You are previewing Data Modeling Made Simple: A Practical Guide for Business and IT Professionals.
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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals

Book Description

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:

  • 1. Know when a data model is needed and which type of data model is most effective for each situation

  • 2. Read a data model of any size and complexity with the same confidence as reading a book

  • 3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model

  • 4. Apply techniques to turn a logical data model into an efficient physical design

  • 5. Leverage several templates to make requirements gathering more efficient and accurate

  • 6. Explain all ten categories of the Data Model Scorecard

  • 7. Learn strategies to improve your working relationships with others

  • 8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables

  • 9. Learn basic UML concepts

  • 10. Put data modeling in context with XML, metadata, and agile development

Table of Contents

  1. Acknowledgements
  2. Foreword
  3. Read me first!
  4. SECTION I Data Modeling Introduction
  5. CHAPTER 1 What is a data model?
    1. Wayfinding Explained
    2. Data Model Explained
    3. Fun with Ice Cream
    4. Fun with Business Cards
    5. EXERCISE 1: Educating Your Neighbor
  6. CHAPTER 2 Why do we need a data model?
    1. Communication
      1. Communicating during the modeling process
      2. Communicating after the modeling process
    2. Precision
    3. Data Model Uses
    4. EXERCISE 2: Converting the Non-Believer
  7. CHAPTER 3 What camera settings also apply to a data model?
    1. The Data Model and the Camera
    2. Scope
    3. Abstraction
    4. Time
    5. Function
    6. Format
    7. EXERCISE 3: Choosing the Right Setting
  8. SECTION II Data Model Components
  9. CHAPTER 4 What are entities?
    1. Entity Explained
    2. Entity Types
    3. EXERCISE 4: Defining Concepts
  10. CHAPTER 5 What are attributes?
    1. Attribute Explained
    2. Attribute Types
    3. Domain Explained
    4. EXERCISE 5: Assigning Domains
  11. CHAPTER 6 What are relationships?
    1. Relationship Explained
    2. Relationship Types
    3. Cardinality Explained
    4. Recursion Explained
    5. Subtyping Explained
    6. EXERCISE 6: Reading a Model
  12. CHAPTER 7 What are keys?
    1. Candidate Key (Primary and Alternate) Explained
    2. Surrogate Key Explained
    3. Foreign Key Explained
    4. Secondary Key Explained
    5. EXERCISE 7: Clarifying Customer Id
  13. SECTION III Conceptual, Logical, and Physical Data Models
  14. CHAPTER 8 What are conceptual data models?
    1. Concept Explained
    2. Conceptual Data Model Explained
    3. Relational and Dimensional Conceptual Data Models
      1. Relational CDM Example
      2. Dimensional CDM Example
    4. EXERCISE 8: Building a CDM
  15. CHAPTER 9 What are logical data models?
    1. Logical Data Model Explained
    2. EXERCISE 9: Modifying a Logical Data Model
  16. CHAPTER 10 What are physical data models?
    1. Physical Data Model Explained
    2. EXERCISE 10: Getting Physical with Subtypes
  17. SECTION IV Data Model Quality
  18. CHAPTER 11 Which templates can help with capturing requirements?
    1. In-The-Know Template
    2. Concept List
    3. Family Tree
    4. EXERCISE 11: Building the Template
  19. CHAPTER 12 What is the Data Model Scorecard®?
    1. Data Model Scorecard Explained
    2. Scorecard Template
    3. Scorecard Summary
    4. Scorecard Example
    5. EXERCISE 12: Determining the Most Challenging Scorecard Category
  20. CHAPTER 13 How can we work effectively with others?
    1. Recognizing People Issues
    2. Setting Expectations
      1. Understanding context
      2. Identifying the stakeholders
      3. Asking key questions
      4. Packaging it up
    3. Staying on Track
      1. Following good practices
      2. Dealing with problems – and problem people
    4. Achieving Closure
      1. Writing reports
      2. Following up
      3. continuous improvement
    5. EXERCISE 13: Keeping a Diary
  21. SECTION V Essential Topics Beyond Data Modeling
  22. CHAPTER 14 What is unstructured data?
    1. Unstructured Data Explained
    2. Data Modeling and Abstraction
    3. Immutable Unstructured Data
    4. Taxonomies Explained
      1. Processing raw text
      2. Capturing taxonomy properties
      3. Maintaining taxonomies over time
      4. Tracing taxonomies
    5. Ontologies Explained
    6. EXERCISE 14: Looking for a Taxonomy
  23. CHAPTER 15 What is UML?
    1. UML Explained
    2. Modeling Inputs
    3. Modeling Outputs
    4. Class Model Explained
      1. Class
      2. Association
      3. Generalization
    5. Use Case Model Explained
      1. Actor
      2. Use case
    6. EXERCISE 15: Creating a Use Case
    7. References
  24. CHAPTER 16 What are the Top 5 most frequently asked modeling questions?
    1. 1. What is metadata?
    2. 2. How do you quantify the value of the logical data model?
    3. 3. Where does XML fit?
    4. 4. Where does agile fit?
    5. 5. How do I keep my modeling skills sharp?
  25. Suggested Reading
    1. Books
    2. Web Sites
  26. Answers to Exercises
    1. EXERCISE 1: Educating Your Neighbor
    2. EXERCISE 3: Choosing the Right Setting
    3. EXERCISE 5: Assigning Domains
      1. Email Address
      2. Gross Sales Amount
      3. Country Code
    4. EXERCISE 6: Reading a Model
    5. EXERCISE 7: Clarifying Customer Id
      1. Document uniqueness properties
      2. Document the characteristics of the identifier
      3. Define the customer
    6. EXERCISE 9: Modifying a Logical Data Model
      1. Option 1
      2. Option 2 (a bit more abstract)
    7. EXERCISE 10: Getting Physical with Subtypes
      1. Identity
      2. Rolling up
      3. Rolling down
    8. EXERCISE 11: Building the Template
    9. EXERCISE 12: Determining the Most Challenging Scorecard Category
  27. Glossary
  28. Index