Non-conformance analytics dashboard built with JSON data pipeline

QMS ETQ Reliance
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1

We successfully implemented a comprehensive non-conformance analytics dashboard that pulls data from ETQ Reliance through a custom JSON-based ETL pipeline. The business requirement was to provide real-time trend analysis across multiple facilities with automated data quality validation.

Our approach centered on JSON schema validation at the extraction layer to ensure data integrity before loading into Oracle BI. The pipeline extracts non-conformance records, validates against predefined schemas, performs quality checks on critical fields (root cause, CAPA linkage, closure dates), and transforms the data for our analytics platform.

Key challenge was handling the ETL pipeline design for incremental loads while maintaining referential integrity across related modules. We built validation rules that flag incomplete records and established automated alerts for data anomalies. The Oracle BI integration required custom connectors to handle ETQ’s nested JSON structures.

Results have been impressive - we achieved 92% improvement in corrective action tracking visibility. Executive dashboards now show trend analysis across departments, facilities, and product lines with drill-down capabilities to individual non-conformance records.

2

We implemented change data capture using ETQ’s modified timestamp fields combined with a staging layer approach. The pipeline maintains a watermark table tracking the last successful extraction timestamp for each entity type. Each run queries records where modified_date > last_watermark.

Critical aspect is handling deletes and relationship changes. We built a reconciliation process that runs weekly to catch any missed updates due to timestamp discrepancies or system issues. The staging layer temporarily holds new data while data quality checks run before promoting to the production analytics schema.

3

What’s your strategy for the ETL pipeline design regarding incremental updates? We’re currently doing full extracts nightly but it’s becoming unsustainable as our non-conformance volume grows. How do you track changes in ETQ to only pull deltas?

4

The 92% improvement in corrective action tracking is remarkable. Could you elaborate on what specific trend analysis capabilities you built and how they integrate with Oracle BI? We need to demonstrate ROI for a similar initiative.