--- title: "Data Quality | Atlan | Context Layer for AI" url: "https://atlan.com/data-quality-studio/" description: "Data quality tools told humans which data to trust. Atlan is the context layer for AI - quality scores that reach every agent automatically, before they act on your data." keywords: "data, data quality, data quality studio, atlan, atlan product" --- > Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/ **Data quality platforms tell humans what data to trust. Atlan is the context layer for AI that makes sure every agent knows the same thing.** Before an AI agent acts on your data, it needs to know whether to trust it: is this asset complete, fresh, certified? Atlan scores every critical asset automatically and propagates that quality context along lineage to every downstream agent, so they know which data to trust before they act. [Book a Demo](https://atlan.com/forms/talk-to-sales-contact/) / [See How It Works](https://atlan.com/context-layer-demo/) ## Data quality was built to catch bad data for humans. AI needs to know what's trustworthy too. Data quality platforms were built for data engineers and stewards: monitoring pipelines, alerting on anomalies, scoring freshness and completeness so humans could decide which data was safe to use. AI agents don't receive quality alerts or review monitoring dashboards before querying. Without quality context surfaced through the context layer, an agent can't know whether its data is complete, fresh, or certified. It answers from stale tables, missing values, and assets your team flagged as unreliable months ago, not because the model is wrong but because it never knew the data was. Atlan is the [context layer for AI](https://atlan.com/context-layer/). The context layer is broader than quality (governance, semantic definitions, lineage, institutional business knowledge), but quality context is foundational: every agent needs to know which data to trust. [Vera, Atlan's Data Quality agent](https://atlan.com/context-agents/), continuously scores critical assets on completeness, accuracy, and freshness. Those scores propagate along [Data Lineage](https://atlan.com/data-lineage/) to every downstream asset and AI agent. [Context Engineering Studio](https://atlan.com/context-engineering-studio/) tests whether agents produce correct answers against your existing dashboards and business questions, surfacing when quality gaps cause wrong answers before users find out in production. ## From data quality monitoring to quality context every agent knows - **Quality context scored automatically.** Vera scores every critical asset on completeness, accuracy, and freshness, continuously, at scale. No manual DQ rule-writing, no monitoring dashboards to review before an agent queries. Quality context is embedded in the asset itself. [Meet Vera](https://atlan.com/context-agents/) - **Quality context propagates along lineage.** When Vera scores an asset as incomplete or stale, every downstream asset and AI agent that depends on it inherits that signal. Agents know which data fails quality thresholds before they act. [Data Lineage](https://atlan.com/data-lineage/) - **Agent outputs validated before production.** Context Engineering Studio auto-generates test suites from your existing dashboards and business questions and tests agent responses against expected answers. Failures surface missing context, wrong numbers, and answers that don't hold up before users see them. [Context Engineering Studio](https://atlan.com/context-engineering-studio/) ## Leading AI teams use Atlan to connect context Trusted by $10T in enterprise value. Customer videos: Sridher Arumugham (DigiKey: Context Readiness), Kiran Panja (CME Group: Context at Speed), Andrew Reiskind (Mastercard: Context by Design), Mauro Flores (Virgin Media O2: Context for All). ## Score: quality context, scored automatically, compounded continuously Vera scores every critical asset on completeness, accuracy, and freshness, and every score compounds with governance, lineage, and semantic context so AI agents get a complete picture of what they can trust. Vera works alongside eight other Context Agents: | Agent | Role | What it does | |---|---|---| | Scout | Usage Intelligence | Ranks assets by what your team actually queries. | | Scribe | Description Writer | Writes descriptions from SQL patterns and lineage. | | Lexis | Glossary Builder | Builds your business glossary from column patterns. | | Doc | Readme Author | Synthesizes signals into comprehensive dataset readmes. | | Nexus | Terms Linker | Links every column to the business term analysts use. | | Sage | Metric Arbiter | Finds conflicting metric definitions and locks one answer. | | Atlas | Domain Classifier | Tags every asset with its business domain at scale. | | Vera | Quality Scorer | Scores every critical asset on completeness and freshness. | | Orion | Ontologist | Maps the ontology connecting every domain and metric. | ### The journey: data catalogs were built for humans who never documented them - **The first copilot.** In 2023 Atlan launched the first AI documentation agent, Atlan AI. It wrote descriptions automatically, but accuracy was 75%. - **We hit a wall.** Accuracy at scale needed AI to access lineage, query history, usage patterns, and relationships between assets. Atlan stored all of that but AI couldn't use it, so Atlan rebuilt the foundation: the Context Lakehouse. - **The new reality.** Context agents now outperform humans on quality; customers say agent-written descriptions are more accurate and complete than their teams' manual work. Acceptance rate today: **90%+**. AI descriptions applied: **350K+**. Start your AI-readiness sprint: Context Agents can get you to AI readiness in 30 days. [Book a Strategy Session](https://atlan.com/forms/talk-to-sales-contact/) ### Rollout in 30 days, not 12 months - **Start with what matters.** Context Agents identify your Gold Layer, Popular BI, Popular SQL, and upstream dependencies first, enriching the assets people use before the long tail. - **AI scores every output.** A composite confidence score across accuracy, clarity, style, and completeness. High-confidence outputs auto-apply; lower-confidence outputs route to humans. - **Humans decide and govern.** Stewards shift from documentation to certification: sampling, validating, and resolving cases that need judgment. One click, not 847 manual reviews. ## Validate: know your agents produce correct answers before they ship Context Engineering Studio auto-generates test suites from your existing dashboards and queries, comparing agent outputs against expected answers. When quality gaps cause wrong results, test failures surface the issue before production. The hardest problem in enterprise AI is knowing whether your agent works accurately in real business scenarios and what context it's missing. Context Engineering Studio reads BI dashboards and SQL queries for context, generates hundreds of questions your AI agent needs to answer correctly, and turns them into an evaluation suite. - **Auto-generate tests.