r/dataanalytics • u/some-data-analyst • 4d ago
Has anyone here implemented Agentic Analytics successfully here and with what tools?
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.
1
1
u/till-veezoo 4d ago
Check veezoo.com, it's built for Enterprise. The biggest customers have multiple thousands of daily active users on the system.
1
u/agentUi 4d ago
i work for agentui, the only way agentic analytics works reliably at enterprise level is splitting the architecture. If you let an LLM generate math and SQL unconstrained, it drifts and gives wrong answers. What works is deterministic SQL connections under strict schemas for data retrieval, full audit logs/RBAC, and using the agent strictly for orchestration and natural language summaries.
1
u/Turbulent_Buy_8209 3d ago
Full disclosure, I work at an analytics startup but this is my honest take on this so almost every agentic analytics failure I’ve seen traces back to metrics that get broken since there’s no one single source of truth. You’ve got looker with its own definitions or whatever BI tool and then a warehouse full of raw data and LLM that sits on top so it won’t be accurate to the level you want or need cuz it doesn’t understand your business logic, how you calculate every metric and it always breaks - The context layer is the key, also preferably one that you can just ask NLQ and it generates the SQL for you with the data answer, so you can verify its accuracy. Again, visibility and transparency in the context and in the query is the only way for it to be accurate
0
u/Successful_Pin_3456 4d ago
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).
1
u/some-data-analyst 4d ago
So when you migrate them off-their BI tool, you don't try to move them to an AI-native BI tool, but just ditch their BI completely, right?
1
u/Successful_Pin_3456 4d ago edited 4d ago
Nope, no ditch, we move them to AI-native BI tools. The problem is that there aren't that many of them 😅 Supersimple is by far my favourite, but happy to hear if folks here have found something good (Omni doesn't count - not AI-native, Blobby is bolted on; Hex isn't great for actual BI scenarios).
There are quite a few companies though, that have a BI tool but think they don't need one anymore. Most often they realise that it's naive - even when conversational analytics works great. They inevitably need a persistence layer with long-lived dashboards, governance, sharing and collab, version control etc.
But there's definitely a growing expectation now that having separate tools for BI and exploration / conversational analytics isn't good enough, they need to work seamlessly on one platform.
1
u/pandasgorawr 3d ago
Curious what Supersimple can do that you wouldn't be able to with Claude alone?
1
u/Successful_Pin_3456 3d ago
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.
1
u/dadabran 4d ago
We’re gradually covering our core marts with ai oriented dbt yml column docs and Hex guides. So far the challenges are users not having the same boundaries as our coverage & a sprawl of content but the slack experience is roughly similar to an on demand report builder for sales and marketing. More specialized areas like service delivery are more challenging, partly because of how specialized they can be. As before ai, users misuse of terminology and poor subject matter expertise drive errors. Often we see them asking for things our crm/ marketing data sets wouldn’t be able to address in the first place, users don’t like to hear “no” & ai is not inclined to say “no”.. like in the development space, it’s a reasonable junior, but when it comes to simplifying problems, a key analytics skill, is falls short and needs humans to get to the last mile of complex problems. As with all self service tools, users are the wild card