Ask most analytics tools what drives your revenue and they'll answer straight away. They rarely mention that 4% of the rows are empty, a latency column is secretly half in seconds, or a batch of records was loaded twice.
Our Data Profiler, free to try in the lab, is built the other way round. Three agents run in sequence, and each one checks the last.
- The Profiler works out what every column is (money, a duration, an email) and how columns relate.
- The Data-quality agent turns that profile into rules, each with the reason for its threshold and sample rows that break it.
- The Insight miner only then looks for drivers, segments, and shifts, and scores each insight lower when the data behind it fails a rule.
“An insight is only as trustworthy as the rows underneath it.”
On our sample SaaS dataset it finds the planted revenue jump on the exact day it happened, and it only reports it after adjusting for customer mix. It also points out the duplicated rows first.
That order, check first and conclude second, is how we build every AI feature for clients.