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

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

Section I  Data Modeling and the Need for Validation
Chapter 1 Data Modeling Primer
Chapter 2 Importance of Data Model Quality
Chapter 3 Data Model Scorecard Overview

Section II  Data Model Scorecard Categories
Chapter 4 Category One: Correctness How well does the model capture the requirements?
Chapter 5 Category Two: Completeness How complete is the model?
Chapter 6 Category Three: Scheme How well does the model match its scheme?
Chapter 7 Category Four: Structure How structurally sound is the model?
Chapter 8 Category Five: Abstraction How well does the model leverage generic structures?
Chapter 9 Category Six: Standards How well does the model follow naming standards?
Chapter 10 Category Seven: Readability How well has the model been arranged for readability?
Chapter 11 Category Eight: Definitions How good are the definitions?
Chapter 12 Category Nine: Consistency How consistent is the model with the enterprise?
Chapter 13 Category Ten: Data How well does the metadata match the data?

Section III Validating Data Models With the Scorecard
Chapter 14 Preparing for the Model Review
Chapter 15 During the Model Review
Chapter 16 Data Model Scorecard Case Study: Consumer Interaction