A documented environment is great. A documented environment full of bad data is just well-organized trouble. So let’s close the loop.
Data quality (DQ) work usually dies in the gap between “we should really check this” and “we don’t have a DQ platform.” Checks get written once, they live in somebody’s saved SQL file, and never run again. Then a bad data load slips through and we find out from a director’s dashboard instead of from ourselves. I don't like that! That is scary.
How the agent helps
The Data Quality Assistant turns your playbook into a helper that writes and explains checks in plain SQL. It suggests sensible thresholds and, just as important, shows people how to log results so you can trend quality over time.
Some of what it can field:
- What checks should I run to validate the new student enrollment table?
- How do I write a SQL check for duplicate records on a given key?
- What thresholds do we use to flag missing values as a quality issue?
- How should I log check results so we can trend data quality over time?
The value (and the same honest caveat)
Like the other two, it’s only as good as the playbook behind it. The agent’s job is to make solid checks easy to write, keep them consistent across the team, and nudge people toward logging results, a step that some people skil. Write the playbook once; the agent keeps everyone actually using it.
What you need:
You know the drill by now: a SharePoint site for the playbook (check definitions, SQL templates, threshold guidance, and the results-log format), scoped permissions, and a Copilot license to build.
Wrapping Up. Three Agents, One Pattern.
Three agents, one idea: point Copilot at content you already have (or really should write), give it a clear job, and let it make a fiddly task fast and consistent. The Analytics Solution Consultant helps you choose, the Data Dictionary Builder helps you understand, and the Data Quality Assistant helps you trust. None of them need a new platform. Just a SharePoint site, a Copilot license, and the discipline to write down what your team already knows.
If you take one thing from this series, take this: the agent is the easy part. The cookbook, the playbook, the documented tool choices, that’s the real work, and it’s worth doing whether or not you ever build the agent. Copilot just makes that work pay off faster. You still need to validate despite documenting and "commanding" the agent on what to do and where to look (or not look).
If you’ve tried something like this on your campus, I’d love to hear about it on the Connected Campus CoP: what worked, what flopped, and which of the three you’d build first. Start a thread or comment below.