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Data Resource Design

Book Description

Are you struggling with the formal design of your organization's data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organization? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture.

Most public and private sector organizations do not consistently follow a formal data resource design process that begins with the organization's perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organizations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organization in its business endeavors.

Data Resource Design describes how to formally design an organization's data resource to meet its current and future business information demand. It builds on Data Resource Simplexity, which described how to stop the burgeoning data disparity, and on Data Resource Integration, which described how to understand and resolve an organization's disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organization's perception of the business world in which they operate.

Like Data Resource Simplexity and Data Resource Integration, Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data 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, philosophy, and biology, and applies them to properly designing data as a critical resource of an organization. He shows how to understand the business environment where an organization operates and design a data resource that supports the organization in that business environment.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents
  6. Figures
  7. Preface
  8. Acknowledgements
  9. About the Author
  10. Chapter 1 - The End of Illusion
    1. The Illusion about Data Resource Design
      1. Business Information Demand
      2. The Illusion of Meeting the Demand
      3. Disparate Data Resource
    2. Realization of an Illusion
      1. Disparate Data Cycle
      2. Data Resource Drift Toward Disparity
      3. Data Dilemma
      4. Disparate Data Shock
      5. Urgency Addiction
    3. Factors Creating the Illusion
      1. Data Resource Not Designed
      2. Canonical Synthesis
      3. Multiple Personality Disorder
    4. Reasons for the Illusion
      1. Constant Change
      2. Hype-Cycles
      3. Lexical Challenge
      4. Attitudes
        1. Lack of Expertise
        2. Paralysis-By-Analysis
        3. Brute-Force-Physical Approach
        4. Physical Orientation
        5. Suck-And-Squirt
        6. Process Orientation
        7. Warping-The-Business
        8. Buying a Solution
    5. The Insanity of Disparity
      1. Fooling Yourself
      2. Keep Doing What You're Doing
      3. Approaching the Problem is the Problem
      4. Data Resource Insanity
    6. Summary
    7. Questions
  11. Chapter 2 - The Beginning of Reality
    1. Data Resource Reality
    2. Concepts and Principles
      1. Definitions
      2. A Critical Resource
      3. Business Oriented
      4. Understanding and Uncertainty
      5. Quality
      6. Simplicity
      7. Agility
      8. Effective and Efficient
      9. Value Added
      10. Point of Diminishing Returns
      11. Philosophy and Science
      12. The I-Organization
      13. Reality Versus Artificiality
    3. Summary
    4. Questions
  12. Chapter 3 - Data Resource Design Concept
    1. Data Resource Management Concept
    2. Data Architectures
      1. Architecture
      2. The Common Data Architecture
      3. Architectural Independence
      4. Canons
      5. An Organization's Data Architecture
      6. Building Codes Versus Buildings
      7. Supporting Theories
    3. Terminology
      1. Business Terms
      2. Common Data Architecture Terms
      3. Mathematic Terms
      4. Logical Data Model Terms
      5. Database Terms
      6. Rationale
    4. Summary
    5. Questions
  13. Chapter 4 - Formal Data Names
    1. Formal Data Name Concept
      1. Data Name Criteria
      2. Primary Data Names
      3. Semiotic Theory
    2. Data Naming Construct
      1. Data Naming Taxonomy
      2. Data Name Components
      3. Data Name Special Characters
      4. Data Name Component Sequence
      5. Data Name Component Use
      6. Data Name Examples
        1. Data Site
        2. Data Subject
        3. Data Occurrence Group
        4. Data Occurrence Role
        5. Data Subject Hierarchy
        6. Data Reference Set
        7. Data Characteristic
        8. Data Characteristic Variation
        9. Data Characteristic Substitution
        10. Data Value
        11. Data Version
        12. Data Rule
      7. Data Name Vocabulary
