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The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems

Shift from application-centric to data-centric to enable your organization to develop more efficient and successful Enterprise Information Systems.

This book is the first part of a trilogy to follow Software Wasteland. In Software Wasteland, we detailed the current poor state of application software development. We offered some tactical advice for reducing some of the worse of the excess. This is the first book in the “what to do instead” trilogy.

“Even if the thought of data modeling makes you cringe, Dave McComb’s latest book makes the case that it is a necessary exercise for the data-driven organization. The ‘Data-Centric Revolution’ shows how to be data-driven in an extensible, flexible way that is baked-into organizational culture, rather than taking a typical project-by-project approach. The book is a fun, insightful and meaty read, well-illustrated, and with endless wonderful examples.”
Doug Laney, Principal, Data & Analytics Strategy, Caserta, and author of the best-seller, “Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage”

“Dave McComb has laid out a roadmap to travel the exciting path towards data centricity. Dave’s passion for semantic modeling is contagious and his expert advice will give you the motivation to rethink application development and the direction needed to deliver value in your organization with linked data.”
Nic Seyot, Executive Director, Information Management at a major investment bank

“In his new book, Dave teaches us why most of the stack we’ve spent decades trying to maintain is just a big, unmanageable pile of duplicative, inflexible code. He shows us how to collapse the stack and blend the logic and data each business needs to thrive, in one contextually rich, machine readable, dynamic, smart data layer. The bloated app and process layers of the stack go away, leaving a thin execution layer calling on the power of the smart data underneath. After ‘Software Wasteland’ explained the problem, ‘The Data-Centric Revolution’ articulates the solution.”
Alan Morrison, Sr. Research Fellow, New Services and Emerging Tech, PwC

From the age of punched cards to today’s internet-driven systems, one thing has stayed fairly constant: software vendors and their implementers have been driving the Enterprise IT industry. This is changing. It will be hard to see initially, but it’s already happening in some more prescient organizations.

As organizations realize they can take control of their own destiny by adopting data-centric principles, they will see their dependency on application software wither. The cost of running internal information systems will drop at least ten-fold, and the cost of integrating them will drop even more rapidly. This will decimate the $400 billion/ year application software industry and the $400 billion/year systems integration industry. The benefit will accrue to the buyers, and will accrue earliest to the first movers.
The trajectory of this book is as follows:

  • Chapters 1 through 3 lay the data-centric foundation. Chapter 1 introduces the data-centric movement and the prerequisites that must be in place for success (including roles and responsibilities). Chapter 2 defines data-centric and explores a data-centric vision and approaches. Chapter 3 covers the management requirements in achieving a data-centric paradigm shift and reveals the new modeling discipline and delivery architecture.
  • Chapters 4 through 6 explain the data-centric approach and its rewards. Chapter 4 summarizes why the data-centric approach will save incredible amounts of time and money. Chapter 5 explores various data centric approaches, and the underlying themes of flexibility and simplicity. Chapter 6 broadens the discussion of paradigm shifts and also discusses who will help you lead this data-centric approach.
  • Chapters 7 through 10 discuss case studies and ways of organizing data. Chapters 7 and 10 discuss several case studies that have taken the data-centric approach. Chapter 8 explains linked data and semantic technologies, and Chapter 9 ontologies and knowledge graphs.
  • Chapters 11 through 13 dig deeper into the pitfalls of the application-centric mindset and the benefits of the data-centric mindset. Chapter 11 gets to the root of the application-centric mindset: application software. Chapter 12 reveals the benefits of code reduction and Chapter 13 the benefits of the model-driven approach.
  • Chapters 14 through 18 explain how to implement the data-centric paradigm. Chapter 14 explains how new technologies fit in with the data-centric approach. Chapters 15 and 16 cover how to get started. Chapter 17 explains the important role of governance in the data-centric approach. Chapter 18 summarizes the key takeaways.

