Missed anomalies. Integration gaps. Alert fatigue. Choosing the wrong data quality and observability solution can stall AI projects, inflate cloud costs, and undermine trust in your data.
This data observability checklist helps you assess vendors with precision, so your data pipelines stay healthy, scalable, and secure.
Evaluate vendors on the following criteria:
- Ecosystem integration: Ensure compatibility with your data lakes, warehouses, catalogs, orchestration tools, and more.
- Anomaly detection: Understand machine learning models, training timelines, and custom metric support.
- Data quality metrics: Measure completeness, accuracy, timeliness, and other KPIs—out of the box or customized.
- Monitoring & alerting: Evaluate coverage across your pipeline and how alerts flow into your tools and teams.
- Scalability & deployment: Determine fit across SaaS, hybrid, or on-prem environments—and meet enterprise-grade security and performance needs.