Athena for Analytics is an AI data analyst from Athena Intelligence. Athena drafts a semantic model from your warehouse, your data team reviews and deploys it, and anyone can then ask questions in plain English, in Athena or in Teams. Every answer links to the query behind it, and the numbers come from your own governed definitions.
Much of analytics happens one question at a time. Someone heading into a regional review wants to know why South missed plan, and the question goes to the data team, which answers it by hand when the queue allows. Athena for Analytics takes that work on, and Figure 1 shows the whole system on one page.
Athena Intelligence builds the agent platform that teams use to create agents, workflows and apps on their own data. Athena for Analytics points that platform at a data team's recurring work: drafting the semantic model, answering questions from it, and filling the Monday review in the team's own template. The video in section 03 walks through one example, using demo data.
- Athena drafts a semantic model from your warehouse catalog, and your data team reviews, validates and deploys it before anyone queries it.
- Anyone can ask in plain English in Athena or in Teams, and each answer links to the query behind it.
- A scheduled agent operating procedure (AOP) can fill your own deck template every Monday morning with cited figures.
- Queries run as the person asking, under the warehouse role selected for the connection.
Contents
01What problem does Athena solve for data teams?
It removes the queue between a business question and a governed answer. In the example in this post, Marcus Lee is a regional leader with a 10:00 AM review. At 8:12 he emails to ask why South missed plan, and by 8:20 the data requests system tells him to expect a reply this week. Figure 2 sets that morning beside the same morning with Athena.
That wait is a pattern. Gartner's survey of 813 organizations found that more than 87 percent have low BI and analytics maturity, and it lists bottlenecks from the central IT team handling content authoring and data model preparation among the traits of those organizations. Gartner's 2024 survey of 479 chief data and analytics officers found that a lack of skills and staff was a major roadblock for their teams. When the same few people write every query, every question joins the same line.
Definitions are the second half of the problem. Variance to plan is net sales minus planned sales, and the number agrees across a dashboard, a deck and a chat answer only when all three read one written definition. Athena puts that definition in a semantic model, a single set of metric definitions that every report and every agent answer reads from. The semantic models page shows how the draft is built from a warehouse catalog.
02What can Athena do for analytics teams?
Athena can take on the recurring work around a team's questions, from drafting models to answering in chat to building the weekly review. Figure 3 groups twelve workflows by who benefits.
Business teams
People who run a region, a store or a product line can ask a question in plain English in Athena and open the query behind the answer, or ask the same question from Teams and continue the thread in Athena. The Monday regional review can arrive filled into the team's own deck template with every figure cited, and a dashboard can reach people who have no warehouse account, with row-level security applied for each viewer.
Data teams
Athena can draft a semantic model from the warehouse catalog and a paragraph of business context, and the team reviews the draft like a document, then validates, deploys and versions it. Athena can read the semantic model of an existing report and rebuild the slowest dashboard on the Athena Lakehouse, and Athena can surface stale pipelines with a root cause and the affected dashboards, posted to Slack.
Analytics leaders
Leaders can consolidate separate reporting capacities onto one governed layer, one dashboard at a time, while keeping Snowflake, Databricks or BigQuery and querying them under each person's own permissions. Data scientists can use notebooks with isolated compute on large datasets, and tables moved to open Iceberg storage can leave dashboards unchanged when the warehouse changes later.
The Data, Analytics and BI solutions page has the longer list, and the semantic model use case shows one draft from three sentences of context.
03How does it work? One Monday, start to finish
Athena for Analytics works in five steps, and a person on the data team approves the model before anyone asks a question. The video below follows a data analyst, a data lead and a regional manager through one Monday, and Figure 4 breaks it into the five steps.
- Connect your sources. Priya opens Athena, adds three weekly files and connects Snowflake. She signs in with her own identity, and queries run as her under the role selected for the connection.
- Build the semantic model. Athena drafts a model called Retail sales, and Sam Ortiz on the data team refines it. The model defines net sales, variance to plan and region, joined to the regional targets. Sam validates it and clicks Deploy.
- Ask anything. Priya asks "Why did South miss plan last week?" Athena answers that South finished $27,000 below plan, $178,000 against $205,000, and that North, East and West all beat plan. Each figure carries a citation. Clicking one opens the query behind it as a chart by region, as SQL and in the model's own query format, with a button to run it with your own connection.
- Schedule the report. An AOP named Regional review runs every Monday at 7:00 AM in the America/New_York time zone. Athena queries net sales against plan by region, fills the deck template and cites every figure. The slide shows net sales of $819k, a variance to plan of minus $16k and South below plan, with a Cited badge on each number.
