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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).
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).
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 202 |
| ISBN-13: | 9781634620826 |
| ISBN-10: | 1634620828 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Hoberman, Steve |
| Hersteller: | Technics Publications |
| Maße: | 235 x 191 x 12 mm |
| Von/Mit: | Steve Hoberman |
| Erscheinungsdatum: | 15.09.2015 |
| Gewicht: | 0,388 kg |
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 202 |
| ISBN-13: | 9781634620826 |
| ISBN-10: | 1634620828 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Hoberman, Steve |
| Hersteller: | Technics Publications |
| Maße: | 235 x 191 x 12 mm |
| Von/Mit: | Steve Hoberman |
| Erscheinungsdatum: | 15.09.2015 |
| Gewicht: | 0,388 kg |
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