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Data Resource Understanding

Book Description

Are you struggling to understand the data you need to support your business activities? Are you frustrated over data that don't answer your questions or provide the wrong answers to your questions? Are you worried that your organization is not adequately supporting its citizens or customers? Are you concerned over civil or criminal liability for the quality and use of your data? If the answer to any of these questions is Yes, they you need to read Data Resource Understanding to help you and everyone in your organization thoroughly understand the data they need to support the business activities. 


Most public and private sector organizations have no formal method for thoroughly understanding the data needed to support their business activities. They seldom have a method that begins with the organization's perception of the business world and continues through a formal Data Resource Development Cycle to produce a high quality, thoroughly understood data resource that fully supports the organization's current and future business information demand. 


Data Resource Data provided the complete detailed data resource model for understanding and managing data as a critical resource of the organization. 
Data Resource Understanding is the companion book to Data Resource Data. It provides a detailed explanation of how to thoroughly understand an organization's data resource and to document that understanding with Data Resource Data. Together they provide an organization with the foundation for properly managing their data as a critical resource. 


Like Data Resource SimplexityData Resource IntegrationData Resource Design, and Data Resource Data, Michael Brackett draws on over half a century of data management experience, in a wide variety of different public and private sector organizations, to understand and document an organization's data resource. He leverages theories, concepts, principles, and techniques from many different and varied disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to the process of formally documenting an organization's data resource. 

