O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

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

Book Description

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.

Table of Contents

  1. CHAPTER 1 The Data-Centric Movement
    1. This movement requires executive sponsorship
    2. If you are not an executive
    3. Chapter Summary
  2. CHAPTER 2 What is Data-Centric?
    1. Data-centric vs. Data-driven
    2. We need our applications to be ephemeral
    3. Data-centric approaches are designed with data sharing in mind
    4. The Data-Centric vision
    5. Evolve-able
    6. Specialize-able
    7. Single but federated
    8. Enterprise app store
    9. Includes all types of data
    10. The economics of the end game
    11. Chapter Summary
  3. CHAPTER 3 Getting There
    1. What it requires
    2. Inertial resistance
    3. Overt and covert resistance
    4. What it doesn’t require
    5. This is a program, not a project
    6. The transition to a Data-Centric approach requires discipline and consistency
    7. The IT fashion industry
    8. Is the Data-Centric approach a fad?
    9. Can Data-Centric methods benefit from other fads?
    10. From Fad Surfing to New Discipline
    11. New modeling discipline
    12. New delivery architecture
    13. Chapter Summary
  4. CHAPTER 4 Why We Need This Now
    1. The status quo is getting exponentially worse
    2. Code creates maintenance
    3. Complexity creates high priests
    4. Application-centricity creates silos
    5. Silos create the need for integration
    6. Legacy creates entrenchment
    7. Inflexibility creates shadow IT
    8. Mega projects create mega failures
    9. Where application complexity comes from
    10. A case example in complexity
    11. Separation and isolation
    12. Humans in the loop
    13. The negative network effect
    14. Complexity math and the way out of the quagmire
    15. Chapter Summary
  5. CHAPTER 5 A Deeper Look at Data-Centric Approaches
    1. It’s the data, stupid
    2. Task-centric is a trap
    3. It’s the stupid data
    4. The “what if” view on Data-Centric methods
    5. Fewer models
    6. Simpler models
    7. Integration almost for free
    8. More flexibility
    9. Chapter Summary
  6. CHAPTER 6 A Paradigm Shift
    1. Paradigm shift
    2. The original paradigm shift
    3. How new ideas take hold
    4. Round earth
    5. Heavier than air flight
    6. Scurvy
    7. Hand washing before the germ theory
    8. Non-linear change
    9. Who is not going to help you with your transformation?
    10. Digital transformation
    11. The herd
    12. Social proof
    13. Incentives
    14. Chapter Summary
  7. CHAPTER 7 Case Studies
    1. S&P market intelligence
    2. Sokil
    3. Chapter Summary
  8. CHAPTER 8 Linked Data
    1. When Linked Data and Semantic Technology become Data-Centric
    2. Separating meaning from structure
    3. A single structure for expressing all data
    4. Graph databases (triple stores) for structures
    5. RDF Resource Description Framework
    6. Global identifiers
    7. Dealing with non-unique but unambiguous IDs
    8. Self-assembling data
    9. Resolvable IDs
    10. Follow your nose
    11. Querying a triple store
    12. Linked data
    13. Chapter Summary
  9. CHAPTER 9 Ontologies, Knowledge Graphs, and Semantic Technology
    1. Metadata is triples as well
    2. Formal definitions
    3. Self-describing data
    4. Schema later
    5. Open world
    6. Local constraints
    7. Curated and uncurated data
    8. Ontologies
    9. Modularity and reuse
    10. Self-policing data
    11. Computable models
    12. Integration with relational
    13. Integration with big data
    14. Natural language processing
    15. Semantic standards stack
    16. Chapter Summary
  10. CHAPTER 10 Case Studies with Semantic Technology
    1. Garlik
    2. Montefiore
    3. Chapter Summary
  11. CHAPTER 11 Application Software is the Problem
    1. Isn’t software a good thing?
    2. How much code do we have?
    3. How much do we need?
    4. Where does it all come from?
    5. Chapter Summary
  12. CHAPTER 12 Data-Centric Means Massive Code Reduction
    1. Reducing schema complexity
    2. Reducing schema variety
    3. Making possible massive reuse
    4. Writing to a subset of the schema
    5. Code reduction through integration elimination
    6. Chapter Summary
  13. CHAPTER 13 Model-Driven Everything
    1. Model-driven development
    2. Low-code and No-code
    3. Declarative code
    4. Model-driven constraints and validation
    5. Model-driven Constraints
    6. Model-driven UI
    7. Model-driven identity management
    8. Model-driven security
    9. Chapter Summary
  14. CHAPTER 14 Data-Centric and other Emerging Technology
    1. Big data
    2. Data lakes
    3. Cloud
    4. NLP
    5. Rule-based systems
    6. Machine learning
    7. Microservices
    8. Kafka
    9. Internet of things
    10. Smart contracts
    11. Chapter Summary
  15. CHAPTER 15 Assess Your Starting Point
    1. Accessing your current situation
    2. A small core
    3. Getting to self-funding
    4. Chapter Summary
  16. CHAPTER 16 Executing Your Initial Projects
    1. Think big and start small
    2. Enterprise ontology
    3. Gist as a starting point for your ontology
    4. Pilots, not POCs
    5. True contingencies
    6. Corporate antibodies
    7. Federated development
    8. An enterprise knowledge graph
    9. Chapter Summary
  17. CHAPTER 17 Governance and the New Normal
    1. The new approach becomes “hot”
    2. The executive’s role in piloting the change
    3. A kinder/ gentler voluntary governance structure
    4. Good, better, best
    5. TBox, CBox, ABox
    6. Share the learning
    7. Data-centric maturity
    8. Chapter Summary
  18. CHAPTER 18 Wrapping Up
    1. About Semantic Arts
    2. About the author
  19. Index