AI TOOL PROFILE
Atlan: Enterprise Metadata Management and AI Context Layer
- Data and Analytics
- Data Management
- Enterprise companies
- Mid-market companies
- Enterprise data teams
- AI-forward organizations
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Mid-market companies, Enterprise data teams, AI-forward organizations
- Key use cases
- Supporting AI Readiness, Enterprise Data Governance, Data Discovery and Reuse, Impact Analysis
- Integrations
- Snowflake, dbt, BigQuery, Looker, Oracle
- Official website
- Visit atlan official website
How AI is used
Atlan is a metadata management platform designed to act as a context layer for an organization's data estate. It unifies business logic, data definitions, and institutional knowledge into a living graph, which may be used by humans and AI agents to understand the meaning and origin of data assets.
The software is built for enterprise and mid-market data teams, including those in sectors such as finance, healthcare, and manufacturing. It supports the transition from manual data cataloging to active metadata management, allowing domain experts to certify and annotate data.
Key capabilities include tracking data lineage at the column level and using connectors to pull context from warehouses and BI tools. By organizing data into data products, companies can manage trusted assets in a marketplace format to support discovery and reuse.
Buyers should confirm if their current technical stack aligns with Atlan's supported connectors and whether they have the internal domain expertise required to certify the metadata that drives the platform's AI capabilities.
Key Features
Enterprise Data Graph
Unifies business systems, SQL query history, and BI definitions into a single graph of the data estate.
Column-Level Lineage
Provides visibility into data flow to help teams find root causes and assess the impact of changes.
Data Marketplace
Supports packaging data tables, dashboards, and metrics into reusable data products.
AI Governance
Provides a framework to govern how AI agents access and use business logic and trusted data.
Metadata Lakehouse
An Iceberg-native architecture that includes vector storage and a knowledge graph for business domains.
Data Quality Tools
Includes tools to define and monitor the reliability and completeness of data assets.
Use Cases
Supporting AI Readiness
Building a context layer that provides AI agents with the business logic and definitions needed to answer complex business questions.
Enterprise Data Governance
Establishing a shared language across the organization by linking business terms to technical data assets.
Data Discovery and Reuse
Creating a searchable marketplace of certified data products to help reduce duplicate dashboards.
Impact Analysis
Using column-level lineage to understand how changes in a data source may affect downstream reports.
Integrations
- Snowflake
- dbt
- BigQuery
- Looker
- Oracle
FAQ
What is the 'context layer' in Atlan?
- It is a system that unifies an organization's data, business logic, and institutional knowledge so that AI agents and employees have a shared understanding of business terms.
Who is Atlan designed for?
- Atlan is designed for data teams within mid-market and enterprise-scale companies, particularly those in sectors like finance, technology, and healthcare.
How does Atlan help with AI governance?
- It provides business context and governance frameworks designed to ensure AI agents act on trusted, certified data.
Source category: Data & Analytics
Source subcategory: Data Management
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