Data quality on autopilot:
catch the broken pipeline before the board deck does
A bad number in a report usually traces back to a pipeline that broke days or weeks earlier, silently, with nobody watching for signs of trouble. We build a monitoring layer that checks continuously for missing records, broken pipelines, duplicate rows and schema drift. A data problem gets caught before it reaches a dashboard anyone trusts.
A pipeline rarely announces that it broke
A data pipeline usually just keeps running. It produces fewer rows than it should, or stale data, or a table with a schema that quietly changed when a source system updated. Nothing throws a visible error, because nothing technically crashed. The first sign of trouble is usually a number in a report that looks wrong to someone who happens to know what the right number should look like.
By the time that happens, the bad data has often already fed a dashboard a leadership team trusts, or a forecast a planning decision relied on. Maybe a report sent to a client. The fix, once found, is usually straightforward: a backfill, a pipeline restart. The real damage sits in the delay between the break and the discovery, decisions made on numbers nobody yet knew were wrong.
The structural reason this keeps happening is that most teams monitor the output, the dashboards and reports, rather than the pipeline and tables feeding them. A problem gets detected downstream, after it has already propagated, instead of at the source, where it actually occurred and where it would be fastest to fix.
What the model checks, table by table
The model runs continuous checks on your tables and pipelines: row count against expected volume, duplicate detection, null value spikes, and freshness, whether a table updated on schedule. That catches a stalled pipeline even when the data it last produced still looks fine. Schema drift detection flags when a source system changes its data format unexpectedly, a column renamed, a type changed, a field that stopped being populated. This is often the root cause behind a report that suddenly looks wrong for no obvious reason.
Each check runs per table or feed individually, rather than producing one blanket health score. An alert names the specific table and check that failed, routed to whoever owns that pipeline. A dashboard gives a data lead a single view of health across the whole warehouse. That helps with day-to-day monitoring and with spotting which pipelines fail most often.
Before go-live, the monitoring gets checked against a past data incident you already know about. That confirms it would have caught that specific problem, and roughly how much earlier than it was actually discovered.
Where your team still fixes the pipeline
Diagnosing the root cause of a flagged issue and fixing the underlying pipeline stays with your data or engineering team. The model detects and alerts on data health problems. It does not modify pipelines, backfill data, or change a schema on its own.
How the monitoring earns trust over time
Every check and its result is logged, so a data team can review history and see how often each table has had issues. That is useful for prioritising which pipelines need a more durable fix, instead of repeated firefighting. A kill switch pauses alerting for any specific table in one message, if it is undergoing planned maintenance that would otherwise trigger false alarms.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $800 | Main tables and pipelines, continuous checks, routed alerts | 7 to 12 days |
| Department package | from $2,500 | Data quality monitoring across a full warehouse with health dashboard | 2 to 4 weeks |
Running cost is usually $20 to $70 a month depending on the number of tables and pipelines monitored.
Related
Pair this with KPI anomaly detection so a business metric anomaly is cross-checked against pipeline health. That tells you whether the number is a real business change or a broken feed. Master data golden record management keeps clean, consistent data flowing into the pipelines this model watches. For the cleanup side of existing bad data, see data cleaning and deduplication. The full package breakdown is on the AI agents service page and the automation-everything overview. For real data infrastructure work, see the two-brand analytics hub case study and the factory ERP recovery case study.
Ready to catch a broken pipeline before it reaches a report? Get in touch and we will look at your data stack in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does data quality monitoring cost?
From $800 for checks across your main tables and pipelines, live in 7 to 12 days. A department package covering a full warehouse with routed alerts usually starts at $2,500.
What counts as a data quality issue it would catch?
Missing or duplicated records, a sudden spike in null values, a source system changing its data format without warning. Also a pipeline that has simply stopped updating, even though no error was ever thrown.
How is this different from KPI anomaly detection?
KPI anomaly detection watches business metrics like conversion rate or revenue. This watches the pipelines and tables underneath those metrics instead. A broken feed gets caught at the source, rather than showing up later as a confusing metric anomaly.
Who gets the alert?
Whoever owns that specific table or pipeline, with the exact check that failed and the table name attached. The person who can actually fix it finds out directly, instead of a generic alert going to everyone.
What systems does it monitor?
Your data warehouse, ETL pipelines, and any scheduled data sync between systems, wherever we can read table metadata, row counts and timestamps.