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Data Model Scorecard: Applying the Industry Standard on Data Model Quality

Book Description

Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it's essential to get the data model right. But how do you determine right? That's where the Data Model Scorecard comes in.

The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization's data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client's data models - I will show you how to apply the Scorecard in this book.

This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections: In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3. In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category:
  • Chapter 4: Correctness
  • Chapter 5: Completeness
  • Chapter 6: Scheme
  • Chapter 7: Structure
  • Chapter 8: Abstraction
  • Chapter 9: Standards
  • Chapter 10: Readability
  • Chapter 11: Definitions
  • Chapter 12: Consistency
  • Chapter 13: Data
In Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).

Table of Contents

  1. Introduction
  2. Section I Data Modeling and the Need for Validation
  3. Chapter 1 Data Modeling Primer
    1. Entities
    2. Attributes
    3. Domains
    4. Relationships
    5. Keys
      1. Candidate Key (Primary and Alternate)
      2. Surrogate Key
      3. Foreign Key
      4. Secondary Key
    6. Subtypes
  4. Chapter 2 Importance of Data Model Quality
    1. Precision
    2. Leverage
    3. Data Quality
  5. Chapter 3 Data Model Scorecard Overview
    1. Scorecard Characteristics
    2. Scorecard Template
    3. DMM Context
      1. DMM and Data Modeling
    4. DMBOK Context
  6. Section II Data Model Scorecard Categories
  7. Chapter 4 Category One: Correctness How well does the model capture the requirements?
    1. Category Expectations
  8. Chapter 5 Category Two: Completeness How complete is the model?
    1. Category Expectations
  9. Chapter 6 Category Three: Scheme How well does the model match its scheme?
    1. Conceptual Data Model Adherence
      1. Relational Adherence
        1. Dimensional Adherence
      2. Logical Data Model Adherence
        1. Relational Adherence
        2. Dimensional Adherence
      3. Physical Data Model Adherence
        1. Relational Adherence
        2. Dimensional Adherence
        3. NoSQL Database Adherence
  10. Chapter 7 Category Four: Structure How structurally sound is the model?
    1. Category Expectations
      1. Model is Consistent
      2. Model has Integrity
      3. Model Follows Core Principles
  11. Chapter 8 Category Five: Abstraction How well does the model leverage generic structures?
    1. Category Expectations
      1. Model is Extensible
      2. Model is Useable
  12. Chapter 9 Category Six: Standards How well does the model follow naming standards?
    1. Category Expectations
      1. Model is Well-Structured
      2. Model Uses the Correct Terms
      3. Model has Consistent Style
  13. Chapter 10 Category Seven: Readability How well has the model been arranged for readability?
    1. Category Expectations
      1. Model is Readable
      2. Entity Layout Acceptable
      3. Attribute Sequence Acceptable
      4. Relationship Layout Acceptable
  14. Chapter 11 Category Eight: Definitions How good are the definitions?
    1. Category Expectations
      1. Definitions are Clear
      2. Definitions are Complete
      3. Definitions are Correct
  15. Chapter 12 Category Nine: Consistency How consistent is the model with the enterprise?
    1. Category Expectations
  16. Chapter 13 Category Ten: Data How well does the metadata match the data?
    1. Category Expectations
  17. Section III Validating Data Models With the Scorecard
  18. Chapter 14 Preparing for the Model Review
    1. Required Documentation
    2. Extra Credit Documentation
    3. Review Structure
    4. Divide the Model up into Realistic Chunks for Review
    5. Deciding who Participates in the Review
    6. Seating Pattern
  19. Chapter 15 During the Model Review
    1. Choose a Mile Deep Over a Mile Wide
    2. Set the Stage
    3. Build a Support Group
    4. Average the Scores
    5. Start With a CDM
    6. Know When to Throw in the Towel
    7. Keep it Fun
  20. Chapter 16 Data Model Scorecard Case Study: Consumer Interaction
    1. 1. Correctness
    2. 2. Completeness
    3. 3. Scheme
    4. 4. Structure
    5. 5. Abstraction
    6. 6. Standards
    7. 7. Readability
    8. 8. Definitions
    9. 9. Consistency
    10. 10. Data
    11. Completed Scorecard
  21. Index