Data Modeling for Information Professionals Review

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(More customer reviews)I found this book to be a refreshing approach to teaching data modeling. With the current confusion between "object models" and "data models", Bob does a good jobe of presenting the concepts fundamental to both. His sequence is unusual (discussing occurrences before classes), but I think this is useful. I definitely recommend it to anyone trying to learn the field.

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8045k-6 "A powerful, yet easy-to-use resource for training people in data modeling principles. I highly recommend it for anyone who needs to develop data modeling competence." Clive Finkelstein, Information Engineering Services, www.ies.aust.com/~ieinfo. "An outstanding vehicle for learning the mysteries of data/object modeling." David Hay, President, Essential Strategies, Inc., www.essentialstrategies.com, author of Data Model Patterns, The most fun you can have learning data modeling! No matter what role you play in managing information, you need an in-depth understanding of how to structure data. Data Modeling for Information Professionals gives you what you need - painlessly! Based on an interactive course that's been earning raves for years, it's the informal, friendly, real-world introduction to data modeling.*Discover what data models are, what makes them successful, and what makes them fail. *Walk through every component of an enterprise data model. *Understand domains, predicates, entities, classes, relationships, attributes, and more. *Learn from enterprise case studies and extensive nontrivial examples.*Great for data administrators, analysts, SMEs, DBAs, and project managers!Comprehensive, insightful, and entertaining, Data Modeling for Information Professionals is the easy way to learn the data modeling techniques you can't afford not to know! REPOSITORY ON CD-ROM The many illustrations in this book expand and link when you launch the free data model using the included SILVERRUN CASE tool. Export this royalty-free model to jump start your own work and to practice using your own CASE tool. Or build your model u sing SILVERRUN, a leading tool for multiplatform, enterprise-capable business modeling.

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Data Modeling Theory and Practice Review

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(More customer reviews)To me, this book's value is a bit like children being warned not to accept lollies from strangers; it's a pity we even have to give such warnings, but it's absolutely essential we do. I wish to congratulate Simsion for bravely tackling a subject of much heated controversy, and in a manner that obviously reflects both a solid practitioner's hard-won lessons, but that is supported by rigorous academic research.
So what's this important message? Simply that data modelling is a creative exercise, where multiple "solutions" may be generated, each with relative merits. The importance lies in practitioners consciously and deliberately generating alternatives. Without this open-minded view, I have personally witnessed heated debates where one modeller defends his/her model because they know it can be made to work, and therefore assumes anything different must be "wrong". But even more significantly, modellers may stop looking as soon as one "workable" model is tabled, and hence miss out on alternatives that may prove beneficial in a given business context.
And why is it even controversial? Apparently, some academics teach data modelling that way. Maybe because it's easier for them to have one "correct" answer to a problem so marking assignments is easier? Or maybe that was what they were taught, and any students who pass through their ranks and end up teaching without encountering real-world modelling may perpetuate?
One warning, though. This book is not the first text to be read by those interested in data modelling. I would recommend Simsion & Witt's "Data Modelling Essentials for such people, followed by one of many excellent books on "patterns". David Hay got the patterns topic going in the data modelling community, and Len Silverston's two volume series has taken it much further. And the object-oriented community also has contributions to make on patterns.
A minor criticism - Simsion largely dismisses the use of the Unified Modeling Language's class modelling notation, in part arguing that "Class diagrams are intended to represent data structures which might be directly implemented using an object-oriented database" and goes on to correctly note the struggle of such databases to gain significant database market share that their vendors initially might have predicted. I would simply comment that there is a difference between using a subset of the class modelling syntax to represent what is truly a data model, as compared to using class modelling notation to represent classes which, in some cases, may never have "persistence" i.e. may never have their data values stored in a database of any kind. And even if class diagram notation is used (some might say misused?) just to represent a data model, I have seen this approach used quite effectively. So on this point, it looks like Simsion and I have slightly different views. But at the very heart of his book, he encourages open debate on alternative views, with the understanding that all views may have something to contribute.
So let's thank Simsion for offering his views, and encouraging others to offer theirs. Well done, it's a great reference book (probably not easy reading for those not exposed to research styles - but don't let that put you off), and one that hopefully bridges the gap between academics and practitioners, and gives the practitioners "permission" to be creative as most know is the way to generate alternative solutions for consideration.

