How Snowflake CoWork answers your toughest cross-domain questions

Enterprise businesses typically have lots of data, but it seldom paints a whole picture that everyone can see at the same time. Data is scattered across different departments in silos, leaving teams unable to connect information like customer sentiment to operational efficiency or revenue fluctuations. Snowflake CoWork changes this by unifying all that data into a cohesive ecosystem and enabling organisations to ask and answer complex questions across domains.

Let’s break down Snowflake CoWork’s Integration Architecture

Snowflake serves as the central nervous system for organisational data, whether it’s structured (SQL tables), semi-structured (JSON), or unstructured (documents).

How does it work?

To connect the dots and answer those tough cross-domain questions, Snowflake uses some core technologies:

Snowflake Semantic Views: The "Dictionary"

Before it can answer your questions, Snowflake CoWork needs to know what your data means. Semantic Views act as a business layer where you define metrics once, map relationships, and add Synonyms, i.e. tell the system that “Complaints”, “Tickets” and “Reviews” all belong to the same domain of “Sentiment”  .

Cortex Agents: The "Orchestrator"

A cross-domain query often requires both structured and unstructured data. Cortex Agents are the “brains” that decide which tool to use and where to look.

If you ask, “Why did sales drop in NSW?”, Cortex is able to: 

1. Query the “Sales Semantic View” for revenue trends.
2. Query “Unstructured Review Data ” for customer feedback from product reviews and social media that you have sourced and ingested.
3. Query support logs to check if there was a spike in product-related issues
4. Synthesise the three into a single, data-backed answer: “Sales dropped in NSW due to a product issue (from querying support tickets) which led to negative social media sentiment (from querying ingested unstructured data from product reviews and social media)”. 

Custom Skills: The "Analyst"

Beyond simply querying shared data, Snowflake CoWork supports Custom Skills that teach the AI assistant specific, domain-expert analytical workflows. Rather than just retrieving data, these skills allow CoWork to break down silos at the analytical level by running cross-functional logic on the fly:

• Cross-Domain Correlation: This skill automatically correlates metrics across completely different business units—such as Voice of Customer (NPS/CSAT), financial netflows, and complaints. For example, if an executive asks, "Is there a statistical link between product-related support tickets and our recent sales drop in NSW?", the agent queries each domain independently, aligns the data along identical time dimensions, and computes statistical correlations (including lead/lag analysis). It can instantly surface whether a spike in software bugs or delivery complaints acts as a leading indicator of an impending drop in regional revenue. No single team owns all three datasets, but the skill stitches them together on the fly to prove the exact root cause.
• Root Cause Summariser: This skill synthesises complaint reason codes and detractor verbatim themes to identify exactly why business metrics are shifting. Instead of requiring a data analyst to manually pull data from multiple systems, rank causes, and spot trends, the agent handles it in seconds, producing ranked root cause tables, distribution charts, and actionable recommendations.

Snowflake CoWork: The "Interface"

This is the unified conversational platform (available at ai.snowflake.com) where the end user interacts with the data. It maintains context across a conversation, respects your security permissions (such as role-based access control [RBAC]), and ensures that the insights surfaced are governed and accurate.

Security and governance: enterprise-grade RBAC

A common concern with cross-domain AI assistants is data leakage, aka ensuring a user doesn't accidentally surface insights from a dataset they shouldn't access. Snowflake CoWork natively addresses this by operating entirely within Snowflake’s governance framework.

• Inherited permissions: CoWork inherits your security. If a user does not have RBAC permissions to view financial netflows or sensitive HR data within Snowflake, the Cortex Agent cannot access that data to answer their question. The AI strictly respects row-level security (RLS) and object-level tagging.
• Context-aware guardrails: When Custom Skills cross domains on the fly, Snowflake’s data governance policies ensure that data is aligned and analysed without exposing the underlying raw tables to unauthorised users. You get cross-functional insights without compromising data privacy.
• Full auditability: Every query orchestrated by Cortex Agents, every semantic view accessed, and every custom skill executed is fully logged. Enterprises maintain absolute visibility into what questions were asked, which models were used, and exactly what data was retrieved, fulfilling strict compliance and auditing requirements.

How this move from reactive to proactive reporting impacts your business agility

Business agility relies on being able to gather accurate insights quickly, so that teams can nimbly pivot as needed. Instead of relying on static data reports and dashboards that inevitably invite “let us get back to you on that”, your teams can ask questions upfront and get the intelligence they need to take immediate action. By using “Snowflake Semantic Views” to define your business logic and “Cortex Agents” to navigate the data, you can remove the problematic data siloes. Everyone can now see the same picture drawn from data, instead of just bits and pieces of it.

How to get started

Altis can help you speed up your Snowflake CoWork adoption with our Accelerator Program. As a Recommended Launch Partner, we work with you to help you unlock the value of Snowflake CoWork and turn it into practical, high-impact use cases. If your key reports already run on Snowflake, you are more ready than you think.

Contact us to discuss your first Snowflake CoWork use case.