Data Quality Monitoring System. @data-quality-monitoring

The Data Quality Monitoring System makes bad data announce itself. Today, quality problems surface at the worst possible point — a dashboard looks wrong in a leadership meeting, and someone traces it backward for two days. This sprint inverts that: your critical datasets get automated checks for freshness, volume, schema changes, nulls, duplicates, and business-rule violations, with anomaly detection catching the drifts nobody thought to write a rule for. The finish state is monitoring live in production, alerts routed to named dataset owners, and detection time for data incidents measured in hours instead of weeks.

Data quality monitoring and AI-assisted checks are part of the DataOps practice Scrums.com builds into its data platforms — nothing built on dirty data survives contact with reality, so the monitoring layer is what keeps everything downstream honest.

Critical Dataset Inventory

The datasets that matter ranked with their consumers — which reports, models, and decisions each feeds — plus a named owner per dataset, because an alert without an owner is noise.

Automated Check Suite

Declarative checks in your stack (dbt tests, Great Expectations, Soda, or your platform's native framework): freshness, row-count thresholds, schema stability, uniqueness, referential integrity, and the business rules your domain demands.

Anomaly & Freshness Detection

Statistical monitoring on volumes, distributions, and arrival patterns to catch the unknown-unknowns — a metric that quietly halves, a segment that vanishes — beyond what explicit rules cover.

Alerting, Lineage & Issue Workflow

Alerts routed by dataset and severity to Slack or your ticketing tool, lineage signals showing what is downstream of a failure, and an issue workflow with status visibility so incidents get resolved, not rediscovered.

1. Scope — Inventory critical datasets, assign owners, and define the check catalog and alert thresholds.
2. Build — Implement checks and anomaly detection, tune thresholds against history to kill false alarms, and wire alert routing.
3. Handover — Monitoring live with dashboards, the issue workflow adopted, and a pattern for covering new datasets by default.

The Data Quality Monitoring System is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

How do you avoid alert fatigue?

Thresholds are tuned against historical data before alerts go live, every alert has a named owner and a severity, and noisy checks are demoted to digest reports. An ignored alert channel is a failed deliverable — tuning is part of the sprint, not an afterthought.

Does this fix the bad data it finds?

It finds, localizes, and routes — fixes stay with the pipeline owners, now armed with lineage showing exactly where the break entered. Systemic fixes uncovered by monitoring often become their own backlog items.

Where does this fit with our warehouse build?

Directly on top of it. If you are implementing the Cloud Data Warehouse Implementation from the menu, this system is its natural second step — the tests it installs become the warehouse's ongoing safety net.