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DATA MODELING THEORY AND PRACTICE is for practitioners and academics who have learned the conventions and rules of data modeling and are looking for a deeper understanding of the discipline.The coverage of theory includes a detailed review of the extensive literature on data modeling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice.The practice component incorporates the largest-ever study of data modeling practitioners, involving over 450 participants in interviews, surveys and data modeling tasks.The results challenge many longstanding held assumptions about data modeling and will be of interest to academics and practitioners alike.Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his Data Modeling Essentials a classic in the field.He begins with a question about the nature of data modeling (design or description), and uses it to illuminate such issues as the definition of data modeling, its philosophical underpinnings, inputs and deliverables, the necessary behaviors and skills, the role ofcreativity, product diversity, quality measures, personal styles, and the differences between experts and novices.Data Modeling Theory and Practice is essential reading for anyone involved in data modeling practice, research, or teaching.

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Data Modeling Made Simple: A Practical Guide for Business & Information Technology Professionals Review

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(More customer reviews)For over a year I'm looking for a good book to help business analysts to understand data models drawn by others and to train them in creating basic data models needed to cover business needs. I found a lot of good books but all too heavy, too many pages, too detailed and very nice if you want to become a real heavy duty data-guru. There is absolutely nothing wrong with data gurus, every organization needs a few of those, but it needs quite a few more of the 'casual' modellers. This book .. not too big.. a good read.. and even better reread.. It contains exactly everything that is needed for those modellers.
So, if you're a Business Analyst, Information Manager and need a good understanding of Data Modelling, even occasionally need to make one yourself, without having to spend years in training: buy this book..


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Ever have a bad data day? If you're a business user, architect, analyst, designer or developer, then you've probably had some bad 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 30 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 excellent 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 excellent 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 andPublisher of The Data Administration Newsletter, tdan.com

"Data Modeling Made Simple is an excellent 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

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Data Modeling: A Beginner's Guide Review

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(More customer reviews)This book is perfect for beginner who wants to learn about data modeling in RDBMS. This book has good TOC and very easy to find what you want to read.
Very useful tips like "Ask the Experts" section. Very easy to understand when a book comes with step-by-step section. Good examples to show tables and data relationships.
I am new to data modeling in RDBMS but after reading this, I can easily create a small test database with some test data and check out the examples from this book. At the end ...., I say hey..it works!!!


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Essential Skills--Made Easy!
Learn how to create data models that allow complex data to be analyzed, manipulated, extracted, and reported upon accurately. Data Modeling: A Beginner's Guide teaches you techniques for gathering business requirements and using them to produce conceptual, logical, and physical database designs. You'll get details on Unified Modeling Language (UML), normalization, incorporating business rules, handling temporal data, and analytical database design. The methods presented in this fast-paced tutorial are applicable to any database management system, regardless of vendor.
Designed for Easy Learning
Key Skills & Concepts--Chapter-opening lists of specific skills covered in the chapter
Ask the expert--Q&A sections filled with bonus information and helpful tips
Try This--Hands-on exercises that show you how to apply your skills
Notes--Extra information related to the topic being covered
Self Tests--Chapter-ending quizzes to test your knowledge

Andy Oppel has taught database technology for the University of California Extension for more than 25 years. He is the author of Databases Demystified, SQL Demystified, and Databases: A Beginner's Guide, and the co-author of SQL: A Beginner's Guide, Third Edition, and SQL: The Complete Reference, Third Edition.

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Database Modeling and Design, Fifth Edition: Logical Design (The Morgan Kaufmann Series in Data Management Systems) Review

