Data Modeling Made Simple: A Practical Guide for Business and...

Features

• Cover Type: Paperback with 144 pages
• Published by: Technics Publications, LLC October 17, 2005
• Written in: English
• ISBN 10 Number: 0977140008
• ISBN 13 Number: 978-0977140008
• Book Dimensions: 9.8 x 6.9 x 0.4 inches
• Weighs: 10.4 ounces

Product Description
Ever have a terrible data day? If you're a business user, architect, analyst, designer or developer, then you've probably had some terrible data days. It comes with the territory. Overcoming these problems is much easier if you have an in-depth understanding of the actual data. That's where a data model comes in handy. It's a diagram that uses text and symbols to represent groupings of data, giving you a clear picture of your business and application environment. Data Modeling Made Simple provides the tools you need to read, create and validate models of your business and applications.



This book contains everything about modeling you need to know but were too afraid to ask, such as:

- What are the traditional and nontraditional uses of a data model?

- How do subject area, logical, and physical data models differ?

- When do I build a BSAM, ASAM, or CSAM?

- What is the easiest way to apply normalization?

- Where can I best leverage abstraction?

- How do I decide whether to use denormalization or dimensionality?

- What are primary, foreign, alternate, virtual, and surrogate keys?

- What is the best approach to building the models?

- How can I use the Scorecard system to validate a data model?



Plus over thirty exercises to reinforce concepts and sharpen your skills!



Reviews:

"What a great book—and a fun read too! Steve has captured the essence of data modeling and made it simple. For those who are not data modelers but need to work with them, this book is an great primer. For those who model data occasionally but not routinely, it is an invaluable quick reference. And for those of us who are experienced (and incorrigible) data modelers, Data Modeling Made Simple is a terrific reminder that we really can keep it simple!"

David Wells, Director of Education, Data Warehousing Institute



"An great introduction from someone who knows his subject and knows how to teach it"

Graeme Simsion, University of Melbourne



“Data Modeling Made Simple is a must read for all professionals new to data modeling as well as those that want to ‘speak the language’ and understand the concepts. Steve writes as though he is right there with you, walking you through the terminology, explaining the symbols, and telling you what you should consider doing before, during and after you have modeled your data.”

Robert S. Seiner, President, KIK Consulting & Educational Services, LLC and Publisher of The Data Administration Newsletter, tdan.com



“Data Modeling Made Simple is an great training guide for anyone entering the data modeling field. Steve Hoberman takes the fundamental concepts of data modeling and presents them in an easy to understand and entertaining manner that I’m sure you will find valuable.”

David Marco, President, EWSolutions



“How does one who is not a formally trained ‘data modeler’ understand the basics of data modeling? Steve Hoberman has created an informative, fun, easy to follow, and practical book sharing data modeling concepts which are essential for any professional involved in information technology. Mr. Hoberman clearly answers key questions behind the what, why and how of data modeling and reinforces the explanations with appropriate examples, analogies and exercises.”

Len Silverston, Best-Selling Author of The Data Model Resource Book, Volumes 1 and 2





From the Author
My challenge was to write a book in under 200 pages which costs under $20 that contains everything you need to know about data modeling. I have been teaching data modeling since 1992 and in my training I take a very practical approach, focusing on what can be immediately applied back in the office. I took this same approach with Data Modeling Made Simple. I start off with an example we can all relate to and carry this example throughout the entire book. I added exercises to the text to make the book as interactive as paper can be. I steered clear of mathematical set theory and advanced dimensional concepts and included what a business or information technology human being actually requirements to know to understand, design, and implement high quality data models. If you would like to chat about anything in the book, feel free to contact me at me@stevehoberman.com.

Reader Reviews
Having its foundations in mathematical logic, especially first-order predicate calculus and relational database theory, data modeling has become a very practical tool for many businesses, and in fact the scale of its use has been vast. It would be difficult to find a large enterprise company that is not currently doing some kind of data modeling, and to support its popularity many tools and books have appeared in the last ten years that discuss data modeling in various levels of detail. This book is one of those, and gives an introduction to data modeling at a level that will be accessible to most everyone. It does not go into the intricacies of mathematical logic or database theory but instead endeavors to give a quick, practical overview of the subject. Readers who need more in-depth discussion can consult the many different books that currently exist on data modeling. Loosely speaking, a database can be considered to be an organized collection of data items that are interconnected logically. Data modeling gives a high-level abstract `model' or `representation' of the database. More often than not, data modeling is thought of as `relational' data modeling, due to its dependence on the `relational data model' that had its origins in computer science research in the 1970's. However, over the past twenty years extensions to the relational data model have appeared, such as deductive data model, and object-oriented data models. All of these approaches have had various degrees of success. The author of this book defines a data model as essentially a diagram that contains text and symbols to represent collections of data. The goal is to allow a user to understand more easily how the different data items are related to each other. It is immediate from this definition that a data model will not be unique: there may be many different data models that could be constructed for a given collection of data. Throughout the book, the author uses the example of a business card to illustrate the basic principles of data modeling and why it is a useful activity. But the author is careful to note that data modeling should not be viewed as useful only in the database context. In particular, data modeling can be used to reverse engineer an application database, perform impact analysis, assist in the understanding of business processes, and for training new personnel. All of the data models representing these applications have common features that make them useful, which the author calls `communication', `formalization', `scope', and `focus.' Data models are used to better communicate certain processes or interactions between various objects. They encapsulate the information in a way that makes complicated processes or data easier to understand. Formalization allows a unique and precise interpretation of every symbol and term in the model. Even though for a given set of processes the data model will not be unique, once the data model is created its syntax and its interpretation are assumed to be immutable. This requires careful attention to the definitions in the data model so as to suppress alternative or multiple interpretations. The scope of a data model refers to the description of data according to the factors of time, quantity, and function. The scope assists in the defining these factors in the model in order to check the accuracy of the model and to complete it. In any type of data modeling effort, one employs a certain level of "coarse-graining" of the data in order to make the model more compact and manageable. The degree of granularity is referred to as the `focus' of the data model, and allows one to communicate the same content at different levels of detail. The author points to three levels of granularity that are typically used by the data modeling community. One of these levels is the `subject area model', which contains only the `basic' and `critical' concepts for a given scope. Another is the `logical data model', which contains the representation of the rules that lie behind the functioning of a particular object. The third is the `physical data model' which is a representation of the rules behind this functioning, but that is optimized for a specific context. All of these levels are discussed in the book, using the business card example as an illustration. The reader not used to a fairly high level of abstraction may find the reading a bit opaque at times. It is apparent that the author realizes this, for he inserts exercises at various places in the book in order that readers are given the opportunity to come up with their own approaches to a particular topic in data modeling. Diagrams are of course essential to realizing a data model, for they give a pictorial representation of how the data are interrelated. The economy of thought obtained by using pictures or diagrams is invaluable in data modeling (and most other fields of modeling also). A complete novice in database theory, or even one who has no background in it at all will still be able to obtain a good understanding of the concepts of data modeling when reading the book. Some of these concepts have formidable names to the beginner, such as `abstraction', `normalization', or `normal form.' But the concrete example of the business card makes these concepts more palatable for the beginning reader. The book will not of course make one an expert in data modeling, but it does contain enough helpful insights to allow one to begin creating actual data models that could be used in many different business environments. Data modeling should also be viewed in the context of more exotic approaches to the intelligent management of information. A few of these approaches include ontological engineering, semantic technology, and knowledge management. Like data modeling, they attempt to give the human user a representation of complicated data relationships. But they also go beyond it in attempting to present information that is more easily manipulated or understood by (non-human) intelligent machines. Comment | | (Report this)