Data Watch Tool

The Data Watch Tool solves the problem of ongoing data quality and metric monitoring across heterogeneous data sources. It targets data engineers, data analysts, product owners, and operations teams who need visibility into ingestion health, processing latency, and data quality indicators in real time. Used during ETL runs, data lake operations, and BI readiness checks, it identifies drift, missing values, latency spikes, and other anomalies before they impact decisions. The tool emphasizes domain logic and reproducible calculations over UI features, enabling clear definitions of metrics, thresholds, and alerting rules that reflect governance policies.
Core features include multi-source metric collection, time-windowed aggregation, and configurable health scoring. Required inputs are data_sources, metrics, time_window, and thresholds; optional inputs include sampling_rate and group_by. Outputs comprise a health status, per-metric details, trend summaries, and exportable reports in JSON or CSV. Advanced capabilities add anomaly detection via rolling statistics, EWMA smoothing, trend comparison across short and long windows, and region or unit normalization to support multinational deployments. The tool can route alerts to chat, email, or orchestration systems and can attach lineage metadata to each metric to support governance.
Use cases span ETL monitoring, data warehouse health checks, and analytics data readiness. Beneficiaries gain earlier warnings, faster root-cause analysis, and consistent data quality dashboards across teams. The value proposition is a domain-focused, scalable monitoring engine that avoids bespoke scripting, with clear, auditable calculations and exportable results. It intentionally excludes UI-centric features, data modeling, and machine learning model training to stay focused on monitoring and validation logic.