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(More customer reviews)I have to underscore a statement in the preface with which I am in total agreement: "This book can . . . be used by the advanced undergraduate or beginning graduate student to supplement a course textbook in introductory database management . . ." In fact, the authors point out that the fifth edition of this book has been split into a second work, PHYSICAL DATABASE DESIGN: THE DATABASE PROFESSIONAL'S GUIDE, 1st edition (a title that is not in print at the time of this review). As a book about "Logical Design," explaining WHY (not HOW) is the strength of the work. This is not a book one would primarily rely upon for developing an application. But it is an excellent work for giving solid background in the underpinnings of database design. The HOW (as the author's stated) is spawned off in another work, in this case.
I'm a non-professional IT person who has designed and implemented dozens of LAMP (Linux-Apache-MySQL-php) applications, and database design activities dating back to the 1980's, primarily in the honorable environment of the various MS-DOS flavors of the day. All of this with zero formal training (which isn't the same as NO training). With that background in mind, after going through this book I confirmed that I had picked up a lot of bad habits and developed a lot of good practices. My worst habit? A preference for flat databases over relational ones (see Chapter 8 - Object-Relational Design). My hardest earned (and confirmed by this book) good practice? The absolute importance of "requirements analysis," or, rigorous interviewing of the end user population to "determine exactly what the database is to be used for . . ." (see Chapter 4 - Requirements Analysis and Conceptual Data Modeling). In both of these cases this book was excellent in explaining WHY.
Since I work almost exclusively with web based databases now, I found Chapter 9 (XML and Web Databases) to contain one of the most concise and elegant explanations of XML I've ever read.
One annoying, but not fatal, flaw of this work: the quite serviceable index appears before the appendices, instead of the almost universal location of the last section of the book. I.e., when I turned to the back of the book to use the index, it wasn't where I expected. After a little fumbling, I did find it, but this is, in my opinion, a logical design flaw of the book.
I wouldn't hesitate to recommend this book as a purchase to an undergraduate MIS student even if it isn't a class requirement. This is a good book to develop a solid conceptual foundation. Also, since this book is primarily conceptual in its focus, it should have a shelf life much longer than works which focus on specific hardware and software which evolve continuously. Most of the concepts described in this book would have been relevant to me in my MS-DOS database days.

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Database systems and database design technology have undergone significant evolution in recent years. The relational data model and relational database systems dominate business applications; in turn, they are extended by other technologies like data warehousing, OLAP, and data mining. How do you model and design your database application in consideration of new technology or new business needs? In the extensively revised fifth edition, you'll get clear explanations, lots of terrific examples and an illustrative case, and the really practical advice you have come to count on--with design rules that are applicable to any SQL-based system. But you'll also get plenty to help you grow from a new database designer to an experienced designer developing industrial-sized systems.
In-depth detail and plenty of real-world, practical examples throughout

Loaded with design rules and illustrative case studies that are applicable to any SQL, UML, or XML-based system



Immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data.

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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition Review

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(More customer reviews)I think very highly of Data Modeling Made Simple (the first edition), so when this second edition came out I had great expectations - which were not only met but also exceeded. Although this second edition is more than twice the number of pages as the first edition, it is still an easy read.
Here are my favorite things about this book:
1.Clearly delivers on its ten objectives. Read the back cover and you will understand the key takeaways you will get after reading the book. After I read the book, I went back over each of these objectives and I was able to check each of these off as accomplished. Everything from justifying the model to building data models to assessing data models was knowledge I gleaned from the book. If you are interested in just one or a subset of these ten objectives, read the Read Me First section and it will reference the sections and chapters you need to read to meet your specific objective.
2.More examples more thoroughly presented. The first edition took a business card example from beginning to end. This edition further expands the business card example and adds several other examples including an ice cream example and many real world examples. The author uses spreadsheets to illustrate many modeling examples, and I too have found spreadsheets to be a very effective way to communicate data and business rules.
3.Data Model Scorecard. The first edition touched on the Scorecard which is the author's technique to reviewing a data model. This second edition goes into detail including providing the template which I can use on my modeling assignments to review my models.
4.Treating a dimensional model as more than just a physical data model. Many texts treat the dimensional as only a physical data model yet there is a business level that this book illustrates at both the subject area and logical levels.
5.Getting other Greats for free. Bill Inmon, Graeme Simsion, and Michael Blaha have all written chapters in this book. I have already starting using Simsion's technique of a diary on my assignments and found it very useful.
My only area for improvement would be to expand the book with more modeling conventions such as ORM and IDEF1X. There is a chapter on UML though that I did find informative. I question however if adding these extra notations would detract from the book's simplicity.
Overall, an excellent read that I would recommend to every business or techie that works with data.


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Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:
Know when a data model is needed and which type of data model is most effective for each situation
Read a data model of any size and complexity with the same confidence as reading a book
Build a fully normalized relational data model, as well as an easily navigatable dimensional model
Apply techniques to turn a logical data model into an efficient physical design
Leverage several templates to make requirements gathering more efficient and accurate
Explain all ten categories of the Data Model Scorecard
Learn strategies to improve your working relationships with others
Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
Learn basic UML concepts
Put data modeling in context with XML, metadata, and agile development

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