Thought Leadership
Practical Perspectives on Data, Metadata, Governance, and AI
My work sits at the intersection of enterprise data strategy, operating models, metadata, governance, and AI. The goal is not theory for its own sake, but clarity that leaders can actually use.
Across my writing and publications, I focus on how data and AI organizations really function—where incentives, ownership, technology, and decision-making meet.
Books
'The Goal is Autonomous Governance:
Moving Data Governance into the Modern Age'
​​
This book is written for enterprise data leaders frustrated with governance models that create friction rather than trust.
​
Rather than treating governance as a compliance exercise, The Goal is Autonomous Governance: Moving Data Governance into the Modern Age explores how organizations can evolve toward autonomous, metadata-driven operating models—where accountability, decision rights, and controls are embedded directly into how data is produced, shared, and consumed.
​
The book is grounded in real-world enterprise experience and focuses on practical evolution, not idealized end states.
​
Key themes include:
​
-
Why traditional governance models break at enterprise scale
-
Metadata as an operating layer, not documentation
-
Ownership, accountability, and decision rights
-
Moving from manual controls to system-enabled governance
-
What “autonomous” really means in modern data and AI organizations
​
'The Goal is Business Capability Reengineering:
A Strategic Framework for the AI Era'
​​
This book is written for enterprise leaders navigating AI transformation who need a strategic map—not just for adding AI to existing tools, but for understanding what the enterprise becomes when AI agents perform the work.
​
Rather than treating AI as a feature layer on top of legacy applications, The Goal is Business Capability Reengineering: Enterprise Architecture for the AI Era explores how organizations will evolve from application-centric to capability-centric operating models—where AI agents execute business capabilities within policy-defined boundaries, and human roles shift from doers to supervisors.
​
The book is grounded in real-world enterprise experience and focuses on strategic frameworks for a transformation that will unfold over the next decade.
​
Key themes include:
​
-
The AI Modernization Cycle: four phases from augmentation to autonomy
-
Modern Business Architecture: a nine-layer framework for the target-state enterprise
-
Capability Applications and Capability Flows replacing traditional applications
-
How roles, governance, and accountability evolve in agent-centric operations
-
Strategic implications for enterprises, SaaS vendors, and consulting firms
​
TDAN Column
Crossing the Data Divide
​
I write a regular column for The Data Administration Newsletter (TDAN) titled Crossing the Data Divide.
​
The column is written for senior data and technology leaders navigating the growing gap between:
​
-
Data ambition and operational reality
-
Centralized control and decentralized delivery
-
Analytics promise and AI risk
Rather than focusing on tools or trends, Crossing the Data Divide explores why data and AI initiatives succeed or fail once they encounter real organizational constraints—including ownership, incentives, operating models, and decision rights.
Common themes include:
​
-
Data leadership and organizational design
-
Metadata as infrastructure, not documentation
-
Governance that enables speed and trust
-
Measuring data value without distortion
-
Preparing data organizations for AI at enterprise scale
Selected Columns from Crossing the Data Divide
​
-
Closing Gaps in Perception of Data as Corporate Asset
-
Data Catalogs and the Generative AI Wave
-
Where Should Data Lineage Rank in a Data Leader’s Priorities?
-
A Framework for Understanding Data Consumers and Producers
​
Executive Briefs & Papers
From time to time, I publish longer-form papers and briefs that go deeper into specific challenges leaders are actively grappling with.
These pieces are designed to be:
​
-
Readable in one sitting
-
Grounded in real enterprise experience
-
Helpful for executive discussion and planning
Topics include:
​
-
Measuring and communicating data value
-
Metadata-driven data mesh and data fabric patterns
-
Governance models for AI-enabled analytics
-
Organizational design for data and analytics teams
How to Use This Content
My goals for all my thought leadership are to be:
​
-
Practical, not academic
-
Opinionated, not vendor-driven
-
Useful for real conversations with executives and teams
​
Many advisory engagements begin with a book, paper, or column that resonates and leads to a deeper discussion.