Coforge’s Data Cosmos Agentic DQ Resolver

Agentic DQ Resolver is a Coforge Data Cosmos artifact that helps clients on Microsoft Azure through an AI-driven data quality solution designed to diagnose, validate, and remediate data issues with intelligent automation across Azure data platforms. Part of the Coforge Data Cosmos Nebula portfolio, it automates data quality profiling, rule generation, issue fixing, and reporting with GenAI-driven capabilities powered by Azure OpenAI — enhancing data trust and reducing DQ issues by up to 40% on Azure Synapse, Microsoft Fabric, and Azure SQL.

Challenges Addressed: • Manual, error-prone DQ processes that consume weeks of analyst time • Siloed and inconsistent quality rules across databases, files, and cloud platforms • Lack of visibility, audit trails, and compliance-ready reporting for regulators

Core Capabilities: • Deep Data Profiling – Automated insights into data types, distributions, missing values, outliers, anomalies, and PII detection across all connected sources • Smart DQ Rules – Azure OpenAI–generated regex rules, pre-defined templates, and GenAI-suggested rules tailored to each dataset's structure and semantics • Automated DQ Fixes – Rule-based corrections at table or column level for missing values, outliers, format inconsistencies, and referential integrity violations • DQ Scorecards – Metrics on missing values, uniqueness, outliers, completeness, and validity for continuous health assessment with configurable thresholds • Comprehensive Reports – Reconciliation and audit-ready reports capturing issues, applied fixes, and final DQ outcomes for governance and compliance • Enterprise Scalability – Supports APIs, flat files, RDBMS, and Azure cloud platforms via a microservices-based architecture

Value Delivered: • ~40% faster DQ issue detection and resolution • ~30% improvement in compliance and regulatory alignment

Industry Examples: • BFS – Automated DQ profiling on credit risk data ensuring BCBS 239 compliance with audit-ready scorecards and auto-fixing missing counterparty fields • Insurance – Claims data quality assessment detecting missing policyholder fields and auto-fixing inconsistencies before actuarial processing and reserve calculations • Travel – Booking data quality scoring across GDS feeds ensuring revenue accuracy and detecting duplicate reservation records across Amadeus/Sabre integrations • Healthcare – Patient data profiling with automated PII/PHI detection, HIPAA compliance reporting, and auto-correction of malformed clinical codes

The 6-week implementation engagement covers: platform deployment on Azure (AKS, Azure OpenAI, Azure SQL), data source connectivity, profiling & anomaly detection, rule generation & configuration, and automated fix execution with scorecard & report generation, plus knowledge transfer to your Azure data teams.

Target Audience: Data Quality Teams, Data Governance Officers, Compliance Teams, Data Engineers, and CDOs adopting or extending Microsoft Azure.