Automating data quality checks with AI (catch the broken pipeline first)

The worst way to find out your pipeline broke is a VP pointing at a dashboard and asking why revenue is zero. You did not catch it. They did. Now you are explaining a tracking bug in a meeting instead of having quietly fixed it on Tuesday.

I have been on the wrong end of that conversation. After the second time, I built a data health check that runs before anyone sees the numbers, and it has caught more silent breakages than I would like to admit were happening. Here is how it works and how to build your own. It is free.

What a data health check actually does

It is a scheduled gate that sits in front of your reporting and asks one question: can I trust today's numbers. It runs a few checks against your key metrics, and based on the result it either stays silent (everything is fine) or DMs you (something is wrong). That is it. No dashboard, no new tool to log into. It only speaks up when it has bad news.

What to check for (and what not to)

The trap people fall into is checking for too much and drowning in false alarms. A health check that cries wolf gets muted in a week. So flag only genuine pipeline failures, the things that are never legitimately true:

• NULL where a metric should always have a value (revenue, active users).
• Zero where zero is impossible (WAU = 0 means the pipeline died, not that nobody showed up).
• Negative values in fields that cannot be negative (counts, GMV).
• A metric that did not refresh: today's date missing from a daily table.

Crucially, do NOT flag normal week-over-week swings. A 15% drop might be real, might be seasonal, might be a campaign ending. That is analysis, not a pipeline failure, and a health check that pages you for every wiggle is worse than no health check. The gate is for "the data is broken," not "the number moved."

Where AI fits (and where it does not)

You do not strictly need AI to check if a number is NULL. A simple rule does that. Where AI earns its place is the summary and the judgement layer: handed the check results, it writes a plain-English "here is what looks wrong and why it matters" instead of a wall of raw flags. It turns "metric_3 IS NULL, metric_7 < 0" into "revenue did not load today and refunds went negative, the pipeline likely failed overnight."

That readable summary is what makes you actually act on the alert at 9 AM instead of skimming past it. AI is the translator, not the detector. Keep the detection rules dumb and deterministic. Let AI handle the narration.

The GREEN / RED gate

The cleanest design is a single gate with two outcomes. The workflow runs the checks, an AI node reads the results and returns one of two statuses with a short summary:

• GREEN: all checks passed. The workflow posts the day's report as normal. You never even notice it ran.
• RED: something failed. The workflow holds the report and DMs you the issues, in English, so you can fix the pipeline before the numbers go anywhere.

This is the difference between catching a problem privately on Tuesday morning and having it caught for you, publicly, in Thursday's leadership review. Same bug. Very different week.

What you need to build it (free)

• n8n (free cloud tier) for the schedule and the flow.
• Your database, for the metric checks.
• A free Gemini key for the summary node.
• Slack, for the DM when something is RED.

The reputation math

An analyst is trusted until the day they present a wrong number with confidence. After that, every number gets second-guessed, and second-guessed numbers are slow numbers. A health check is cheap insurance against the single event that does the most damage to an analyst's credibility: shipping a broken number you did not know was broken.

You build it once. It runs forever. It speaks up maybe twice a quarter. Each time, it saves you a meeting you really did not want to be in.

Build the health check live

The 3-Hour Champion Sprint includes building this exact data health check gate, plus an automated report and a Slack bot, live against a real Postgres warehouse. You leave with the n8n workflow yours to point at your own pipeline. ₹1,499 Early-Bird, one Saturday.