Bad data caught before a dashboard,
not after a decision was made on it
A dashboard is only as trustworthy as the data feeding it, and bad data rarely announces itself. It just sits in a table looking plausible until a decision gets made on a number that was never right. A data quality agent checks every incoming batch for missing fields, duplicates, out-of-range values and schema drift. It flags what it finds instead of silently fixing or dropping it. The same discipline sits behind a monitor of ours that caught 184 of 218 real anomalies with zero false alarms.
Bad data looks plausible until a decision is made on it
Bad data usually gets discovered the expensive way. A report looks off, someone traces it back through several tables, and finds a batch from three weeks ago with a format that quietly changed and nobody caught. By the time it surfaces, decisions have already been made on the wrong numbers, and un-making those decisions costs more than catching the issue would have.
Duplicates are the second cost: the same order counted twice because a retry was not idempotent. That inflates a number just enough to look plausible rather than obviously wrong, which is exactly what makes it dangerous.
Manual spot-checks do not scale either. A person glancing at a dashboard once a week will catch an obvious outlier. A quietly wrong field buried in a table of thousands of rows is not something a glance ever finds.
What the agent checks
The agent checks every batch of incoming data against a set of rules built from your actual schema. Missing required fields. Duplicate keys. Values outside a plausible range. Schema drift, where a source’s format changed without warning. It flags what it finds with the specific rule that failed and the exact record. Whoever owns that data starts from a precise lead, not a vague sense something is wrong.
It tracks its own false-positive rate over time and reports it, because a data quality tool nobody trusts gets ignored. One of our own monitors runs on that same discipline: it caught 184 of 218 real events with zero false alarms, instead of flooding the team with noise.
Typical scope: warehouse tables, API feeds, and scheduled loads. It is a watcher, not a fixer, by design. Every flag goes to a human or a separately approved process, never a silent correction.
What your team still decides
Deciding the validation rules initially is a joint step. What counts as out-of-range for your specific business gets built from your schema and your team’s experience with past data problems. Correcting a flagged record is a human decision, since the right fix usually depends on context the rule alone cannot know.
How trust in the flags holds up
The agent never fixes or drops a record silently. Every flag is logged and routed to a person. False-positive rate is tracked and reported, not hidden, so the rule set stays trustworthy. Rules are reviewed before going live against real historical data, not deployed on guesses about what “wrong” looks like.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $2,200 + support plan | Agent built, tuned and supervised by us, monthly rule review | 2 to 3 weeks |
| Full control, handover-ready | from $3,200 | Same agent on your own warehouse, documented rules, your team maintains it | 3 to 4 weeks |
Running cost is usually $15 to $60 a month in model and warehouse query usage.
Related
See the analytics service page and AI agents service page for the surrounding build. Data engineering and ETL agent feeds this one the data it checks. SQL analyst agent and security monitoring agent apply the same flag-don’t-fix pattern elsewhere. For a related one-time setup, see automate data quality monitoring. Real monitoring discipline behind this page: the two-brand analytics hub case study and the ProBay AI agent team case study. A margin guard there held every order below cost in a pre-launch verification run.
Found bad data the hard way, after a decision was already made on it? Get in touch and we will look at where it is most likely hiding.
FAQ
How much does a data quality agent cost?
From $2,200 to build validation rules for one warehouse or feed, live in 2 to 3 weeks. Multiple feeds or more complex business rules usually run $3,500 to $5,500.
How long before it is catching real issues?
2 to 3 weeks. Building rules from your schema and known past data problems takes most of it. Then a tuning period runs against real incoming data before alerts go live.
Which systems does it watch?
Your warehouse, API feeds, and scheduled data loads, wherever data enters a system your team relies on for decisions. It works alongside a data engineering and ETL agent if you have one, or on its own against an existing warehouse.
What happens when it flags something, good or bad?
It alerts and logs. It never silently fixes or deletes a record on its own. A false positive gets the rule adjusted. A real catch gets handled by whoever owns that data, with the specific rule and record already identified.
Does checking data quality mean it can alter our data?
No write access by default. It reads and flags. Any correction to a flagged record is a decision your team makes, and if automated at all, is a separate, explicitly approved step.