What is conversational analytics?
The hard part isn't the chat window. The system has to understand your metrics, not just your words. It's sometimes called natural language analytics or chatting with your data.
The term is also used for analyzing customer conversations, like call center transcripts or chat logs. In data and BI, it means the conversation is with the data itself.
How does conversational analytics work?
- Ask the question. Type it the way you'd ask a colleague, such as "Which markets drove the lift in Q2?"
- Resolve the meaning. The system maps words like "revenue" and "Q2" to governed metrics, dimensions, and calendars.
- Run the analysis. It writes and runs the query, often SQL, against the data.
- Return and explain. You get an answer, table, or chart, ideally with the data and logic behind it.
- Follow up. Ask the next question in the same conversation, or hand off to deeper analysis.
What is an example of conversational analytics?
A budget question answered in the meeting
In a quarterly review, a CMO asks how spend paced against revenue this month and which channel is furthest off plan.
Someone types the question, gets a chart and a short explanation drawn from governed data, and asks a follow-up about the outlier channel. The team decides before the meeting ends.
Conversational analytics vs. dashboards vs. generic AI chatbots
| Dashboards | Generic AI chatbot | Conversational analytics | |
|---|---|---|---|
| Questions it handles | The ones planned in advance | General knowledge, or files you upload | New questions about your own data |
| Data source | Governed data | Training data or uploaded exports | Governed business data |
| Metric definitions | Built into each view | Inferred from the question | Shared, agreed definitions |
| Traceability | High | Low | High, when the logic is shown |
Why does conversational analytics matter?
Most business questions come up in the moment, in a planning meeting or a performance review. When answers depend on an analyst queue, decisions get made on instinct instead.
- Faster decisions. Answers arrive while the decision is still open.
- Broader access. People who don't write SQL can still get to the data.
- Analysts on harder work. Fewer repeat requests leave more time for modeling and interpretation.
- Trust depends on the foundation. Without governed data and shared definitions, fast answers can still be wrong.
How does Alli do this?
Ask Alli is the AI agent inside Alli, PMG's platform for business transformation. You type a question in plain English, and every request resolves against governed metrics, dimensions, and business rules in Alli Semantic Layer, so it uses the definitions your teams already agreed on. You get an answer, table, chart, or summary, and you can see the data, logic, and source behind it.
From there, you can hand off into Alli Data Explorer for deeper analysis, build a dashboard from a sentence in Generative Dashboards, or turn the request into a recurring workflow.
Explore Ask AlliConversational analytics FAQ
Is conversational analytics the same as a chatbot?
Not quite. A general chatbot answers from training data or uploaded files. Conversational analytics answers from your own governed business data, using agreed metric definitions.
Do you need to know SQL for conversational analytics?
No. The system writes the query for you. Technical teams still govern the sources and definitions underneath.
How accurate is conversational analytics?
It's as accurate as the data and definitions it works from. Tools that resolve questions against a semantic layer and show their logic are much easier to trust and check.
Does conversational analytics replace dashboards?
No. Dashboards are good for questions you ask every week. Conversational analytics handles the new questions that come up in the moment.
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