NLQ
NLQ — Natural Language Query — means you can ask your data questions in plain English instead of writing code or building reports. Instead of calling IT and waiting two weeks for a custom report, a regional director types "what was our no-show rate by program last quarter?" and gets the answer in seconds. The system translates the question into a database query, runs it, and returns the result — as a table, a chart, or a spoken answer. It is Google for your company data. No SQL, no training, no dashboard navigation. Just ask.
Go deeper
It is 7:45 AM and you are walking into a board meeting in fifteen minutes. A board member emailed last night asking about denial rates at your newest location. Your analyst is on PTO. The dashboard shows denial rates by program but not filtered to the one location for the specific time period the board member asked about. With NLQ, you type the question into your phone, get the answer, and walk into the meeting prepared. Without it, you say 'I will have to get back to you on that' — again.
The trap most companies fall into is assuming NLQ means anyone can ask anything and get a perfect answer. It does not work that way. NLQ is only as smart as the data definitions underneath it. If nobody defined what 'denial rate' means in your system — is it denied claims divided by submitted claims, or denied dollars divided by billed dollars? — the AI will guess, and it might guess wrong with total confidence. NLQ is the last thing you build, not the first.
Questions to ask
- Before we invest in NLQ, do we have a documented data dictionary that defines our key metrics?
- What happens when someone asks a question the system cannot answer — does it say 'I don't know' or does it hallucinate?
- Can we see an audit log of questions asked and answers given, so we can catch errors?