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    Data Governance Framework – AI-Driven Enterprise Governance

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    Coforge Data Governance Framework is an AI-driven governance framework built on three pillars — People (governance org, steering committee, councils, roles), Process (governance by design, critical data elements, DQ policies, business rules), and Technology (data management, monitoring, AI-enabled tools). Follows a four-phase approach: Assess (AI-driven profiling, maturity evaluation, regulatory exposure mapping), Plan (strategy, roles, stewardship design, tool selection), Innovate (AI-enabled metadata classification, anomaly detection, predictive governance), and Execute (embed governance into workflows, enforce policies, monitor KPIs, drive continuous optimization). Goals: improve data trust, manage risk & compliance, improve decision-making, and customer accountability. Proven — 40% faster data access and 35% reduced reporting complexity. Deployed on AWS with Amazon EKS, AWS Glue, Amazon Redshift, and Amazon Bedrock.

    Overview

    Overview: Coforge Data Governance Framework is an AI-driven framework treating data as a strategic asset — embedding governance across platforms, business units, and personas. Supports MiFID II, FINRA, GDPR, SEC, BCBS 239, HIPAA. Part of Coforge Data Cosmos™ – the innovation backbone comprising platforms, agents, and services that accelerates execution across every phase of the data lifecycle.

    Three Pillars: • People — Governance Organisation (Steering Committee, Council, Working Groups), Roles (Owners, Stewards, Custodians) • Process — Governance by Design, Critical Data Elements, Business Rules, Measure & Monitor • Technology — Data Management tools, metadata platforms, AI-enabled automation

    AI-Driven Four-Phase Approach:

    Phase 1: ASSESS • AI-driven profiling to identify governance gaps and ownership voids • Evaluate data maturity — ownership clarity, stewardship coverage, quality baselines • Catalogue critical data elements; assess regulatory exposure (GDPR, HIPAA, BCBS 239)

    Phase 2: PLAN • Define governance strategy, vision, roadmap aligned with business objectives • Design roles and policies for ownership, stewardship, quality, security via RACI • Establish governance org structure; select enterprise cloud native tools (Collibra, Alation etc)

    Phase 3: INNOVATE • Integrate Data Cosmos artifacts and accelerators (Data Governance Nexus, AutoClassifier) for intelligent automation • Deploy AI-enabled metadata classification, automated lineage tracking, anomaly detection • Enable predictive governance and policy-as-code enforcement via Coforge Data Governance Nexus and Amazon Bedrock

    Phase 4: EXECUTE • Embed governance into core systems and workflows with targeted training • Enforce policy automation, stewardship workflows, and approval processes • Monitor KPIs continuously — data trust scores, compliance rates, quality trends • Drive continuous optimization through AI-augmented insights and self-service governance

    Core Components: • Data Ownership & Stewardship • Data Quality Management • Metadata & Lineage • Policy & Compliance • Business Glossary

    Industry Applications: • Banking — Governance across multiple databases for BCBS 239, CCAR, DFAST. 40% faster data access, 35% reduced reporting complexity. • Insurance — Policyholder governance with Solvency II evidence. • Travel — Guest data governance with GDPR and PCI DSS compliance. • Healthcare — HIPAA-compliant clinical data governance.

    Cloud-Native on AWS: Deployed on Amazon EKS. AWS Glue Data Catalog. Amazon Redshift. Amazon Bedrock for AI classification. Amazon S3.

    Highlights

    • Three pillars: People (governance org), Process (governance by design), Technology (AI tools)
    • AI-driven 4-phase approach: Assess, Plan, Innovate and Execute
    • Proven — 40% faster data access and 35% reduced reporting complexity

    Details

    Delivery method

    Deployed on AWS
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