Data and analytics agents
Data and analytics agents let people ask questions of governed data in plain words, then write the query, check it, and show the definitions behind the number they return. They are built on your semantic layer, so what comes back agrees with the figures your finance and data teams already publish, and we test that agreement on questions your data team has answered before anybody else is given access.
How the system is put together.
What goes in, what the system is allowed to touch, where a person decides, and the number it gets measured on. We draw every system before we build it, which is the cheapest place to have the argument about what it should do.
What actually changes once it is live.
These are the changes we measure. One of them becomes the number in the contract, and the monthly report is written against it for as long as we run the system.
- Questions answered the same afternoon they are asked
- One set of definitions behind every number
- Recurring reports drafted, with the variances already explained
- A metric moving the wrong way flagged before the monthly review
What data and analytics agents gets used for.
Questions in plain words
Asked of the warehouse or lakehouse, answered with the query shown and the definitions it used set out beside the number.
Reports and commentary
Board packs, operational reviews and client reports drafted, including the paragraph that explains why the numbers moved.
Monitoring and alerting
Metrics watched continuously, with an account of what changed and where, sent to whoever can act on it.
Data quality
Checks written, run and triaged, with proposed fixes going to the owner of the data.
Planning support
Assumptions gathered, models re-run and results explained, through a planning cycle that used to be a fortnight of spreadsheets.
From your process to a running system.
- 01Start from your metric definitions. Where they do not exist yet, agreeing them is the first piece of work, and it is worth doing anyway.
- 02Build the agent to write a query, validate it, and show its working.
- 03Test it on questions your data team has already answered, compare, and then put it where people already are: Microsoft Teams, Slack, Power BI, the browser.
Good fit
Worth doing where there is a warehouse or a lakehouse already, and a queue of business questions that never reaches the top of the backlog.
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Data and analytics agents: questions
Systems that often run alongside this one.
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