Data Observability
Data Observability: See. Alert. Resolve.
Blind spots in data pipelines lead to bad insights. Datafyze’s Data Observability services provide complete visibility tracking lineage, monitoring quality metrics, detecting anomalies, and automating alerting and root-cause workflows—to maintain unwavering trust in your data.
Key Capabilities
Data Lineage Tracking
Map data flow across pipelines to understand origins, transformations, and destinations.
Anomaly Detection
Auto-identify data drift, volume spikes, and schema changes with ML-driven alerts.
Root Cause Analysis
Drill down into pipeline failures and data errors with interactive tracing tools.
Quality Monitoring
Define and measure key metrics (completeness, accuracy, timeliness) across datasets.
Alerting & Reporting
Configure thresholds, real-time notifications, and dashboards for proactive issue resolution.
Proven Outcomes
80% reduction in data incidents through proactive monitoring.
90% faster issue resolution by pinpointing pipeline failures to specific transformations.
Full visibility into data flows, accelerating audits and impact assessments.
FAQs
What is data observability?
Data observability is the practice of monitoring data health—tracking lineage, assessing quality metrics, detecting anomalies, and alerting on issues to ensure reliable analytics and operations.
How do you detect data anomalies?
We apply statistical and machine learning models to baseline normal data patterns, then trigger alerts when deviations occur in volume, schema, or content quality.
Can we trace data lineage across all pipelines?
Yes. Our tools capture metadata at each pipeline stage—creating a comprehensive lineage graph that lets you trace every data point back to its source and transformations.