You are previewing Principles of Data Management.
O'Reilly logo
Principles of Data Management

Book Description

Organisations increasingly view data as a valuable corporate asset and its effective management can be vital to success. This professional guide covers all the key areas including database development, data quality and corporate data modelling. It provides the knowledge and techniques required to successfully implement the data management function.

Table of Contents

  1. Copyright
  2. Dedication
  3. Figures and tables
  4. About the author
  5. Foreword
  6. Glossary
  7. Preface
  8. Introduction
  9. 1 Data and the Enterprise
    1. INFORMATION IS A KEY BUSINESS RESOURCE
    2. THE RELATIONSHIP BETWEEN INFORMATION AND DATA
    3. THE IMPORTANCE OF THE QUALITY OF DATA
    4. THE COMMON PROBLEMS WITH DATA
    5. AN ENTERPRISE-WIDE VIEW OF DATA
    6. MANAGING DATA IS A BUSINESS ISSUE
    7. SUMMARY
  10. 2 Database Development
    1. THE DATABASE ARCHITECTURE OF AN INFORMATION SYSTEM
    2. AN OVERVIEW OF THE DATABASE DEVELOPMENT PROCESS
    3. CONCEPTUAL DATA MODELLING (FROM A PROJECT-LEVEL PERSPECTIVE)
    4. RELATIONAL DATA ANALYSIS
    5. THE ROLES OF A DATA MODEL
    6. PHYSICAL DATABASE DESIGN
    7. SUMMARY
  11. 3 What is Data Management?
    1. THE PROBLEMS ENCOUNTERED WITHOUT DATA MANAGEMENT
    2. DATA MANAGEMENT RESPONSIBILITIES
    3. ROLES WITHIN DATA MANAGEMENT
    4. THE BENEFITS OF DATA MANAGEMENT
    5. THE RELATIONSHIP BETWEEN DATA MANAGEMENT AND ENTERPRISE ARCHITECTURE
    6. SUMMARY
  12. 4 Corporate Data Modelling
    1. WHY DEVELOP A CORPORATE DATA MODEL?
    2. MORE DATA MODELLING CONCEPTS
    3. THE NATURE OF A CORPORATE DATA MODEL
    4. HOW TO DEVELOP A CORPORATE DATA MODEL
    5. CORPORATE DATA MODEL PRINCIPLES
    6. A FINAL THOUGHT
    7. SUMMARY
  13. 5 Data Definition and Naming Conventions
    1. THE ELEMENTS OF A DATA DEFINITION
    2. DATA NAMING CONVENTIONS
    3. SUMMARY
  14. 6 Metadata
    1. WHAT IS METADATA?
    2. METADATA FOR DATA MANAGEMENT
    3. METADATA FOR CONTENT MANAGEMENT
    4. METADATA FOR DESCRIBING DATA VALUES
    5. SUMMARY
  15. 7 Data Quality
    1. WHAT IS DATA QUALITY?
    2. ISSUES ASSOCIATED WITH POOR-QUALITY DATA
    3. THE CAUSES OF POOR-QUALITY DATA
    4. THE DIMENSIONS OF DATA QUALITY
    5. DATA MODEL QUALITY
    6. IMPROVING DATA QUALITY
    7. SUMMARY
  16. 8 Data Accessibility
    1. DATA SECURITY
    2. DATA INTEGRITY
    3. DATA RECOVERY
    4. SUMMARY
  17. 9 Database Administration
    1. DATABASE ADMINISTRATION RESPONSIBILITIES
    2. PERFORMANCE MONITORING AND TUNING
    3. SUMMARY
  18. 10 Repository Administration
    1. REPOSITORIES, DATA DICTIONARIES, ENCYCLOPAEDIAS, CATALOGS AND DIRECTORIES
    2. REPOSITORY FEATURES
    3. THE REPOSITORY AS A CENTRALISED SOURCE OF INFORMATION
    4. METADATA MODELS
    5. SUMMARY
  19. 11 The Management of Data Management
    1. TECHNIQUES AND SKILLS FOR DATA ADMINISTRATION
    2. TECHNIQUES AND SKILLS FOR DATABASE ADMINISTRATION
    3. TECHNIQUES AND SKILLS FOR REPOSITORY ADMINISTRATION
    4. THE POSITIONING OF DATA MANAGEMENT WITHIN THE ENTERPRISE
    5. SUMMARY
  20. 12 Industry Trends and their Effects on Data Management
    1. THE USE OF PACKAGES
    2. DISTRIBUTED DATA AND DATABASES
    3. DATA WAREHOUSING AND DATA MINING
    4. OLAP
    5. OBJECT ORIENTATION AND DATABASES
    6. MULTIMEDIA AND DATABASES
    7. DATA AND WEB TECHNOLOGY
    8. SUMMARY
  21. Appendix A Comparison of Data Modelling Notations
    1. The Ellis-Barker notation
    2. The Chen notation
    3. The information engineering notation
    4. The IDEF1X notation
    5. The UML class notation
    6. Comparison of the notations
  22. Appendix B Hierarchical and Network Databases
    1. Hierarchical databases
    2. Network databases
  23. Appendix C Generic Data Models
  24. Appendix D An Example of a Data Naming Convention
    1. Introduction
    2. Concepts used in naming
    3. The naming of entity types in a conceptual data model
    4. The naming of domains in a conceptual data model
    5. The naming of attributes in a conceptual data model
    6. The naming of relationships in a conceptual data model
    7. The naming of tables in an SQL logical schema
    8. The naming of columns in an SQL logical schema
    9. Rules for abbreviations in data names
    10. Restricted terms
    11. The naming of attributes: a formal description
  25. Appendix E Metadata Models
    1. Introduction
    2. Metadata model to record conceptual data models
    3. Metadata model to record SQL logical schemas
    4. Metadata model to support the mapping of concepts
    5. The inclusion of processes
  26. Appendix F A Data Mining Example
    1. Introduction
    2. The scenario
    3. Step 1
    4. Step 2
    5. Step 3
    6. Step 4
    7. Step 5
    8. Step 6
    9. Conclusions
  27. Appendix G HTML and XML
    1. Introduction
    2. HTML
    3. HTML
  28. Appendix H XML and Relational Databases
    1. Introduction
    2. Representing data from a database as XML
    3. Extracting relational data from an XML document
  29. REFERENCES
  30. FURTHER READING
  31. Index