Data Interoperability: Unified Architecture Connecting All of Your Data, by Dave Wells
Break free from data silos and build an architecture where systems work together.
A Second Look at Operational and Analytical Data
Operational Systems: The Data Workhorses
The Comfort of Analytical Data
The Interoperability Imperative
Expanding the View of Operational Systems
Understanding the Operational Systems Portfolio
Management Challenges of Operational Systems
What Happened to Operational Data Architecture?
Managing Operational Data
Data Sprawl: When Everything is Everywhere
Data Silos: Islands of Information
Data Disparity: When the Pieces Don’t Fit
Data Friction: When Data Gets in the Way
Global Data: The Geography of Complexity
Data Chaos: When it all Comes Apart
The Typical Response: Point-to-Point Pile-Up
Managing Analytical Data
Platform Sprawl: Every Architecture Stays in Play
Analytical Silos: Isolated Islands of Insight
Metric Disparity: When the Same Measure Means Different Things
Tool Friction: When Analytics Gets in its Own Way
Legacy Gravity: The Pull Keeping Old Systems in Orbit
Analytical Data Chaos: The Compounding Effect
Why Architecture Matters Now
Rethinking Data Management Architecture
Modernizing and Unifying Data Management Architecture
Next Generation Data Architecture
Data Interoperability versus Data Integration
Why Data Interoperability?
Architectural Barriers to Interoperability
The Role of Semantics in Data Management
The Semantic Layer and Data Interoperability
Knowledge Graphs
Property Graphs
The Semantic Modeling Process
APIs and Data Services
Data Products
Data Contracts
Data Virtualization
Semantic Data Mapping
Data Translation
Data Linking
Tools and Technologies
Adapting Your Architecture for Interoperability
Before Interoperability
With Interoperability
For decades, organizations have relied on copying and transforming data across warehouses, lakes, and point-to-point pipelines. That approach enabled reporting and analytics, but it also created technical debt. Move beyond endless integration toward a sustainable model where systems exchange data with shared meaning and purpose.
Dave Wells draws on decades of experience in data architecture, governance, and analytics to explain why interoperability is the foundation of next-generation data management. Through clear examples and practical design patterns, he explores both the operational and analytical data landscapes, highlighting the pain points of sprawl, silos, disparity, and friction—and how a semantic approach resolves them. You’ll discover why traditional architectures fail under modern demands, and how to design environments that are simpler, faster, and more resilient.
Unlike books that focus only on tools or technology, this guide emphasizes the principles of interoperability: semantics, data contracts, APIs, data products, and enterprise semantic layers. With these concepts, Wells shows how to harmonize operational and analytical systems, reduce redundancy, and create data ecosystems that adapt to change rather than collapse under it. The result is an architecture that balances innovation with governance and supports AI, analytics, and digital transformation at scale.
Readers will learn how to design for interoperability across domains, apply semantic data modeling, and create reusable data products that deliver trusted, fit-for-purpose information. Each chapter provides architectural insights that help organizations replace chaos with clarity, fragmentation with coherence, and fragile connections with resilient design.
Whether you are a Chief Data Officer, business analyst, data architect, data modeler, data governance lead, or business professional frustrated with disconnected systems, use this roadmap to build a connected, sustainable, and future-ready data environment.
Dave Wells is a data management consultant and educator with experience across a broad spectrum of data management processes and practices. As a consultant he provides advice, direction, and guidance for data architecture, data quality, data governance, data integration, and data interoperability. As an educator, he is the Director of Education and an instructor at eLearningCurve and instructor for a variety of courses at Dataversity. Several decades of information systems, data management, and business management experience give Dave a well-balanced perspective about the synergies of business, information, data, and technology. Knowledge sharing and skills building are Dave’s passions, carried out through consulting, speaking, teaching, and writing.
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