Request review copy (Instructors only) | Interview with author Dave McComb on why IT spends too much


Chapter 1: The Data-Centric Movement
This movement requires executive sponsorship
If you are not an executive

Chapter 2: What is Data-Centric?
Data-centric vs. Data-driven
We need our applications to be ephemeral
Data-centric is designed with data sharing in mind
The Data-Centric vision
Single but federated
Enterprise app store
Includes all types of data
The economics of the end game

Chapter 3: Getting There
What it requires
Inertial resistance
Overt and covert resistance
What it doesn’t require
This is a program, not a project
The transition requires discipline and consistency
The IT fashion industry
Is the Data-Centric approach a fad?
Can Data-Centric methods benefit from other fads?
From Fad Surfing to New Discipline
New modeling discipline
New delivery architecture

Chapter 4: Why We Need This Now
The status quo is getting exponentially worse
Code creates maintenance
Complexity creates high priests
Application-centricity creates silos
Silos create the need for integration
Legacy creates entrenchment
Inflexibility creates shadow IT
Mega projects create mega failures
Where application complexity comes from
A case example in complexity
Separation and isolation
Humans in the loop
The negative network effect
Complexity math and the way out of the quagmire

Chapter 5: A Deeper Look at Data-Centric Approaches
It’s the data, stupid
Task-centric is a trap
It’s the stupid data
The “what if” view on Data-Centric methods
Fewer models
Simpler models
Integration almost for free
More flexibility

Chapter 6: A Paradigm Shift
Paradigm shift
The original paradigm shift
How new ideas take hold
Round earth
Heavier than air flight
Hand washing before the germ theory
Non-linear change
Who is not going to help with your transformation?
Digital transformation
The herd
Social proof

Chapter 7: Case Studies
S&P market intelligence

Chapter 8: Linked Data
When Linked Data becomes Data-Centric
Separating meaning from structure
A single structure for expressing all data
Graph databases (triple stores) for structures
RDF Resource Description Framework
Global identifiers
Dealing with non-unique but unambiguous IDs
Self-assembling data
Resolvable IDs
Follow your nose
Querying a triple store
Linked data

Chapter 9: Ontologies and Knowledge Graphs
Metadata is triples as well
Formal definitions
Self-describing data
Schema later
Open world
Local constraints
Curated and uncurated data
Modularity and reuse
Self-policing data
Computable models
Integration with relational
Integration with big data
Natural language processing
Semantic standards stack

Chapter 10: Case Studies with Semantic Technology

Chapter 11: Application Software is the Problem
Isn’t software a good thing?
How much code do we have?
How much do we need?
Where does it all come from?

Chapter 12: Data-Centric Means Massive Code Reduction
Reducing schema complexity
Reducing schema variety
Making possible massive reuse
Writing to a subset of the schema
Code reduction through integration elimination

Chapter 13: Model-Driven Everything
Model-driven development
Low-code and No-code
Declarative code
Model-driven constraints and validation
Model-driven Constraints
Model-driven UI
Model-driven identity management
Model-driven security

Chapter 14: Data-Centric and other Emerging Technology
Big data
Data lakes
Rule-based systems
Machine learning
Internet of things
Smart contracts

Chapter 15: Assess Your Starting Point
Accessing your current situation
A small core
Getting to self-funding

Chapter 16: Executing Your Initial Projects
Think big and start small
Enterprise ontology
Gist as a starting point for your ontology
Pilots, not POCs
True contingencies
Corporate antibodies
Federated development
An enterprise knowledge graph

Chapter 17: Governance and the New Normal
The new approach becomes “hot”
The executive’s role in piloting the change
A kinder/ gentler voluntary governance structure
Good, better, best
TBox, CBox, ABox
Share the learning
Data-centric maturity

Chapter 18: Wrapping Up
About Semantic Arts
About the author

About Dave

Dave McComb is the author of the bestseller, Software Wasteland, and the President and co-founder of Semantic Arts, a consulting firm that helps organizations uncover the meaning of the data from their information systems. For 20 years, Semantic Arts has helped firms of all sizes in this endeavor, including Procter & Gamble, Goldman Sachs, Schneider-Electric, Lexis Nexis, Dun & Bradstreet and Morgan Stanley. Prior to Semantic Arts, Dave co-founded Velocity Healthcare, where he developed and patented the first fully model driven architecture. Prior to that, he was part of the problem.

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