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Data Resource Integration: Understanding and Resolving a Disparate Data Resource

Book Description

Are you struggling with a disparate data resource? Are there multiple existences of the same business fact scattered throughout the data resource? Are those multiple existences out of synch with each other? Do you have difficulty finding the data you need to support business activities? Do the data you find have poor quality? If the answer to any of these questions is Yes, then you need this book to guide you toward creating an integrated data resource.

Most public and private sector organizations have a disparate data resource that was created over many years. That disparate data resource contains multiple existences of business facts that are out of synch with each other, are of poor quality, and are difficult to locate. The traditional approach to dealing with a disparate data resource is to perform periodic and temporary data integration to support a specific application or business activity. Those piecemeal data integration efforts may meet a current need, but seldom solve the underlying problems with a disparate data resource, and sometimes make the situation worse.

Data Resource Integration explains how to go about understanding and resolving a disparate data resource and creating a comparate data resource that fully meets an organization's current and future business information demand. It builds on Data Resource Simplexity, which described how to stop the burgeoning data disparity. It explains the concepts, principles, and techniques for understanding a disparate data resource within the context of a common data architecture, and resolving that disparity with minimum impact on the business.

Like Data Resource Simplexity, Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data resources in both public and private sector organizations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, and biology, and applies them to the process of understanding and resolving a disparate data resource. He shows you how to approach and resolve a disparate data resource, and build a comparate data resource that fully supports the business.

Table of Contents

  1. FIGURES
  2. PREFACE
  3. ACKNOWLEDGEMENTS
  4. ABOUT THE AUTHOR
  5. Chapter 1 TOWARD A COMPARATE DATA RESOURCE
    1. RAMPANT DATA DISPARITY
    2. COMPARATE DATA RESOURCE
    3. SUMMARY
    4. QUESTIONS
  6. Chapter 2 DATA RESOURCE INTEGRATION CONCEPT
    1. INTEGRATING DATA RESOURCE MANAGEMENT
    2. INTEGRATING THE DATA RESOURCE
    3. INTEGRATING THE DATA CULTURE
    4. SUMMARY
    5. QUESTIONS
  7. Chapter 3 INTEGRATING THE DATA RESOURCE
    1. CONCEPTS
    2. DATA ARCHITECTURE COMPONENTS
    3. DATA RESOURCE INTEGRATION OVERVIEW
    4. SUMMARY
    5. QUESTIONS
  8. Chapter 4 DISPARATE DATA VARIABILITY
    1. CONCEPTS AND PRINCIPLES
    2. DATA NAME AND DEFINITION VARIABILITY
    3. DATA STRUCTURE VARIABILITY
    4. DATA INTEGRITY VARIABILITY
    5. DATA DOCUMENTATION VARIABILITY
    6. VARIABILITY OVER TIME
    7. SUMMARY
    8. QUESTIONS
  9. Chapter 5 DATA INVENTORY CONCEPT
    1. CONCEPTS
    2. PRINCIPLES
    3. DATA INVENTORY DOCUMENTATION
    4. SUMMARY
    5. QUESTIONS
  10. Chapter 6 DATA INVENTORY PROCESS
    1. DATA INVENTORY PREPARATION
    2. SUPPORTING TECHNIQUES
    3. INVENTORYING DISPARATE DATA
    4. SUMMARY
    5. QUESTIONS
  11. Chapter 7 DATA CROSS-REFERENCING CONCEPT
    1. CONCEPT AND PRINCIPLES
    2. COMMON DATA ARCHITECTURE COMPONENTS
    3. PRODUCT-TO-COMMON CROSS-REFERENCE
    4. INITIAL COMMON DATA ARCHITECTURE
    5. SUMMARY
    6. QUESTIONS
  12. Chapter 8 DATA CROSS-REFERENCING PROCESS
    1. DATA CROSS-REFERENCE PREPARATION
    2. DATA CROSS-REFERENCES
    3. DATA PRODUCTS
    4. INTERIM COMMON DATA ARCHITECTURES
    5. ENHANCEMENT AND ADJUSTMENTS
    6. DATA REDUNDANCY AND VARIABILITY
    7. SUMMARY
    8. QUESTIONS
  13. Chapter 9 PREFERRED DATA ARCHITECTURE CONCEPT
    1. CONCEPTS AND PRINCIPLES
    2. DATA ARCHITECTURES
    3. PREFERRED DATA DESIGNATIONS
    4. DATA TRANSLATIONS
    5. SUMMARY
    6. QUESTIONS
  14. Chapter 10 PREFERRED DATA ARCHITECTURE PROCESS
    1. PREFERRED DATA DESIGNATION PREPARATION
    2. PREFERRED DATA DESIGNATIONS
    3. DATA TRANSLATION RULES
    4. PREFERRED PHYSICAL DATA ARCHITECTURE
    5. PREFERRED DATA TEMPLATES
    6. SUMMARY
    7. QUESTIONS
  15. Chapter 11 DATA TRANSFORMATION CONCEPT
    1. CONCEPTS AND PRINCIPLES
    2. DATA TRANSFORMATION PHASES
    3. SUMMARY
    4. QUESTIONS
  16. Chapter 12 DATA TRANSFORMATION PROCESS
    1. DATA TRANSFORMATION PREPARATION
    2. DATA TRANSFORMATION
    3. DATA TRANSFORMATION SUPPORT
    4. SUMMARY
    5. QUESTIONS
  17. Chapter 13 INTEGRATING THE DATA CULTURE
    1. CONCEPTS AND PRINCIPLES
    2. DATA CULTURE COMPONENTS
    3. DATA CULTURE INTEGRATION
    4. SUMMARY
    5. QUESTIONS
  18. Chapter 14 MANAGING THE DATA RESOURCE
    1. HALTING AND RESOLVING DISPARITY
    2. ON GAINING RESPECT
    3. SUMMARY
    4. QUESTIONS
  19. Appendix A DATA INTEGRITY RULE EXAMPLES
  20. Appendix B DATA HIERARCHY NOTATION
  21. Appendix C AGGREGATED DATA
  22. GLOSSARY
  23. BIBLIOGRAPHY
  24. INDEX