Trusted Data & AI Hub

Project: Trusted Data & AI Hub

Situation

The client needed enterprise data to carry shared business definitions, clear ownership, appropriate access, and quality expectations before it could be reused across analytics and AI.

Task

Bring enterprise data architecture, reusable data products, semantic models, and AI governance together on Microsoft Fabric and Azure.

Action

Defined the route from operational sources to analytical data products and business-facing semantic models
Established responsibilities for domain owners, data stewards, platform teams, analysts, business consumers, and AI teams
Connected naming, metadata, classification, quality, lineage, access, and change-management expectations
Set architecture boundaries across the cloud foundation, analytics platform, governance processes, and security controls
Created an AI governance framework covering suitability, risk, human oversight, validation, and monitoring
Used workshops and consulting to connect implementation decisions with the operating model

Result

Delivered a Trusted Data & AI Hub as both a technical platform and an operating model: a shared route from operational data to governed analytical and AI use with definitions and responsibilities attached throughout.

Technologies

Microsoft Fabric, Azure, OneLake, data products, semantic models, enterprise data architecture, AI governance