- Answer in Teams. Marcus types "@Athena how did East do against plan last week?" in a Teams channel. Athena replies in the thread that East finished $4,000 above plan, $214,000 against $210,000, one of three regions above plan, with South the only region below. A Continue in Athena link opens the same conversation in the app.
The video closes on three lines: deploy in your VPC, model agnostic, and enterprise agent infrastructure. For the Teams surface in more detail, see Athena Anywhere.
04How is this different from other AI analytics tools?
Athena answers from your semantic model first and writes SQL second, so a chat answer, a dashboard and a deck draw on the same definitions. Gartner predicts that by 2027 organizations that prioritize semantics in AI-ready data will increase GenAI model accuracy by up to 80 percent, which fits that design. Table 1 compares the manual way with Athena, task by task.
| Task | The manual way | With Athena |
|---|---|---|
| Ask a question | An email or a ticket, with a reply promised later in the week | Plain English in Athena or Teams, answered in the thread |
| Check the number | Ask the analyst which query was run | Open the cited query as a chart or as SQL |
| Define variance to plan | Lives in spreadsheets and in people's heads | Written once in the semantic model, reviewed and versioned by the data team |
| Build the Monday review | An analyst fills the deck by hand | A scheduled run fills your template with cited figures |
| Ask a follow-up | A new ticket | Reply in the same thread or continue in Athena |
| Fix a wrong definition | Hunt through each report | Edit the model once and deploy a new version |
Athena works on the warehouse you already use. Nothing needs to move on day one, and each person queries under their own permissions.
Every answer opens to its query, and the Athena Governance System records each change by a person or an agent so the work can be rolled back. The same platform lets the data team build the next workflow themselves, such as a pipeline health alert or another scheduled report. Figure 5 shows the citation and the activity trail as they appear in the product.
05Is Athena secure enough for regulated companies?
Athena is built for regulated work: SOC 2 Type II and HIPAA, zero data retention agreements with model providers, and deployment inside your own environment. Figure 6 shows where each part runs.
Athena can run in Athena's managed cloud, in your VPC, on-prem or air-gapped, and you own the data plane. The deployment options go into detail. Athena has zero data retention agreements with Anthropic, OpenAI and Gemini, and Athena does not train on customer data. Athena has passed over a dozen enterprise security and AI vendor reviews, and a Big Four audit firm has run Athena in production for over a year. In the analytics example, every query runs under the permissions of the person asking, so the access rules in the warehouse keep applying.
06How do we get started?
Start with the report or question the business asks for most often. Connect the warehouse, let Athena draft the semantic model from the catalog and a paragraph about the business, and have the data team review it. Then ask the question in plain English and open the query behind the answer. A question that comes back right is a good first agent operating procedure to schedule.
Curious how it would work on your data? We're happy to show you.
07Questions teams ask
What is a semantic model?
A semantic model is a single written set of your business metrics, dimensions and joins, such as net sales, region and how they connect to plan. Dashboards, decks and agent answers all read from it, so they agree. In Athena, the model is drafted from your warehouse catalog and a paragraph of context, then your data team reviews, validates and deploys it.
What is variance to plan?
Variance to plan is the difference between actual results and the plan for the same period, for example net sales minus planned sales for a region in a given week. In the demo, South finished $27,000 below plan, with $178,000 against a plan of $205,000. The numbers are demo data.
Do we need to replace our warehouse or BI tool?
No. Athena starts on top of what you have. Athena queries Snowflake, Databricks and BigQuery under each person's own permissions, and can read the semantic model of an existing report. Nothing is copied unless you choose to migrate it, and migrated tables use the open Iceberg format, so they stay on your storage.
Can people ask questions from Teams?
Yes. In the demo, a regional leader types @Athena with a question in a Teams channel, and Athena replies in the thread with a cited answer and a Continue in Athena link. People can keep chatting in the thread or say reset to start over.
How do I know an answer is right?
Each answer carries citations that open the query behind it, as a chart, as SQL and in the model's own query format. Anyone with access can run it again with their own connection and compare. Because the numbers come from the semantic model your data team approved, the answer matches the definitions used in your reports.
Who controls the definitions?
Your data team does. Athena drafts the model, and a person on the data team reviews, validates and deploys it. Definitions are versioned, each change is attributed to a person or an agent, and earlier versions can be restored. Owners approve every change before it goes live.
Can Athena automate a recurring report?
Yes. An agent operating procedure (AOP) can run on a recurring schedule, such as every Monday at 7:00 AM in your time zone. In the demo, the AOP fills a deck template with last week's net sales and variance to plan by region and cites every figure. The template stays yours, so the layout matches what your team already uses.
Run this on a schedule
Run your weekly analysis on a schedule instead of rebuilding it by hand.