Table of Contents

  1. FIGURES
  2. SCENARIOS
  3. PREFACE
  4. ACKNOWLEDGEMENTS
  5. ABOUT THE AUTHOR
  6. Chapter 1 THE NEED TO UNDERSTAND
    1. A DATA UNDERSTANDING STORY
    2. UNDERSTANDING
    3. DATA RESOURCE DATA
    4. PERCEPTION AND REALITY
    5. META-BUSINESS DATA
    6. THE BOOK
    7. PRESENTATION RULES
      1. Definitions, Descriptions, and Comments
      2. Data Reference Sets
      3. System Identifiers
      4. Sequence
      5. Required Data Characteristics
      6. Data Subject-Relation Diagrams
    8. SUMMARY
  7. Chapter 2 DATA RESPONSIBILITY
    1. ORGANIZATION UNITS
    2. DATA STEWARDS
      1. Data Stewardship Data Reference Sets
    3. SUMMARY
  8. Chapter 3 DATA INVENTORY
    1. DATA INVENTORY OVERVIEW
    2. DATA INVENTORY APPROACH
      1. Data Inventory Sequence
      2. Databases And Dictionaries
      3. Data Product Data Breakdown
    3. DATA PRODUCT DATA
      1. Data Site
      2. Data Product
      3. Data Product Set
      4. Data Product Unit
      5. Data Product Code
    4. DATA PRODUCT KEYS
      1. Primary Keys
      2. Foreign Keys
    5. DATA PRODUCT STEWARD
    6. DATA PRODUCT MODEL
      1. Data Model Diagram
      2. Data Product Model
      3. Data Product Set Model
    7. SUMMARY
  9. Chapter 4 COMMON DATA
    1. COMMON DATA APPROACH
      1. Sequence
      2. Initial Common Data
      3. Levels of Detail
      4. Common Data Names
    2. COMMON DATA NUCLEUS
      1. Basic Common Data
      2. Common Data Reference Sets
      3. Data Subject
      4. Data Characteristic
      5. Data Characteristic Variation
      6. Data Reference Set Variation
      7. Data Reference Item
      8. Data Subject Models
    3. COMMON DATA KEYS
      1. Primary Keys
      2. Primary Key Characteristics
      3. Foreign Keys
      4. Foreign Key Characteristics
    4. DATA SUBJECT STEWARD
    5. DATA SUBJECT AREAS
      1. Data Subject Area
      2. Data Subject Area Assignment
      3. Data Subject Area Model
    6. SUMMARY
  10. Chapter 5 DATA LEXICON
    1. COMMON WORDS
      1. Data Resource Component Type
      2. Data Name Common Word
      3. Data Name Common Word Thesaurus
    2. DATA NAME ABBREVIATION
      1. Data Name Word Set
      2. Data Name Word
      3. Data Name Abbreviation Algorithm
      4. Data Name Abbreviation Scheme
    3. DATA SUBJECT THESAURUS
    4. BUSINESS TERMS
      1. Business Glossary
      2. Business Glossary Item
    5. SUMMARY
  11. Chapter 6 DATA CROSS-REFERENCES
    1. DATA CROSS-REFERENCE APPROACH
      1. Data Cross-Reference Difficulty
      2. Data Cross-Reference Sequence
      3. Data Cross-Reference Comments
      4. Data Cross-Reference Support
    2. DATA PRODUCT SET CROSS-REFERENCE
    3. DATA PRODUCT UNIT CROSS-REFERENCE
    4. DATA PRODUCT CODE CROSS-REFERENCE
    5. OF-TYPE CROSS-REFERENCE
    6. OTHER CROSS-REFERENCES
    7. SPONTANEOUS INVOLVEMENT
    8. SUMMARY
  12. Chapter 7 DATA PROTECTION
    1. DATA PRIVACY AND CONFIDENTIALITY
      1. Jurisdiction
      2. Legal Regulation
      3. Data Privacy Regulation
    2. SUMMARY
  13. Chapter 8 DATA ACCESS
    1. DATA USE
      1. Business Process Steward
      2. Business Process
      3. Data Product Set Process
      4. Data Product Unit Process
    2. DATA CREATION, UPDATE, AND DELETION
    3. SUMMARY
  14. Chapter 9 DATA PROVENANCE
    1. DATA PROVENANCE ORIGIN
    2. DATA PROVENANCE
      1. Data Track
      2. Data Step
    3. SUMMARY
  15. Chapter 10 DATA SHARING
    1. DATA CLEARINGHOUSE
      1. Data Clearinghouse Item
      2. Data Clearinghouse Author
      3. Data Clearinghouse Item Author
      4. Data Clearinghouse Topic
      5. Data Clearinghouse Item Topic
      6. Data Clearinghouse Keyword
      7. Data Clearinghouse Topic Keyword
      8. Spatial Area
      9. Data Clearinghouse Item Area
    2. DATA PROJECTS
    3. SUMMARY
  16. Chapter 11 DERIVED DATA
    1. DERIVED DATA OVERVIEW
    2. EVALUATIONAL DATA
      1. Data Focus
      2. Foreign Data Characteristic Assignment
      3. Analytical Processing
    3. DERIVED DATA HIERARCHY
      1. Data Set Hierarchy
      2. Data Subject Set
      3. Data Subject Set Characteristic
    4. DERIVED SPATIAL DATA SUBJECT
      1. Data Subject Contributor
    5. OPERATIONAL DATA HIERARCHIES
      1. Data Occurrence Hierarchy
      2. Data Subject Hierarchy
    6. SUMMARY
  17. Chapter 12 PREFERRED DATA
    1. PREFERRED COMMON DATA
      1. Preferred Data Characteristic Variations
      2. Preferred Data Reference Set Variations