    3. Data Name Abbreviations
      1. Data Name Abbreviation Principle
      2. Data Name Word Abbreviations
      3. Data Name Abbreviation Algorithm
      4. Data Name Abbreviation Scheme
    4. Preventing Synonyms and Homonyms
      1. Semantic Silos
      2. Business Term Glossary
      3. Data Subject Thesaurus
    5. Data Naming Guidelines
    6. Summary
    7. Questions
  14. Chapter 5 - Comprehensive Data Definitions
    1. Comprehensive Data Definition Concept
      1. Data Definition Criteria
      2. Comprehensive Data Definition Principles
      3. Elemental and Combined Data
      4. Fundamental and Specific Data
      5. Ontologies and Taxonomies
      6. Data Definition Guidelines
    2. Data Definition Examples
      1. General Data Definition Examples
        1. Year and Quarter
        2. State Agency
        3. Sex and Gender
        4. Race and Ethnicity
        5. National Origin
        6. Native Language
        7. Preferences
      2. Data Definition Inheritance
        1. Chronology
        2. Geographic Coordinates
        3. Person
        4. Records
        5. Specific Data Definition Inheritance
      3. Component Data Definition Examples
        1. Data Site
        2. Data Subject
        3. Data Occurrence Group
        4. Data Occurrence Role
        5. Data Reference Set and Items
        6. Data Characteristic
        7. Data Characteristic Variation
        8. Data Version
    3. Summary
    4. Questions
  15. Chapter 6 - Data Keys
    1. Data Key Concept
    2. Primary Keys
      1. Primary Key Concept
      2. Primary Key Notation
      3. Primary Key Classifications
        1. Primary Key Composition
        2. Temporal Primary Key
        3. Primary Key Meaning
        4. Primary Key Origin
        5. Primary Key Purpose
        6. Primary Key Scope
        7. Primary Key Status
        8. Multiple Primary Keys
        9. Surrogate Keys
      4. Reading Primary Keys
      5. Designating Primary Keys
    3. Foreign Keys
      1. Foreign Key Concept
      2. Foreign Key Notation
      3. Foreign Key Classification
      4. Reading Foreign Keys
      5. Enhanced Notation
      6. Designating Foreign Keys
    4. Secondary Keys
    5. Data Integration Keys
      1. Primary Key Failure
      2. Preventing Primary Key Failure
    6. Summary
    7. Questions
  16. Chapter 7 - Data Relations
    1. Data Relation Concept
      1. Definitions of Terms
      2. Supporting Theories
    2. Data Relation Notation
      1. Data Relation Symbols
        1. Data Subject and Data File Symbols
        2. Data Relation Symbols
      2. Data Relation Types
        1. One-To-One Data Relations
        2. One-To-Many Data Relations
        3. Many-To-Many Data Relations
        4. Resolving a Many-to-Many Data Relation
      3. Recursive Data Relation Types
        1. One-to-one Recursive Data Relation
        2. One-to-Many Recursive Data Relation
        3. Many-to-Many Recursive Data Relation
        4. Resolving a Many-to-Many Recursive Data Relation
      4. Binary and N-ary Data Relations
    3. Data Cardinality
    4. Semantic Statements
      1. Semantic Statement Notation
      2. Nouns and Verbs
      3. Business Rules
    5. Data Hierarchies
      1. Data Subject Hierarchy
      2. Data Subject Type Hierarchy
      3. Data Categories
    6. Data Relation Diagrams
      1. Diagram Segmentation
      2. Diagram Criteria
    7. Summary
    8. Questions
  17. Chapter 8 - Data Normalization
    1. Data Normalization Concept
      1. Terminology
      2. Canonical Synthesis
      3. Orientation
      4. The Five-Schema Concept
    2. Data Normalization
      1. Data Characteristics Within Data Subjects
        1. Repeating Groups
        2. Partial Key Dependencies
        3. Inter-Characteristic Dependencies
        4. Derived Data
        5. Inter-Subject Dependencies
      2. Business Facts Within Data Characteristics
      3. Data Properties Within Data Reference Items
      4. Data Reference Items Within Data Reference Sets
      5. Data Normalization Sequence
    3. Data Subject Optimization
    4. Summary
    5. Questions
  18. Chapter 9 - Data Denormalization
    1. Data Denormalization Concept
    2. Data Deoptimization
      1. Data Subject Partitioning
      2. Data Occurrence Partitioning
      3. Data Characteristic Partitioning
    3. Data Denormalization
      1. Data Physicalization
      2. Data Denormalization Criteria
      3. Data Structure Denormalization
        1. Data Subject Denormalization
        2. Data Occurrence Denormalization
