A business leader asks an AI agent: Why did customer retention decline this quarter? Before it can deliver a useful answer, the agent needs to know how your business defines retention, which customer data to use and how that data connects to the reports your teams rely on. A fluent explanation is not enough if it starts from the wrong definition or data set.
Much of that knowledge already exists in your organization. It lives in business reports, transformation logic, data documentation and the experience of your teams. When it stays scattered, every new AI use case requires people to assemble it again. That means more preparation for developers, more validation for analysts and a longer path from a business question to a decision.
At Snowflake Summit, we introduced Horizon Context: a governed context layer within Horizon Catalog that collects metadata, enriches it with business meaning and makes it useful to people, BI tools and AI agents.
Metadata connectors for 18 sources, targeted permissions for stewards to curate descriptions and automatic propagation of descriptions to supported downstream columns are now in public preview. Together with the recently redesigned Horizon Catalog Explorer, these capabilities help teams discover, enrich and reuse existing data knowledge for AI and analytics.
With Horizon Context, teams can:
- Understand data before using it with a consistent catalog experience that brings definitions, tags and contacts to the forefront.
- Reuse existing data knowledge from reports, databases and pipelines rather than reconstructing it for each AI use case.
- Troubleshoot issues and assess change impact using connected lineage across supported sources, transformations and BI tools.
- Bring business experts into context curation without granting broader control over the underlying data.
- Reduce documentation maintenance by propagating descriptions to supported downstream columns.
- Ground AI and analytics in shared context , reducing the need to recreate knowledge in each application or prompt.
Scale business understanding alongside AI adoption #
In our customer conversations, enthusiasm for AI comes with practical expectations: help people find the right data, make its meaning clear and reduce dependence on a small group of experts. Teams value seeing the source behind a report and reusing documentation rather than maintaining it repeatedly. Those needs matter even more when people delegate data work to agents.
As AI adoption grows, maintaining a separate set of instructions and definitions for each agent creates more work and more opportunities for inconsistency. A shared, governed context layer gives teams a foundation they can reuse across use cases, instead of treating business understanding as a new integration project every time.
Horizon Context connects three parts of that foundation: the evidence in your data systems, the knowledge of your business experts and the tools that use both. The goal is less time reconstructing context and more time applying it to business decisions.
Build on the knowledge in your existing data investments #
Your organization has already invested in the reports and data pipelines that run the business. Their metadata captures valuable evidence about how data is structured, transformed and used. Making that evidence available gives AI developers a stronger starting point than isolated table names and column lists.
Horizon Catalog's metadata connectors now support 18 sources across databases, BI and data pipeline tools. These include Power BI, Tableau, Looker, Sigma, Hex, dbt, SQL Server, Oracle, PostgreSQL, Databricks and BigQuery.
Depending on the connector and its configuration, Horizon Context collects schemas, query logs, dashboard definitions and usage signals. Teams can browse external assets alongside Snowflake objects, search their metadata and explore cross-platform lineage from the catalog.
Put data understanding front and center #
The redesigned Horizon Catalog delivers a consistent experience that puts data definitions, tags and contacts front and center. Business analysts can easily find data assets and understand what they mean, how they can be used and whom to ask for clarification. Together with metadata connectors, this makes Horizon Catalog a single pane of glass for understanding data across Snowflake and connected systems, helping teams and agents discover the right data and use it with shared business context.
When a retention report shows an unexpected decline, teams need to investigate whether it reflects a business change or a data issue. Across supported, connected systems, lineage helps them trace the report through transformations to its source data and identify where to investigate. Before changing an upstream table or column, they can follow the same dependencies downstream to assess which reports could be affected.
Snowflake CoCo brings this context into an AI-assisted workflow. Teams can ask questions such as "Where does this retention data set come from?" or "Which downstream reports depend on this column?" CoCo can use available lineage to help investigate issues and assess change impact, reducing the need to reconstruct dependencies manually.
These connectors collect metadata; they do not require replicating the underlying data sets into Snowflake. They make existing data knowledge easier to discover and reuse, while governed business definitions provide the additional meaning needed to interpret it.
Bring business expertise into your AI foundation #
The people who understand customer retention may not be the people who manage the database. Yet their knowledge of customer status, exclusions and reporting conventions is essential to interpreting the data. Capturing that knowledge should not depend on an engineer translating every clarification into documentation.
Metadata visibility separates catalog browsing from access to the underlying data. With the appropriate metadata permissions, users can explore catalog information and lineage without receiving permission to query the data itself.
The new APPLY COMMENT privilege also gives administrators a targeted way to let stewards edit object and column descriptions without granting broader privileges such as MODIFY or OWNERSHIP.
This gives business experts a direct role in improving the context that people and AI workflows can use, while administrators retain control over data access. A clarification becomes reusable knowledge alongside the data, rather than an answer that stays in a chat thread or one agent's prompt.
Reduce the cost of maintaining useful context #
An AI initiative needs context that teams can maintain, not just documentation prepared for its first demo. When the same customer identifier appears in multiple downstream views, repeatedly copying its description adds work without adding meaning. Missing or inconsistent descriptions leave both analysts and AI workflows with less context to work from.
Column description propagation uses column lineage to carry descriptions to supported downstream columns when their values pass through unchanged. For example, a description on a customer identifier can follow that column into a downstream view, even if the column is renamed.
Propagation stops when values are transformed, combined or aggregated, and it does not overwrite descriptions written directly on downstream columns. Teams can distinguish propagated descriptions from user-written ones. Once enabled, this reduces repetitive maintenance while keeping owners in control of meaning, so experts can focus on the definitions that need judgment.
For an AI-assisted retention analysis, context serves several purposes. Descriptions explain what customer fields represent. Lineage helps teams inspect where the data came from. A governed metric definition specifies how retention should be calculated. Each contributes something different to an answer people can evaluate. People can explore context in Horizon Context. Snowflake CoCo can use catalog context for tasks such as data discovery and impact analysis. Semantic views complement that foundation with governed definitions of metrics, dimensions and relationships for AI and analytics.
The business value is reuse: Teams can build on shared knowledge rather than recreating it in each dashboard, application or prompt. That foundation supports more consistent interpretation and gives people evidence to examine when reviewing an AI-generated answer.
Put your business knowledge to work #
Connect your supported data, BI and pipeline systems to Horizon Catalog. Bring business experts into description curation, and use Snowflake CoCo to explore lineage, investigate data issues and assess the impact of changes.
The knowledge your teams build today can become a reusable foundation for the next report, application and AI workflow.
Explore the supported connectors and setup documentation to get started.