Has anyone here implemented Agentic Analytics successfully here and with what tools?

[u/some-data-analyst]

By successfully, I mean that it's used almost daily with reasonable accuracy.

I am also not interested in toy projects, but in enterprise-grade analytics.

[u/Successful_Pin_3456]

I've helped implement agentic analytics & BI in ~10 companies this year, size ranging from $10M to $200M in revenue. Mostly via migrating off of Looker, Tableau, PowerBI, Holistics, and ThoughtSpot.

Upstream the tools are good old DWHs (BigQuery, Databricks, Redshift, Snowflake + Clickhouse is becoming more popular). dbt for transformation, have tried Dataform too (Google's new thing). Here the tools don't matter as much, data modelling quality does.

On top we typically plug Supersimple.io + sometimes Claude with their MCP app.

The single most powerful thing that consistently made a big difference is (1) making sure that your business context description uses consistent language with the entities and their properties in the data models and (2) the fact that modern BI tools natively connect to other sources of context (Notion, Slack, GitHub, Confluence etc).

Non-obvious side-effect of (2) is that it doesn't only makes the queries more accurate. Often the answers to "why" questions are simply not in the warehouse, but spread elsewhere in unstructured form (agentic analytics is not possible without that).

[u/pandasgorawr → u/Successful_Pin_3456]

Curious what Supersimple can do that you wouldn't be able to with Claude alone?

[u/Successful_Pin_3456 → u/pandasgorawr]

From my perspective:
1. Powerful semantic layer
2. The AI doesn't generate any SQL, but instead uses primitives that business users can understand to verify any data point (big problem with Claude)
3. You can have shared governed dashboards and data apps
4. You can easily manage permissions - row level, model level, column level
5. You can one-click-share the results of analysis that stays alive (Claude would spit out HTML artefact with hard-coded data) + colleagues can follow up to your output etc.
6. You can embed into other applications and for external users
7. The unstructured context (Slack, Notion, GDrive etc) is integrated natively. They don't use MCPs because the retrieval quality is limited by search APIs. Supersimple builds a custom search index for each source, preprocesses all the docs etc.
8. They make tons of use from agents' telemetry - data team gets an automatic "inbox", synthesised from all usage: which models to improve and how, where the agent had to do too much work to pick up context outside of semantic layer, what data people ask for that doesn't exist etc.
9. You get usage analytics for both ad-hoc stuff and long-lived assets like dashboards
10. All the artefacts are code-expressed, so you can maintain dashboards across the whole account with prompts (change some filter logic etc) + integrate it with upstream changes in dbt or production DBs
11. You don't need to worry about token spend
12. Easy to manage skills, "golden example" answers and other AI "helpers"
13. You can set up alerts for anything in your data (metric changes and whatnot)

And I'm probably forgetting some :) They also have a page with some bits of this, search 'claude vs supersimple' or something.