Data Quality Scoring Root Cause Classifier
Description
Classify exceptions and audit findings associated with data quality scoring into root-cause categories such as data quality, interface timing, manual error, policy gap, or system limitation. The MVP would connect Advana, data catalog, ERPs, feeder systems, data dictionaries, lineage tools and produce read-only recommendations for CDAO, OUSD(C), Component Chief Data Officers, system owners.
AI / analytics pattern
NLP classification + clustering
Automation level / stage
analytics triage
Expected benefit
Better remediation targeting, fewer recurring errors, clearer NFR/CAP analytics.
Audit / financial statement impact
Audit traceability and all statements dependent on source data quality
Controls / human review
Human review for exceptions and recommendations; maintain evidence packages, lineage, source citations, model cards, data-quality checks, and periodic QA sampling.
Data needed
Advana, data catalog, ERPs, feeder systems, data dictionaries, lineage tools; master/reference data; audit logs; policy/control requirements; prior exceptions; relevant document evidence.
Possible metrics
root-cause coding accuracy; CAP targeting cycle time; recurring issue reduction
MVP scope
Start with one Component/reporting entity and one subprocess (data quality scoring) for two close/audit cycles; read-only outputs first.
Related material weakness / control objective
Universe of Transactions, data quality and system modernization