** Existing dashboards and reports signal the questions users will ask; the studio uses them to simulate test questions and compare generated answers with expected ones. - **Failure traces.** When an evaluation fails, the studio identifies what's missing (a relationship the model doesn't know, a synonym it can't resolve, a filter it should apply by default) and suggests a specific update to the context model. - **Human ON the loop.** AI generates the test questions, runs the simulations, finds the gaps, and suggests fixes. Humans verify and refine, adding institutional knowledge to the context repository. "Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs." - Joe DosSantos, VP Enterprise Data & Analytics, Workday ## Connect: quality scores that travel with your data When Vera scores an asset as stale or incomplete, that signal propagates to every downstream asset and AI agent that depends on it, so agents know which data to trust without checking a monitoring dashboard. A living graph that compounds everything: - **Quality compounds along lineage.** When a quality check fails upstream, lineage shows every downstream dashboard, pipeline, and AI agent affected. Root cause analysis goes from days to minutes. - **Governance propagates along lineage.** Tag a column as PII once; lineage propagates that classification downstream and syncs bi-directionally with Snowflake and Databricks. - **Impact analysis travels along lineage.** Before a data engineer ships a change, Atlan shows the full blast radius inside the GitHub or GitLab pull request. - **AI agents read the full context chain.** Through Atlan's MCP Server, an agent checking a column gets back provenance, quality score, governance policy, and ownership in one call. "With Google DataPlex, lineage only showed part of the story. Our business operates across many systems and we needed complete, enterprise-wide lineage. Atlan's platform was more intuitive, delivered on complex end-to-end lineage, and had a strong library of connectors. We also used OpenLineage for Spark jobs to tie operational lineage to our data platform." - Zenul Pomal, Core Data Platform & Enterprise Architecture, CME Group CME Group: **18M+** assets cataloged; **1,300+** glossary terms connected; **100+** active users. [Watch the case study](https://atlan.com/regovern-watch-center/cme-group-evaluation/) ## Industry recognition - **Leader, 2026 Gartner Magic Quadrant for Data & Analytics Governance:** "Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. The underlying metadata lakehouse architecture boosts performance, scalability, extensibility and time travel auditability." [Report](https://atlan.com/gartner-magic-quadrant-data-governance-2026/) - **Leader, 2025 Gartner Magic Quadrant for Metadata Management Solutions:** "Atlan's solution focuses on automation, allowing every action to be performed programmatically via APIs and calling via an LLM. Its core components also include a knowledge graph for business domains and vector storage & analytics, which is purpose-built for AI." [Report](https://atlan.com/gartner-magic-quadrant-metadata-management-solutions-2025/) - **Leader, The Forrester Wave: Data Governance Solutions, Q3 2025:** "Atlan offers features that are among the best in class for policy management, stewardship, and collaborative governance. Its knowledge graph and AI-powered automation support clear data ownership, surfacing policy-relevant context and automating governance workflows." [Report](https://atlan.com/know/forrester-wave-data-governance-2025/) ## Explore the platform - [Context Agents](https://atlan.com/context-agents/): quality scoring and context compounding at scale. - [Data Lineage](https://atlan.com/data-lineage/): quality context that propagates automatically to every agent. - [Context Engineering Studio](https://atlan.com/context-engineering-studio/): validate quality context before production. - [Data Marketplace](https://atlan.com/data-marketplace/): help every human find trustworthy data. ## FAQ: data quality **What is quality context for AI agents?** What AI agents need to know whether the data they're acting on is trustworthy: is this asset complete, fresh, certified? Without it, agents act on data they shouldn't trust and produce answers that fail because the underlying data failed first. Atlan surfaces it automatically: Vera scores every critical asset, scores propagate along lineage, and Context Engineering Studio tests agent outputs against your existing dashboards and business questions. **How does Vera score data quality?** Vera automatically scores critical assets on completeness, accuracy, and freshness, continuously, at scale, with no manual DQ rule-writing. Vera's scores compound with governance classifications, semantic definitions, and lineage context. High-confidence outputs auto-apply; lower-confidence cases route to human stewards. **How do quality scores reach AI agents automatically?** Atlan propagates quality context along the lineage graph. When Vera scores an asset as stale, incomplete, or below your threshold, every downstream asset and AI agent inherits that signal. Agents querying through Atlan's MCP Server receive quality context, alongside governance and semantic context, before they act. **How does Context Engineering Studio fit into data quality?** It tests whether agents produce correct answers, using your existing dashboards and queries as ground truth. When quality gaps cause wrong answers, test failures surface the issue before production. CES doesn't inspect freshness or completeness directly (that's Vera's job); it validates the downstream effect: whether agent outputs are accurate enough to ship. **Can Atlan replace our existing data quality platform?** Yes. Atlan replaces your data quality platform with the context layer for AI: Vera continuously scores every critical asset and those scores propagate to every downstream agent and analyst through Data Lineage. The manual rule-writing, monitoring dashboards, and human review workflows of traditional DQ platforms are replaced by quality context that travels with your data. Vera scores every critical asset automatically, quality context propagates along lineage, and Context Engineering Studio catches quality gaps before users do. [Book a Demo](https://atlan.com/forms/talk-to-sales-contact/) (30-min call).