Most organizations struggle to move beyond data strategy slide decks. They invest in platforms, hire skilled teams, and establish governance frameworks—yet research shows 80% of these initiatives fail to deliver measurable business value. The problem isn't technology—it's the gap between strategic intent and operational reality.

This session tackles that gap head-on by demonstrating how to implement a modern data strategy using Microsoft Fabric, transforming abstract concepts into production-ready data products that business users actually adopt.

The Problem: Organizations face three critical challenges: (1) translating data strategy documents into actionable operating models, (2) balancing central governance with domain autonomy without creating bottlenecks, and (3) preparing data infrastructure that makes AI capabilities genuinely useful rather than just impressive demos.

The Solution: Microsoft Fabric's unified platform—combined with a structured approach to data products, domains, and personas—provides the technical foundation.

But success requires more: clear decision rights, reusable DevOps patterns, automated quality gates, and a Centre of Excellence that enables rather than restricts.

What You'll Learn: In 50 minutes, we'll walk through a practical framework covering:

• How to structure workspaces, domains, and capacities to operationalize "discipline at the core, flexibility at the edge"

• The six components that convert strategy into execution: leadership sponsorship, persona-based skills development, operating model design, medallion architecture patterns, governance automation, and value measurement

• Live demonstration of transforming a typical Excel-based financial process into a governed, AI-ready data product using Power Query, lakehouse layers, semantic models, and natural language queries via Copilot

• Integration with MCP (Model Context Protocol) servers to extend Power BI and Fabric capabilities into AI agent workflows

• Real-world patterns for balancing speed and control: data contracts, Great Expectations quality tests, and Git-based promotion pipelines