Root Cause Analysis of Data Issues

Purpose

Root Cause Analysis of Data Issues is designed to correlate data errors with source systems or specific transformations using pattern detection. Its purpose is to support analysis of data quality issues by using ETL logs, data lineage metadata, and comparisons between raw and processed data.

Primary users

The primary user is not specified in the provided information. The agent is associated with AQS and is owned by Ronan Davit.

Where it fits (process/stage/trigger)

This agent fits into data quality investigation workflows when data errors need to be analyzed and linked to possible source systems or transformation steps. It is relevant after data issues are detected and supporting technical evidence such as ETL logs, lineage metadata, or raw versus processed data comparisons is available.

Key capabilities / workflow

The agent analyzes ETL logs, data lineage metadata, and raw and processed data comparisons to detect patterns in data errors. It then uses those patterns to help correlate observed issues with source systems or specific transformations, validates the correlation where possible, and supports the preparation of root cause analysis information.

Inputs

Inputs are not explicitly specified as user-provided fields. The available dataset information indicates that the agent uses ETL logs, data lineage metadata, and raw and processed data comparisons.

Outputs / Deliverables

Outputs are not specified in the provided information. The expected deliverable is therefore not specified, beyond supporting root cause analysis of data issues based on the stated use case.

Value

The agent helps accelerate investigation of data errors by connecting observed issues to likely source systems or transformations using pattern detection. This can support more focused remediation of data quality problems in an insurance context.