Data quality issues are incidents that happen to be silent. The best teams treat them that way.
Dimensions
Freshness, volume, schema, accuracy, completeness, uniqueness. Monitor all six.
Tools
Monte Carlo, Bigeye, Elementary. dbt tests for minimum viable coverage.
Anomaly Detection
ML-based on important metrics. Catches subtle drift humans won't notice.
Alerting And Ownership
Alerts route to data owners. Incident response. Post-mortems.
Who This Is For
- Data and analytics engineering leaders
- CTOs modernizing their data stack
- Teams making decisions off data they can't yet trust
Common Mistakes
- Buying the stack before defining what decisions it supports
- Ignoring data contracts until pipelines break at 3am
- Assuming dashboards equal data quality
Business Impact
- Single source of truth for every business metric
- Analytics velocity that matches product velocity
- Data systems that power AI without rewrites
Frequently Asked Questions
Coverage target?
100% critical tables. 80%+ of all tables.
Build or buy?
Buy at scale. dbt tests cover the basics cheaply.
Who owns?
Data producers. Downstream consumers file bugs, not fix-it tickets.
Why AIM Tech AI
- Custom-built systems, not templates or off-the-shelf wrappers
- AI + backend + cloud + infrastructure expertise in one team
- Built for production scale, not demo-day experiments
- Beverly Hills, California — serving clients worldwide
Build Systems, Not Experiments
AIM Tech AI designs and ships AI, cloud, and custom software systems for companies ready to turn technology into real business advantage.
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