Data Quality Framework – AI-Driven Continuous DQ Management

Overview: Coforge Data Quality Framework is an AI-driven continuous DQ management framework ensuring accuracy, completeness, consistency, and timeliness. Poor DQ costs millions in rework and compliance failures. This framework provides structured profiling, cleansing, improving, and monitoring at scale — powered by Agentic DQ Resolver. Part of Coforge Data Cosmos™ – the innovation backbone comprising platforms, agents, and services that accelerates execution across every phase of the data lifecycle.

4-Phase DQ Lifecycle:

Phase 1: ASSESS • Information Needs & Gaps — Assess current DQ state against business requirements • Data Profiling — Automated profiling of distributions, completeness, validity, uniqueness • Baseline DQ metrics and identify critical data elements requiring governance

Phase 2: PLAN • Define DQ vision, objectives, and priorities through the Business Case & Change Council • Design DQ rules, thresholds, governance policies; assign stewardship and accountability • Plan cleansing strategy: correction rules, standardization logic, deduplication approach • Establish scorecard KPIs and reporting cadence for executive and operational visibility

Phase 3: INNOVATE • Deploy Agentic DQ Resolver — AI-based technology artifact for autonomous anomaly detection, intelligent DQ fix recommendations and subsequent remediation of quality issues • Introduce ML-based entity resolution for fuzzy matching and duplicate consolidation • Enable AI-driven continuous monitoring with predictive alerting on degradation

Phase 4: EXECUTE • Apply automated correction, cleansing, standardization, and enrichment across production data • Activate centralized rules engine — DQ rules enforced consistently across all pipelines • Deploy DQ dashboards, scorecards, and BI reporting for real-time visibility • Monitor continuously with AI/ML-driven alerting; update logic to prevent recurring issues • Manage DQ knowledge base — best practices, playbooks, trend analysis

Governance Layer: • Business Needs — Vision, objectives, pain points • Governance — Policies, Stewardship, Standards • Business Case & Change Council

DQ Value-Added Services: • BI Reporting — Dashboards and scorecards • Analytics — Trend analysis, root cause, predictive insights • Enrichment — Data augmentation • Workflow — Automated issue routing

Industry Applications: • Banking — DQ profiling for BCBS 239. Continuous monitoring for regulatory accuracy. • Insurance — Claims DQ with deduplication. DQ scorecards for actuarial teams. • Travel — Booking DQ across GDS feeds with format standardization. • Healthcare — Patient DQ with PII/PHI profiling and HIPAA compliance.

Business Benefits: • Automated 4-phase lifecycle reducing manual effort by 40–60% • Continuous AI/ML monitoring with proactive alerting • Centralized rules engine for consistent DQ • DQ scorecards for executive visibility

Cloud-Native on AWS: Deployed on Amazon EKS. Amazon Bedrock for AI. AWS Glue for profiling. Amazon S3 for reports. CloudWatch for alerting.