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Information-Driven Business: How to Manage Data and Information for Maximum Advantage

Book Description

Information doesn't just provide a window on the business, increasingly it is the business. The global economy is moving from products to services which are described almost entirely electronically. Even those businesses that are traditionally associated with making things are less concerned with managing the manufacturing process (which is largely outsourced) than they are with maintaining their intellectual property.

Information-Driven Business helps you to understand this change and find the value in your data. Hillard explains techniques that organizations can use and how businesses can apply them immediately. For example, simple changes to the way data is described will let staff support their customers much more quickly; and two simple measures let executives know whether they will be able to use the content of a database before it is even built. This book provides the foundation on which analytical and data rich organizations can be created.

Innovative and revealing, this book provides a robust description of Information Management theory and how you can pragmatically apply it to real business problems, with almost instant benefits. Information-Driven Business comprehensively tackles the challenge of managing information, starting with why information has become important and how it is encoded, through to how to measure its use.

Table of Contents

  1. Copyright
  2. Preface
  3. Acknowledgments
  4. 1. Understanding the Information Economy
    1. 1.1. DID THE INTERNET CREATE THE INFORMATION ECONOMY?
    2. 1.2. ORIGINS OF ELECTRONIC DATA STORAGE
    3. 1.3. STOCKS AND FLOWS
    4. 1.4. BUSINESS DATA
    5. 1.5. CHANGING BUSINESS MODELS
    6. 1.6. INFORMATION SHARING VERSUS INFRASTRUCTURE SHARING
    7. 1.7. GOVERNING THE NEW BUSINESS
    8. 1.8. SUCCESS IN THE INFORMATION ECONOMY
    9. 1.9. NOTES
  5. 2. The Language of Information
    1. 2.1. STRUCTURED QUERY LANGUAGE
    2. 2.2. STATISTICS
    3. 2.3. XQUERY LANGUAGE
    4. 2.4. SPREADSHEETS
    5. 2.5. DOCUMENTS AND WEB PAGES
    6. 2.6. KNOWLEDGE, COMMUNICATIONS, AND INFORMATION THEORY
    7. 2.7. NOTES
  6. 3. INFORMATION GOVERNANCE
    1. 3.1. INFORMATION CURRENCY
    2. 3.2. ECONOMIC VALUE OF DATA
    3. 3.3. GOALS OF INFORMATION GOVERNANCE
    4. 3.4. ORGANIZATIONAL MODELS
    5. 3.5. OWNERSHIP OF INFORMATION
    6. 3.6. STRATEGIC VALUE MODELS
    7. 3.7. REPACKAGING OF INFORMATION
    8. 3.8. LIFE CYCLE
    9. 3.9. NOTES
  7. 4. Describing Structured Data
    1. 4.1. NETWORKS AND GRAPHS
    2. 4.2. BRIEF INTRODUCTION TO GRAPHS
    3. 4.3. RELATIONAL MODELING
    4. 4.4. RELATIONAL CONCEPTS
    5. 4.5. CARDINALITY AND ENTITY-RELATIONSHIP DIAGRAMS
    6. 4.6. NORMALIZATION
      1. 4.6.1. FIRST NORMAL FORM
      2. 4.6.2. SECOND NORMAL FORM
      3. 4.6.3. THIRD NORMAL FORM
      4. 4.6.4. BOYCE-CODD NORMAL FORM
      5. 4.6.5. FOURTH NORMAL FORM
      6. 4.6.6. FIFTH NORMAL FORM
    7. 4.7. IMPACT OF TIME AND DATE ON RELATIONAL MODELS
    8. 4.8. APPLYING GRAPH THEORY TO DATA MODELS
    9. 4.9. DIRECTED GRAPHS
    10. 4.10. NORMALIZED MODELS
    11. 4.11. NOTE
  8. 5. Small Worlds Business Measure of Data
    1. 5.1. SMALL WORLDS
    2. 5.2. MEASURING THE PROBLEM AND SOLUTION
    3. 5.3. ABSTRACTING INFORMATION AS A GRAPH
    4. 5.4. METRICS
    5. 5.5. INTERPRETING THE RESULTS
    6. 5.6. NAVIGATING THE INFORMATION GRAPH
    7. 5.7. INFORMATION RELATIONSHIPS QUICKLY GET COMPLEX
    8. 5.8. USING THE TECHNIQUE
    9. 5.9. NOTE
  9. 6. Measuring the Quantity of Information
    1. 6.1. DEFINITION OF INFORMATION
    2. 6.2. THERMAL ENTROPY
    3. 6.3. INFORMATION ENTROPY
    4. 6.4. ENTROPY VERSUS STORAGE
    5. 6.5. ENTERPRISE INFORMATION ENTROPY
    6. 6.6. DECISION ENTROPY
    7. 6.7. CONCLUSION AND APPLICATION
