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

Hoberman, Steve

 
9781634620826: Data Model Scorecard: Applying the Industry Standard on Data Model Quality
Softcover
ISBN 10: 1634620828 ISBN 13: 9781634620826
Verlag: Technics Publications, 2015

Inhaltsangabe

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).

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style, and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. One of Steve's frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conference, recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, and highest rated tutorial presenter at Enterprise Data World 2014 and Enterprise Data World 2015.

Von der hinteren Coverseite

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).

Aus dem Klappentext

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 its essential to get the data model right. But how do you determine right? Thats 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 organizations data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my clients 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).

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Verlag
Technics Publications
Erscheinungsdatum
2015
Sprache
Englisch
ISBN 10 
1634620828
ISBN 13 
9781634620826
Einband
Taschenbuch
Anzahl der Seiten
202
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Deutschland