      3. Preferred Data Definitions
      4. Preferred Data Integrity Rules
      5. Preferred Data Name Abbreviations
      6. Preferred Data Keys
      7. Preferred Data Sources
    2. PREFERRED PHYSICAL DATA
      1. Data Denormalization
      2. Preferred Data Product Data
      3. Preferred Data Products
      4. Preferred Data Product Sets
      5. Preferred Data Product Units
      6. Preferred Data Product Codes
      7. Preferred Data Product Data Names
      8. Preferred Data Product Data Definitions
      9. Preferred Data Product Data Integrity Rules
      10. Preferred Data Product Data Keys
      11. Preferred Data Product Data Cross-References
      12. Multiple Physical Implementations
    3. SUMMARY
  18. Chapter 13 DATA TRANSFORMATION
    1. DATA TRANSFORMATION OVERVIEW
    2. DATA TRANSLATION
      1. Data Characteristic Translation
      2. Data Reference Item Translation
    3. DATA TRANSFORMATION
      1. Data Transform Process
      2. Data Transform Step
      3. Data Transform Unit
    4. SUMMARY
  19. Chapter 14 DATA RESOURCE REALITY
    1. DATA RESOURCE PLAGUE
      1. Hype-Cycles
      2. Lexical Challenge
      3. The Five Horsemen
      4. Data Manipulation Industry
    2. CURING THE DATA RESOURCE PLAGUE
      1. Power Versus Force
      2. Comparate Data
      3. Simplicity
      4. Reality
      5. Perception
      6. Observation Can Influence Reality
      7. Agility
    3. ACHIEVING DATA RESOURCE REALITY
      1. Reality Versus Artificiality
      2. No Silver Bullets
      3. When Is Reality Achieved?
    4. SUMMARY
  20. Chapter 15 DATA RESOURCE DEVELOPMENT
    1. DATA RESOURCE DEVELOPMENT CYCLE
      1. Development Cycle Overview
      2. Business World Perception
      3. Changes In Perception
      4. Data Schema Development
      5. Pre-empting Data Schema Development
      6. Data Resource Use
      7. Data Resource Data
      8. Disparate Data Integration
      9. Quality and Disparity
    2. DATA RESOURCE COMPONENTS
      1. The Missing Descriptive Component
      2. Adding The Descriptive Component
      3. Balancing The Components
    3. THE DATA – INFORMATION – KNOWLEDGE CYCLE
    4. SUMMARY
  21. Chapter 16 RELATIONSHIPS
    1. THE DATA RESOURCE IS ABOUT RELATIONSHIPS
      1. Relationships Are Pervasive
      2. Business World Relationships
      3. Data Schemas Represent Relationships
      4. Operational, Evaluational, and Predictive Data
      5. Degrees Of Data Structuring
      6. Single Data Architecture
    2. RELATIONSHIPS AND COMPUTATIONAL SPACES
      1. Basic Computational Spaces
      2. Additional Computational Spaces
      3. Computational Space Relationships
      4. Relationships And Quality
    3. RELATIONSHIPS AND DATA NORMALIZATION
      1. Data Normalization
      2. Data Normalization Techniques
      3. Temporal And Spatial Relationships
      4. Data Optimization and Deoptimization
      5. Data Denormalization
      6. Data Renormalization
    4. TEMPORAL DATA
      1. Temporal Granularity
      2. Temporal Data Aspects
      3. Temporal Integrity And Navigation
    5. SPATIAL DATA
      1. Spatial Data Systems
      2. Spatial Data Layers
      3. Spatial Data Referencing
      4. Derived Spatial Data
      5. Spatial Data Documentation
    6. PROCESSING RELATIONSHIPS
    7. SUMMARY
  22. Chapter 17 ARCHITECTURES AND MODELS
    1. DATA MODEL DETAIL
      1. Ubiquitous Data Model
      2. Detailed Ubiquitous Data Model
      3. Strategic And Tactical Data Models
      4. Predefined Data Models
      5. Sharing Data
    2. DATA RESOURCE ANOMALIES
      1. Party Anomaly
      2. Other Anomalies
    3. DATA ARCHITECTURE AND MODEL DEVELOPMENT
      1. Data Resource Architecture And Models
      2. Traditional Data Model Problems
      3. Data Resource Models
      4. Data Resource Model Support
    4. SUMMARY
  23. Chapter 18 ACHIEVING DATA UNDERSTANDING
    1. DATA MANIPULATION INDUSTRY
      1. Health Care Analogy
      2. Software Support
      3. Another Analogy
      4. Profit Motivation
      5. A Data Resource Management Approach
    2. THE AGENDA
      1. Industry Standard
      2. The Agenda
      3. Vendor - Customer Paradox
    3. PROFESSIONAL PERSPECTIVE
      1. Current Data Management
      2. A Data Management Profession
      3. Data Management Profession Approaches
      4. Data Management Profession Status
    4. ORGANIZATION DISCRETION
    5. SUMMARY
  24. APPENDIX A DATA RESOURCE DATA CHANGES
    1. ADDITIONS
    2. DELETIONS
  25. BIBLIOGRAPHY
  26. INDEX