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Data Model Scorecard: Applying the Industry Standard on Data Model Quality, by Steve Hoberman
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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
- Sales Rank: #918880 in Books
- Published on: 2015-09-04
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .46" w x 7.50" l, .0 pounds
- Binding: Paperback
- 210 pages
From the Inside Flap
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).
About the Author
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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Thank you for making this widely available!
By M M.
Steve Hoberman is a recognized authority on data modeling; his educational presentations have been enthusiastically received for many years at industry conferences and his previous books are widely quoted. The Data Model Scorecard has been made available to the data management industry free of charge, thanks to Steve’s generosity. It provides a sound, rational framework for evaluating the quality and correctness of data models.
I employed the Data Model Scorecard as an auditable foundation for a data modeling standards compliance program at a Federal agency, applicable across the Requirements (Logical) and Design (Physical) phases of the Systems Development Life Cycle. Compliance reports produced by applying the ten Scorecard categories were accepted by project teams – (always challenging to inform a project team that modifications to their design are necessary) – and comments were much more palatable applying the Scorecard approach. The second round of Scorecard results, after modifications were applied, were a key factor in go / no-go decisions by the Architectural Review Board.
The book is written in Steve’s trademark clear style, with minimal technical jargon. He reviews fundamental data modeling principles using commonly encountered business examples, provides an overview of the Scorecard categories and scoring weights, and thoroughly explicates all of the factors involved in deriving the scores in each category. All of the guesswork is removed, so the evaluator / data architect can use consistent practice statements and terminology in expressing the results, for instance, “Make sure all candidate keys are unique, stable, and mandatory (not null).” At the end of each chapter there is a Summary of checks to be applied; this assists the evaluator to ensure that all best practice checks have been applied to the analysis of the model.
The Data Model Scorecard also provides preparation steps and a documentation list to prepare for the model review. I especially appreciated the inclusion of the Requirements document(s), since project teams sometimes forget to maintain parallelism between what the application is supposed to DO versus the supporting data that it must HAVE. This book is a Must Read for anyone who creates logical or physical data models, as it will definitely improve the quality and consistency of your work. And for the Data Management organization and model reviewers, it is a highly valuable framework for fostering higher quality data models and improving communications with project teams and the lines of business.
1 of 1 people found the following review helpful.
A Simplified Data Model Scorecard
By DeJaan LaFleur
I've just completed reading the Data Model Scorecard, by Steve Hoberman, and I now know how to validate the data models within my organization. This is the first and most comprehensive measure of data model quality I have seen. I find myself thinking how easy this will be to implement this data model in my everyday business life.
If you are interested in learning about data sampling and using it effectively, this is a great place to start. Each chapter consist of templates and formula’s to get that visual effect and adhoc reporting to give you a better understanding of the Data Model Scorecard. The author gave concise information, how this can be done and he does not use lots of technical jargons that goes over the reader head.
The book is methodically divided into three sections; Data Modeling and the Need for Validation, Data Model Scorecard Categories, Validating Data Models With the Scorecard. This gives it a more cohesive flow. What you will learn is a lot of useful information that will assist you in your business.
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