Data Quality, Lineage & Ontology – EA 2.0 Blueprint™ – by Hari Krishna

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Overview & Principles

4

What is EA 2.0?
AI-Augmented Enterprise Architecture
Core Components & Architecture Layers
Governance Model & Operating Principles

Data Sourcing & Integration

8

Complete Data Source Inventory
Integration Patterns (Cloud, COTS, On-Prem)
Performance-Safe Data Collection
Coverage Assurance & Ghost-App Discovery
Finance & Procurement Feed Strategy
ETL / ELT Pipeline Design
Living Master System Index (MSI)
Data Sourcing Risk Playbook

Reasoning & Intelligence Layer

5

Natural-Language Query (NLQ) Design
Predictive Insights Engine
Autonomous Optimisation
Policies, Triggers & Thresholds
Model Governance & AI Trust Fabric

Outbound Actions & Governance

4

Outbound Connectors
ServiceNow GRC Integration
Native Azure Enforcement
Audit & Feedback Loop

Data Quality, Lineage & Ontology

4

Canonical Ontology & Schema
Data Quality Metrics & Gates
Lineage Capture & Provenance Rules
Vocabulary & Taxonomy Governance

Platform Implementation

5

EA 2.0 on Azure (Reference Deployment)
Integration Middleware Setup
Integration Middleware Setup
Graph Database Configuration
Cost Management & Scaling

Governance, Roles & Operations

5

Roles & Responsibilities
Stewardship Workflows
Compliance & Audit Automation
KPIs & Maturity Metrics
Operating Model for Continuous Improvement

Reference Assets & Visual Library

4

Architecture Diagrams (HTML / SVG)
Sample Queries & Dashboards
Templates & Checklists
Glossary & Acronyms

Implementation Playbooks

4

Generic EA 2.0 Implementation Playbook
Quick-Start Pilot Guide
Corporate / Hybrid Reference Implementation
Cloud / Sovereign Environment Variant

FAQ & Troubleshooting

1

FAQ & Troubleshooting

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Data Quality, Lineage & Ontology

Data Quality, Lineage & Ontology

Canonical Ontology & Schema
Data Quality Metrics & Gates
Lineage Capture & Provenance Rules
Vocabulary & Taxonomy Governance

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