Chaos to Clarity: Governance and Adoption Lessons from Rolling Out Power BI and Fabric

Description

Power BI + Fabric at scale? Chaos looms—fragmented workspaces, messy permissions, frustrated users. Join us to see how we turned disorder into a governance playbook that accelerates adoption. Learn role-based access, streamlined workspace/app strategies, and AI-ready best practices to avoid pitfalls and win enterprise buy-in.

Key Takeaways

• What breaks down as Power BI and Fabric adoption grows
• Workspaces, permissions, and distribution patterns that didn’t scale
• Role-based access, apps, and enterprise semantic models
• How governance accelerates trust and usage (instead of slowing it down)
• Preparing data and semantic models for Copilot and future AI
• 6. Key Takeaways, Resources, & Q&A
• Governance ≠ locking everything down

My Notes

Action Items

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Resources & Links

Slides

Chaos to Clarity
Governance and Adoption Lessons from Rolling
Out Power BI and Fabric
Chris Aleman
Data Analytics
Manager
Why This Session
Power BI + Fabric adoption scales fast
Governance often lags behind adoption
Result: confusion, rework, loss of trust
Agenda

1. Setting the Stage: Chaos at Scale
   • What breaks down as Power BI and Fabric adoption grows
2. Governance Lessons Learned
   • Workspaces, permissions, and distribution patterns that didn’t scale
3. From Chaos to Clarity
   • Role-based access, apps, and enterprise semantic models
4. Driving Adoption with Guardrails
   • How governance accelerates trust and usage (instead of slowing it down)
5. AI-Ready by Design
   • Preparing data and semantic models for Copilot and future AI
6. Key Takeaways, Resources, & Q&A
   What “Chaos” Looks Like At Scale
   ≠
   Multiple Versions of “truth”
   Fragmented Workspaces
   Messy Permissions
   Frustrated end users
   Governance Is About Behavior
   • Governance ≠ locking everything down
   • Governance = guiding how people work with data
   • Balance empowerment and guardrails
   Our Starting Point
   Early Mistakes We Made
   We moved fast. Then we paid the price
   • Direct workspace access for consumers
   • Department-level security groups
   • No clear separation of build vs consume
   • Consultants learning alongside us
   Why Workspaces Matter
   Security Boundary
   Ownership
   Build | Consume
   Overall Architecture: How We Put It Together
   Workspace Strategy We Landed On
   • Workspaces = build layer only
   • Creators collaborate here
   • Consumers do not live in workspaces
   Apps as the Consumption Layer
   • Apps = trusted entry point
   • Clean navigation
   Apps as the Consumption Layer: Before
   Apps as the Consumption Layer: After
   Apps as the Consumption Layer
   • Role-based audiences
   • Scales adoption cleanly
   Role-Based Access (What Actually Works)
   Entra ID security groups
   Group by job role, not department
   Role-Based Access (What Actually Works)
   • Dynamic membership where possible
   • Fewer exceptions over time
   The Semantic Model Shift
   Semantic Model - Before
   Semantic Model - After
   Enterprise Semantic Models
   • Single source of truth tables
   • Shared semantic models
   • Thin reports
   • Excel + Power BI + NLQ supported
   Source Control & Deployment
   Governing Semantic Models
   Ownership is explicit
   Changes are intentional
   Quality is enforced
   before users feel pain
   Dataflows: Bridge, Not Destination
   • Dataflows Gen2 helped early
   • Some business logic still lives there
   • Long-term goal: push logic upstream
   • Reduce report-side transformations
   Monitoring & Visibility
   Oversharing is a governance risk
   Need visibility into:
   Sharing patterns
   Workspace sprawl
   Orphaned assets
   Monitoring & Visibility
   • Adoption signals (views,
   unique users)
   • Refresh failures / reliability
   • Semantic model duplication
   • Workspace sprawl
   • Sharing patterns (direct
   share, link sprawl)
   Monitoring & Visibility
   Adoption ≠ Enablement
   • Users need clarity, not just access
   • Training and structure matter
   • Apps reduced “where do I go?” questions
   ≠
   Access
   Enablement
   Why AI Raises the Stakes
   Data Quality
   /
   AI / Copilot
   Answers
   What “AI-Ready” Actually Means
   AI / Copilot
   Definitions
   Grain
   Measures
   Access
   If We Did It Again
   Go straight to Snowflake sooner
   Design security groups up front
   Apps from day one
   Fewer like-for-like conversions
   Trust Is the Real KPI
   • Trust is slow to build
   • Easy to lose
   • Consistency beats speed long-term
   • Identify business champions early
   Outcomes
   Consistency
   Governance
   Trust
   Governance Playbook (Summary)
   Monitor Continuously
   Enforce Quality Early
   Shared Semantic Models
   Role-Based Security Groups
   Apps as Default Distribution
   Separate Build / Consume
   Key Takeaways
   Chaos is predictable without structure
   Governance enables adoption
   Semantic models are the contract
   AI rewards discipline, not shortcuts
   Trusted Resources
   • Microsoft Learn Fabric Adoption Roadmap
   • Microsoft Learn Power Bi Implementation Planning
   • sqlbi
   • Tabular Editor
   • Data Goblins
   • Radacad
   • Story Telling With Data
   • Guy in a Cube
   • Chris Webb’s Blog
   • Powerbi.tips
   • Paul Turley
   Thank You!
   Questions?
   Chris Aleman
   Caleman@gvec.org
   Sound off.
   The mic is all yours.
   Influence the product roadmap.
   Join the Fabric User Panel
   Join the SQL User Panel
   Share your feedback directly with our
   Fabric product group and researchers.
   Influence our SQL roadmap and ensure
   it meets your real-life needs
   https://aka.ms/JoinFabricUserPanel
   https://aka.ms/JoinSQLUserPanel
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