artificial intelligence (AI) is developed, deployed, and managed responsibly.AI ReadinessAI readiness is an organization's ability to deploy AI use cases that work reliably in production, backed by data, governance, skills, and infrastructure that are fit for each specific use. It matters because, according to Gartner, returns from AI depend less on the sophistication of the model than on how well it is integrated, governed, and aligned with real operational needs.(1)AI StackAn AI stack is the layered set of technologies, tools, and services that enable building, deploying, and operating AI-powered applications end to end.Active MetadataActive 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.Agentic WorkflowsAn agentic workflow is an AI-driven process in which intelligent agents autonomously manage and adapt tasks to achieve goals.Alation ComposeAlation 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 .

B01 Entry

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

C03 Entries

Cloud MigrationCloud 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.Context DriftContext drift is the decay of the business context AI agents rely on (such as metric definitions, certified tables, and policies), as the business changes but that context does not.¹ Agents continue to apply a definition that was right once. Pipelines pass, quality checks stay green, and the answer arrives fluent, confident, and incorrect. Picture a quarterly review. Finance has revised “net revenue” to exclude one-time restructuring charges, and its report reflects that. An agent, working from outdated context, will still include the charges when addressing “net revenue” questions, so its figure is inaccurate. No alert fired, and no job failed. The two numbers simply disagree, and no one in the room can say why. That gap is context drift.Critical Data Elements (CDEs)Critical Data Elements (CDEs) are the essential data points that enable an organization to operate efficiently, make informed decisions, and comply with regulatory requirements.

D14 Entries

Data CatalogA 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 ClassificationData classification is the practice of evaluating and organizing data into categories for more efficient retrieval, management, security, and more.Data CultureData culture is an organizational mindset that supports, nurtures, and enables data-driven decision-making.Data CurationData curation is the active and continuous management of data throughout its lifecycle.Data GovernanceData governance is the set of policies, roles, and controls that keep data accurate, secure, and compliant, and trustworthy enough for people and AI agents to use. It defines who owns data, the rules that govern it, and how you prove they're followed, from creation to deletion. The goal is a trusted data foundation that supports decisions, innovation, and compliance. As AI agents start querying data on their own, that foundation matters more, and so does the ability to show your work.Data IntelligenceData intelligence is a system to deliver trustworthy, reliable data. Very simply put, it is intelligence about data.Data Intelligence PlatformA data intelligence platform is an enterprise software system that unifies metadata management, data governance, data lineage, and data quality into a single, integrated environment. It enables organizations to discover, understand, trust, and govern their data assets at scale—across cloud, on-premises, and hybrid environments.Data LineageData 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 MeshData Mesh is a revolutionary approach to data architecture that addresses the limitations of traditional, centralized data management systems.Data PipelineA 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.Data ProductA 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 QualityData quality is commonly defined as the measure of how well data meets expectations around accuracy, validity, completeness, consistency, and more.Data Search & DiscoveryData search and discovery is the process of locating, identifying, and understanding data assets within an organization to facilitate informed decision-making.Data StewardshipA data steward oversees specific data assets within an organization, ensuring they are properly managed and utilized.

E01 Entry

Ethical AIEthical AI refers to artificial intelligence systems designed, developed, and deployed in ways that prioritize fundamental human values—such as fairness, transparency, accountability, privacy, and societal wellbeing—above mere legal compliance.

G01 Entry

GDPRThe 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.

K01 Entry

Knowledge LayerThe Knowledge Layer is a unified, metadata-driven foundation that enables AI agents to access, understand, and act on enterprise data and knowledge with accuracy, accountability, and trust.

M03 Entries

MetadataMetadata is information that describes other information or, more simply, data about data.Metadata-aware AgentsMetadata-aware agents are advanced, autonomous systems that understand and act upon the rich contextual signals encoded in enterprise metadata.Model GovernanceModel 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.

S04 Entries

Self-Service AnalyticsSelf-service analytics empowers people across an organization to discover, access, and analyze data without assistance.Semantic ConsistencySemantic consistency ensures that data is conceptually meaningful and uniformly understood across departments, systems, and stakeholders within an organization.Semantic LayerA semantic layer is a business-friendly layer between raw data and the tools and AI agents that query it, defining metrics and relationships once so every consumer gets the same answer. The layer stores definitions and logic, not data, so “revenue” means the same thing in every dashboard, spreadsheet, and agent.Structured DataStructured data is information organized in a fixed format, such as rows and columns in a database, making it easy to store, search, and analyze.

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