        3. Data Characteristic Denormalization
        4. Data Key Denormalization
        5. Data Relation Denormalization
      4. Data Name Denormalization
      5. Data Definition Denormalization
      6. Data Integrity Rule Denormalization
      7. Documentation Data Denormalization
    4. Summary
    5. Questions
  19. Chapter 10 - Time and Change
    1. Time
      1. Definitions
      2. Granularity of Time
        1. Astronomical Time
        2. Geologic Time
        3. Calendar Time
        4. Clock Time
        5. Christian Era
      3. Temporal Relevance
      4. Primary Keys
      5. Time Periods
      6. Time Relational Data
    2. Change
      1. Longitudinal Data
      2. Historical Changes
        1. Current and Historical Data
        2. History Primary Keys
        3. Primary Key Problems
      3. Change Sources
      4. Change Reasons
      5. Change States
      6. Effective and Transaction Points in Time
      7. Proactive and Retroactive Updating
      8. History Data Subject
      9. Documenting Change
        1. No Change Allowed
        2. No Change History
        3. Same Data Subject History
        4. Separate Data Subject History
        5. Hybrid Data History
    3. Summary
    4. Questions
  20. Chapter 11 - Proper Data Structure
    1. Data Structure Concept
      1. Data Structure Criteria
      2. Proper Data Structure Principles
      3. Names, Definitions, and Structures
      4. Simple and Meaningful
    2. Structured Data Components
      1. Data Relations Between Data Subjects
      2. Recursive Data Relations
      3. Data Hierarchies
      4. Mutually Exclusive Parents
      5. Fixed And Variable Data Hierarchies
    3. Complex Data Components
      1. Textual Data
      2. Voice Data
      3. Video Data
      4. Image Data
      5. Spatial Data
    4. Combining Basic Components
    5. Summary
    6. Questions
  21. Chapter 12 - Precise Data Integrity
    1. Data Integrity Rule Concept
      1. Data Accuracy and Precision
      2. Data Completeness and Suitability
      3. Data Volatility and Currentness
      4. Data Integrity and Process Rules
      5. Precise Data Integrity Criteria
    2. Developing Precise Data Integrity Rules
      1. Data Integrity Rule Name
      2. Data Integrity Rule Normalization
      3. Data Integrity Rule Definition
      4. Data Integrity Rule Notation
      5. Data Integrity Rule Common Words
      6. Data Integrity Rule Types
        1. Data Value Rule
        2. Conditional Data Value Rule
        3. Data Structure Rule
        4. Conditional Data Structure Rule
        5. Data Derivation and Re-derivation Rules
        6. Data Retention Rule
        7. Data Selection Rule
        8. Data Conversion Rule
        9. Other Data Integrity Rules
      7. Data Integrity Rule Inheritance
      8. Data Rule Versions
      9. Explicit Data Integrity Rules
      10. Data Integrity Rule Lockout
      11. Data Integrity Failure Principle
      12. Data Integrity Rule Edits
      13. Data Integrity Rule Management
    3. Summary
    4. Questions
  22. Chapter 13 - Robust Data Documentation
    1. Robust Data Documentation Concept
      1. Robust Data Documentation Criteria
      2. Meta-Data
      3. Data Resource Data
      4. Para-Data
    2. Robust Data Documentation Principles
      1. Data Resource Data Aspect Principle
      2. Complete Data Documentation Principle
      3. Data Documentation Design Principle
      4. Current Data Documentation Principle
      5. Understandable Data Documentation Principle
      6. Non-Redundant Data Documentation Principle
      7. Readily Available Data Documentation Principle
      8. Documentation Known To Exist Principle
      9. Data Resource Guide Principle
    3. Data Resource Data
      1. Data Names
      2. Data Definitions
      3. Data Structure
      4. Data Integrity
      5. Data Provenance
      6. Data Subject Thesaurus
      7. Business Term Glossary
      8. Other Components
    4. Developing Data Resource Data
    5. Summary
    6. Questions
  23. Chapter 14 - Cohesive Data Culture
    1. Cohesive Data Culture Concept
    2. Data Culture Components
      1. Reasonable Data Orientation
      2. Acceptable Data Availability
      3. Adequate Data Responsibility
      4. Expanded Data Vision
      5. Appropriate Data Recognition
    3. Summary
    4. Questions
  24. Postscript
  25. Appendix A - Innovation and Diffusion
  26. Appendix B - ISO 11179
  27. Appendix C - Five-Tier Five-Schema Concept
  28. Appendix D - Data Hierarchies
  29. Glossary
  30. Bibliography
  31. Index