    8. 6.8. NOTES
  10. 7. Describing the Enterprise
    1. 7.1. SIZE OF THE UNDERTAKING
    2. 7.2. ENTERPRISE DATA MODELS ARE ALL OR NOTHING
    3. 7.3. THE DATA MODEL AS A PANACEA
    4. 7.4. METADATA
    5. 7.5. THE METADATA SOLUTION
    6. 7.6. MASTER DATA VERSUS METADATA
    7. 7.7. THE METADATA MODEL
    8. 7.8. XML TAXONOMIES
    9. 7.9. METADATA STANDARDS
    10. 7.10. COLLABORATIVE METADATA
    11. 7.11. METADATA TECHNOLOGY
    12. 7.12. DATA QUALITY METADATA
    13. 7.13. HISTORY
    14. 7.14. EXECUTIVE BUY-IN
    15. 7.15. NOTES
  11. 8. A Model for Computing Based on Information Search
    1. 8.1. FUNCTION-CENTRIC APPLICATIONS
    2. 8.2. AN INFORMATION-CENTRIC BUSINESS
    3. 8.3. ENTERPRISE SEARCH
    4. 8.4. SECURITY
    5. 8.5. METADATA SEARCH REPOSITORY
    6. 8.6. BUILDING THE EXTRACTS
    7. 8.7. THE RESULT
    8. 8.8. NOTE
  12. 9. Complexity, Chaos, and System Dynamics
    1. 9.1. EARLY INFORMATION MANAGEMENT
    2. 9.2. SIMPLE SPREADSHEETS
    3. 9.3. COMPLEXITY
    4. 9.4. CHAOS THEORY
    5. 9.5. WHY INFORMATION IS COMPLEX
    6. 9.6. EXTENDING A PROTOTYPE
    7. 9.7. SYSTEM DYNAMICS
    8. 9.8. DATA AS AN ALGORITHM
    9. 9.9. VIRTUAL MODELS AND INTEGRATION
    10. 9.10. CHAOS OR COMPLEXITY
    11. 9.11. NOTES
  13. 10. Comparing Data Warehouse Architectures
    1. 10.1. DATA WAREHOUSING
    2. 10.2. CONTRASTING THE INMON AND KIMBALL APPROACHES
    3. 10.3. QUANTITY IMPLICATIONS
    4. 10.4. USABILITY IMPLICATIONS
    5. 10.5. HISTORICAL DATA
    6. 10.6. SUMMARY
    7. 10.7. NOTES
  14. 11. Layered View of Information
    1. 11.1. INFORMATION LAYERS
    2. 11.2. ARE THEY REAL?
    3. 11.3. TURNING THE LAYERS INTO AN ARCHITECTURE
    4. 11.4. THE USER INTERFACE
    5. 11.5. SELLING THE ARCHITECTURE
  15. 12. Master Data Management
    1. 12.1. PUBLISH AND SUBSCRIBE
    2. 12.2. ABOUT TIME
    3. 12.3. GRANULARITY, TERMINOLOGY, AND HIERARCHIES
    4. 12.4. RULE 1: CONSISTENT TERMINOLOGY
    5. 12.5. RULE 2: EVERYONE OWNS THE HIERARCHIES
    6. 12.6. RULE 3: CONSISTENT GRANULARITY
    7. 12.7. RECONCILING INCONSISTENCIES
    8. 12.8. SLOWLY CHANGING DIMENSIONS
    9. 12.9. CUSTOMER DATA INTEGRATION
    10. 12.10. EXTENDING THE METADATA MODEL
    11. 12.11. TECHNOLOGY
  16. 13. Information and Data Quality
    1. 13.1. SPREADSHEETS
    2. 13.2. REFERENCING
    3. 13.3. FIT FOR PURPOSE
    4. 13.4. MEASURING STRUCTURED DATA QUALITY
    5. 13.5. A SCORECARD
    6. 13.6. METADATA QUALITY
    7. 13.7. EXTENDED METADATA MODEL
    8. 13.8. NOTES
  17. 14. Security
    1. 14.1. CRYPTOGRAPHY
    2. 14.2. PUBLIC KEY CRYPTOGRAPHY
    3. 14.3. APPLYING PKI
    4. 14.4. PREDICTING THE UNPREDICTABLE
    5. 14.5. PROTECTING AN INDIVIDUAL'S RIGHT TO PRIVACY
    6. 14.6. SECURING THE CONTENT VERSUS SECURING THE REFERENCE
  18. 15. Opening Up to the Crowd
    1. 15.1. A TAXONOMY FOR THE FUTURE
    2. 15.2. POPULATING THE STAKEHOLDER ATTRIBUTES
    3. 15.3. REDUCING E-MAIL TRAFFIC WITHIN PROJECTS
    4. 15.4. MANAGING CUSTOMER E-MAIL
    5. 15.5. GENERAL E-MAIL
    6. 15.6. PREPARING FOR THE UNKNOWN
    7. 15.7. THIRD-PARTY DATA CHARTERS
    8. 15.8. INFORMATION IS DYNAMIC
    9. 15.9. POWER OF THE CROWD CAN IMPROVE YOUR DATA QUALITY
    10. 15.10. NOTE
  19. 16. Building Incremental Knowledge
    1. 16.1. BAYESIAN PROBABILITIES
    2. 16.2. INFORMATION FROM PROCESSES
    3. 16.3. THE MIT BEER GAME
    4. 16.4. HYPOTHESIS TESTING AND CONFIDENCE LEVELS
    5. 16.5. BUSINESS ACTIVITY MONITORING
    6. 16.6. NOTE
  20. 17. Enterprise Information Architecture
    1. 17.1. WEB SITE INFORMATION ARCHITECTURE
    2. 17.2. EXTENDING THE INFORMATION ARCHITECTURE
    3. 17.3. BUSINESS CONTEXT
    4. 17.4. USERS
    5. 17.5. CONTENT
    6. 17.6. TOP-DOWN/BOTTOM-UP
    7. 17.7. PRESENTATION FORMAT
    8. 17.8. PROJECT RESOURCING
    9. 17.9. INFORMATION TO SUPPORT DECISION MAKING
    10. 17.10. NOTES
  21. Looking to the Future
  22. About the Author