About This Automation
Data quality monitoring ensures incoming data meets standards before it reaches dashboards and reports. Manual validation is slow, error-prone, and delays detection of problems by hours or days.
Automation runs validation rules instantly on every data load, calculates quality scores, and alerts teams to issues in minutes. Bad data is caught before it reaches end users.
Top friction points when done manually
The issues teams report most often with this process
Automation readiness
How well-suited this process is for automation
How The Automation Works
The full workflow, from trigger to completion.
Automation is triggered when new data arrives in the staging database or a scheduled validation window opens. The trigger captures the dataset name, row count, and timestamp.
The automation platform executes a suite of pre-configured validation rules against the dataset, checking for nulls, duplicates, out-of-range values, schema compliance, and referential integrity. Results are logged with severity levels.
The automation evaluates validation results, calculates a data quality score, identifies patterns in failures, and determines if the issue is critical, warning, or informational.
If issues are found, a formatted alert is sent to the appropriate channel with the quality score, affected fields, record counts, and a link to the detailed report.
All detected issues are automatically logged to a centralized tracking system with metadata, severity, and timestamp, creating an audit trail and historical record.
If data quality score falls below a critical threshold, an incident is automatically created to page the on-call engineer for immediate investigation.
Validation results and quality metrics are pushed, updating a real-time data quality dashboard that shows trends, failure rates, and issue history.
Most popular tool stack used
— the complete tool combinations companies use
PostgreSQL
Google Sheets
Tableau
Slack
PagerDutyWhat you get when you map this process
Everything you need to understand, plan, and build your automation.
ROI and business case
What this process costs today and what changes once it's automated.
Launch schedule
What gets built, in what order, and what success looks like once it's live.
Process runbook
How the automation runs day to day, including exceptions and human decision points.
Developer handover pack
Full build spec, logic, and configuration — ready to hand off without a briefing call.
Integration and connections guide
Every tool connection, credential, and data mapping the build needs.
Test and QA plan
Every scenario checked and signed off before the automation goes live.
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Frequently asked questions
Everything you need to know before mapping this process.
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