What you will learn
- Which checks catch real data problems earliest
- How anomaly detection complements fixed thresholds
- The build order from table checks to incident workflows
- Why alert quality decides whether monitoring gets used
What this project is
Data breaks quietly. A source system changes a status code, a pipeline drops a partition, a vendor renames a field, and reports keep publishing with plausible-looking wrong numbers. Quality monitoring exists to catch those breaks at the data layer, before they become boardroom arguments.
The platform's job is triage, not just detection. Every table can generate hundreds of checks, and a system that alerts on all of them teaches teams to ignore it. Good monitoring ranks problems by business impact, routes them to owners, and tracks them to resolution.
Core features
- Automated checks for freshness, volume, uniqueness, null rates, ranges, and referential integrity.
- Anomaly detection on metrics over time, catching gradual drift that fixed thresholds miss.
- Quality scores per table and per dataset, combining check results into a readable signal.
- Incident workflows with ownership, severity, status, and resolution notes.
- Check scheduling tied to pipeline runs, so checks run on fresh data, not on a timer alone.
- Historical check results, so teams see when quality started degrading.
How it is built
- Core checks first. Freshness, volume, and null rates on the tables stakeholders rely on most.
- Ownership and routing second, so alerts reach the team that can fix them.
- Anomaly detection third, once baseline history exists to learn from.
- Incident workflows fourth, tracking problems to resolution with notes.
- Scoring and reporting last, summarising quality for leadership and data owners.
Technology considerations
Checks should run where the data lives, in the warehouse, rather than copying tables elsewhere for inspection. Results need a time-series store so trends and drift are visible, not just the latest pass or fail. Alerting must integrate with the team's existing incident tooling, or alerts become another inbox nobody reads. Costs scale with table counts and check frequency; confirm with a quote.
- In-warehouse checks with results stored as time series.
- Anomaly detection trained on each table's own history.
- Incident records linked to checks, owners, and resolutions.
- Integration with existing alerting and incident tools.
Challenges to plan for
- Alert fatigue. Too many low-value checks destroy trust, so check design must prioritise business impact.
- Threshold tuning. Fixed thresholds break on seasonal data, which is where anomaly detection earns its place.
- Ownership gaps. An alert with no owner is a notification, not a workflow, so every check needs a named team.
- Check maintenance. Checks rot as schemas change, so check definitions need review alongside pipeline changes.
A worked example
A bank's regulatory report is built from a loans table, and one month a source system change quietly starts writing nulls into the interest-rate column for a subset of loans. The quality platform's checks on null rates fire the day after the change, and the incident routes to the loans pipeline owner with the affected table, the failing check, and the history showing null rates jumping from near zero to twelve percent overnight.
The owner traces the nulls to a new loan product whose rate field maps differently, fixes the mapping, and backfills the affected rows. The incident record notes the root cause and the fix, and the team adds a range check on interest rates so a future mapping error that writes zeros instead of nulls is also caught. The regulatory report ships on time with correct rates, and the quality score for the loans dataset, which dipped during the incident, recovers visibly in the monthly summary. Without monitoring, the nulls would have reached the regulator as understated interest income.
Who it is for, and its limits
This fits analytics teams whose reports feed decisions or external reporting, and any team that has been embarrassed by wrong data reaching stakeholders.
It does not fit a single small warehouse where pipeline checks plus manual review suffice. It also does not fix source quality; it makes source problems visible and accountable.
Frequently asked questions
What checks should exist on every important table?
Freshness, row volume, null rates on key columns, uniqueness of keys, and value ranges. Those five catch most breaks.
How does anomaly detection help beyond thresholds?
It learns each metric's normal pattern, including seasonality, and flags gradual drift that a fixed threshold would miss.
Who should own a data quality alert?
The team owning the pipeline or source for that table, recorded on the check, not a central data team alone.
What is a data quality score?
A summary of check results for a table or dataset, useful for prioritising fixes, not a substitute for reading the failed checks.
How often should checks run?
After each pipeline run for freshness-critical tables, and on a schedule for slower-moving reference data.
What is the first sign of a data quality problem?
Usually a check failure on freshness, volume, or null rates on an important table. Those five basic checks catch most breaks before stakeholders see wrong numbers.