Ask the question in plain language. Get the data instantly.
A report that arrives three days after you ask for it answers a question you no longer have.
The typical cycle goes like this: you need a piece of data, you contact IT or the management control team, you wait, a file arrives, the file raises a new question, and the cycle starts again. In the meantime, the decision has already been made based on intuition.
Conversational analytics breaks this cycle. Ask the question in natural language — “how did sales in the North perform last quarter compared with last year?” — and get the answer with the chart, immediately. Then ask why, and drill down further.
What makes it reliable, and different from a generic assistant
- It only reads your data: it does not rely on memory or make things up. If the data is not there, or is not available at that level of detail, it tells you.
- Every answer is verifiable: it shows where the number comes from, which filters were applied, and which period was used. If something does not add up, you can check it instead of arguing about it.
- It respects permissions: every query is executed with the permissions of the person asking it. No one can use the tool to see data they would not be allowed to access elsewhere.
- Metrics have a single definition: “margin” means the same thing to everyone because it is defined once and used consistently.
The last point is usually what makes the difference in meetings.
Why we do not query the database directly
A natural-language question is not translated into a query against the raw database. Instead, it is translated into an intermediate model where tables, fields, and metrics have business-friendly names and definitions written once. This is the central point of the architecture, and the reason the system is reliable.
If the system wrote queries directly against the database, the same question asked twice could produce two different numbers, both technically defensible. With the intermediate model, the definition of “net revenue” is a single one, certified by the data owner: the system decides which metrics to use and how to filter them, not how they are calculated.
When the question is ambiguous, it asks
If a question can be interpreted in more than one way, the system asks for clarification instead of choosing. The cost of a clarification question is a second; the cost of a plausible but incorrect number is a wrong decision.
Questions that repeatedly have the same ambiguity become a useful signal: they indicate that a shared definition is missing, and that definition can be added to the data model once and for all.
Performance and costs under control
Frequently asked questions are served from precomputed answers, the most commonly requested aggregates are pre-built, and each user has a spending limit. The distribution of questions across the company is highly concentrated: a small number of questions account for most of the traffic, and optimizing those solves much of the cost problem.