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 hands-on training day tackles that gap by demonstrating how to implement a modern data strategy using Microsoft Fabric, transforming abstract concepts like "data products," "domains," and "data mesh" into production-ready solutions that business users actually adopt.

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

This training day walks through the complete implementation framework: structuring workspaces, domains, and capacities to operationalize "discipline at the core, flexibility at the edge"; defining personas, decision rights, and operating models; building medallion architecture patterns with automated quality validation; enriching data models with metadata for AI readiness; and establishing DevOps workflows for controlled promotion and deployment.

Full Day Agenda

**Morning Session (09:00 - 12:30)

*Module 1: Data Strategy Foundations and Fabric Architecture (09:00 - 10:30)

-Why data strategies fail: Common pitfalls and the strategy-to-execution gap
-The six components of effective data strategy: Leadership, people, process, technology, governance, measurement
-Fabric architecture overview: Workspaces, domains, capacities, and OneLake
-Operating models: Centralized, decentralized, and blended approaches

Hands-on Lab: Fabric environment setup: Provisioning Fabric capacity and understanding licensing models; Creating workspace structure aligned to organizational domains; Configuring domain hierarchies and ownership models; Setting up role-based access control (RBAC) for different personas

-Introduction to personas: Data consumers, explorers, analysts, analytics engineers, data engineers, administrators

* Coffee break (10:30 - 10:45)

* Module 2: Medallion Architecture and Data Products (10:45 - 12:30)

-From data assets to data products: What makes a data product
Medallion architecture: Bronze, silver, gold, and platinum layers
Hands-on Lab: Building bronze layer with raw data ingestion

-Creating lakehouse for raw data landing
-Implementing data pipelines for incremental extraction
- Handling schema drift and source system changes
- Establishing data lineage with Purview integration

Hands-on Lab: Silver layer with business rules and quality gates:
Implementing Great Expectations for data quality validation/creating automated quality tests/schema validation, business rule checks, referential integrity; Building Data Activator alerts for quality threshold breaches; Documenting data contracts: schema definitions, SLAs, quality rules Understanding data product ownership and accountability

*Lunch (12:30 - 13:30)

**Afternoon Session (13:30 - 17:00)

*Module 3: Gold Layer and Semantic Models for AI (13:30 - 15:00)

- Gold layer design: Consumption-optimized data products
Hands-on Lab: Creating gold layer with dimensional modeling

- Designing star schemas for analytics consumption
Implementing incremental processing and performance optimization
- Applying row-level and column-level security
- Creating shortcuts for cross-domain data sharing without duplication

-Hands-on Lab: Building AI-ready semantic models: Creating semantic models on gold layer data; Enriching with metadata: descriptions, synonyms, display folders, data categories;
Configuring AI instructions/metadata for Copilot; Building calculation groups for time intelligence; Testing Copilot integration for natural language insights

-Hands-on Lab: MCP server integration: Configuring MCP servers for programmatic AI access; Building custom AI agents that query governed data products; Validating AI interpretation of business metrics

*Coffee break (15:00 - 15:15)

*Module 4: DevOps, Governance, and Centre of Excellence (15:15 - 16:30)

-DevOps for Fabric: Version control, CI/CD, and deployment automation

-Hands-on Lab: Git-based deployment pipelines: Configuring Git integration for Fabric workspaces; Creating Azure DevOps pipelines for automated deployment; Implementing deployment stages: development, test, production; Building automated testing for data quality and semantic model validation
Implementing rollback strategies for failed deployments

-Hands-on Lab: Governance automation Configuring Microsoft Purview for data cataloging and lineage; Applying sensitivity labels and information protection policies; Implementing data loss prevention (DLP) rules; Creating compliance dashboards and audit reports

-Centre of Excellence patterns: Capability management, solution design, shared assets, community enablement

-Hands-on Lab: Creating reusable templates and patterns: Building workspace templates (defined in markdown/JSON) with pre-configured elements required for security/governance; Creating pipeline templates for common ingestion patterns; Establishing documentation standards and wiki templates

- Module 5: Measurement, Value Demonstration, and Continuous Improvement (16:30 - 17:30)

- Measuring success: Adoption metrics, data quality trends, business impact
- Live Demo: Building executive dashboards for data strategy ROI
- Tracking workspace usage and adoption by persona
- Monitoring data quality trends and cost of quality incidents
- Measuring time-to-delivery for new data products
- Calculating ROI: time saved, decisions accelerated, manual processes eliminated
-Operating model maturity assessment framework
- Common pitfalls and lessons learned from real-world implementations
- Scaling patterns: From pilot to enterprise rollout