Customer Reviews

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

5 star:		(9)
4 star:		(4)
3 star:		(1)
2 star:		(0)
1 star:		(4)

 

 

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..

This is one of my favorite data modeling books. Not because it provides the most detail into the differences betwen 4th and 5th Normal Form or because it explains in excruciating detail how to develop and Enterprise Data Model, but because it helps me to work with people who are not data architects.

I was recently working with the VP of BI for the company I work for and was telling him that we needed to develop a subject area model for our data warehouse and that we would use it as a sort of road map in building the data warehouse.

He asked a simple question, "What's a subject." I gave him some examples and he said, "Oh, you mean a table." Hmmm....not quite. More discussion ensued and I finally got the point across.

I realized after that incident that I had a hard time communicating exactly what my deliverables would be and why they are important. I grabbed Steve's book off my shelf and re-read it that weekend (it's an easy book to read).

As I looked at the list of things I planned to do in building a new data warehouse and pondered the help I would need from the business users in completing them, I saw more conversations in my future about what I do and why it's necessary. I promptly ordered 3 copies of the book for the business users I interact (including the aforementioned VP) with and 3 more for the junior data modelers on my team.

The book has been very helpful in bringing non-technical folks up to speed to help with a DW initiative. We are currently working on definitions and Steve's examples and guidelines in this area are helping us put together solid definitions and I am doing a lot less editing than I expected.

As someone who does data architecture every day, I sometimes forget why. I just know that I start with subject areas and work my down. I need definitions that are useful, but sometimes find it hard to explain to others what I mean by that. I have adopted Steve'w words and examples when communicating what I do and why I do it that way to others and the best part is that people are getting it.

I think this is one of the best practical data modeling books I own at any price. And on a value/$ scale, this tops the charts.

Nice work Steve!

Think what you want, but to me there definitely IS a difference between books about data modeling written by nonpractitioners and practitioners. Same subject, but sometimes poles apart in terms of quality and readability. Theoretical background is certainly essential for a good data modeler, however many books written by nonpractioners overcomplicate things and fail to bring their points across in an easy to understand language. Likewise there are books by practitioners that seriously lack quality. The book at hand combines the best of both worlds and is a pearl among the entry-level books about data modeling. Easy to read and understand, concise, and straight to the point with "just enough" theory. If you are a beginner in data modeling, this book will help you in getting started quickly and in the right direction. If you are an advanced (or even expert) data modeler, this book is a good reread to refresh your knowledge.

As the "new kid on the block" in my data architecture group, I attended my first DAMA conference in March. One of the sessions I sat in on was given by an IT manager for the Department of State in Washington. The speaker ended her talk with five things they did to make their project a success, and one of them was giving Data Modeling Made Simple to everyone in their department, even the business folks.

I made a note of this title and did not buy the book until recently, but I'm glad I did and wish I bought it sooner. I was able to read the book cover to cover during my lunch break in less than a week and picked up some great messages. Here are the techniques that make this book five stars:

1. The business card example - explaining data modeling with something so simple like a business card is a great method, and I should have bought this book back in March just for this example. A business card is chock full of data and is used to tie all of the data modeling concepts in the book together.

2. Normalization - this is a topic I used to struggle with before reading this book. Steve's simple steps in Chapter 8 are easy to follow and remember.

3. Data Model Scorecard - this is how Steve reviews a model. A template is provided that my company is using and so far the feedback is positive.

The one thing I think Steve should have included in the book is a comparison to other modeling notations. Steve uses information engineering (IE) notation and I would like to see a short side-by-side comparison with IDEFIX (which we use here).

Data Modeling Made Simple is easy to read and understand, and I think it is true that someone that does not have a technical background can also benefit from reading this book.

Data Modeling Made Simple comes in at a lean 132 pages that effectively provides clear understanding of high-level concepts involved in data modeling while holding off on the low-level details to be covered by other books.

As a developer who approaches a database as just a backend to my business application, I gained further respect for data modeling, even as a parallel process to object modeling. Most of the author's statements of purpose and benefits of the data model match the purpose and benefits of an object model. I believe that doing both only gains me more insight into the business which is of course invaluable.

On more than one occasion after the author suggests something I found myself thinking, "I hope he explains other situations where you would not want to do that!" And almost without fail two sentences or paragraphs later he explained the alternatives or downsides to the suggestion. To me this is a hallmark of any good book on technology and methods. In fact, just to be picky, I only recall one time he did not do this. After explaining that we might denormalize a database to make it more user friendly (among many other reasons he goes in to) he then states that we might want to use an integer as opposed to a 10-character code for a surrogate key on an entity because the integer is more efficient. That's true, but I was expecting him to state the tradeoff that the 10 character code is much more user friendly, more easily read. However, as I said earlier, I think most tradeoffs that I thought of were all covered in the book.
The book is definitely an introduction book and never claims to be more than that. The title even states it is just a simple, practical guide. The chapters are laid out as common questions someone might have about data modeling which makes it an easy read and an easy reference. I will recommend this book to new developers as a quick intro to data modeling and to help arm them with good definitions of terms and high-level explanations of common concepts that they need to understand. I will also recommend this to mid-level and senior developers who still seem to have no basic grasp of data modeling concerns and methods. And lastly I will recommend it to those who seem to think there is a huge gap between object modeling and data modeling in the common business application.

Mislead by other reviews. Covers only basics of ERD style modeling. Not current with any UML approaches. If you only do Oracle and have never modeled, this might be a good start. Would have returned it if I could.

The ease of flipping pages with easy understanding is a core feature of any good book and this one has it. I feel that this is a good start for anyone looking to learn more on data modeling or anyone who is pursuing business & data analysis.

I'm on the business side (I am not a techie) and this is the first technical book that I have actually read cover to cover. Steve in a very easy to understand manner walks through step-by-step the components of data models and the different types of models that exist. As a business user I found Chapter 7 (What is the Subject Area Model?) to be the most relevant for my day to day job. I also finally now understand the difference between normalization and abstraction. Chapter 11 on the Data Model Scorecard will be very useful to my team to ensure that data models are critiqued against the Scorecard's 10 categories. The only thing I can think of to make this book better would be to include an index, but the book is so concise and quick to locate topics that it is not really essential.

I have purchased several of these books for use in my position as a Data Architect and Database Designer. It is the perfect tool to give to both technical and business people who are new to data modeling. Steve has a way of explaining the complexities and fundamentals of data modeling in a way that people from diverse experiences and backgrounds can understand. If you have a need to quickly educate a person on data modeling, this book fits that bill and has always been well received from those to whom I shared it.

It is not a comprehensive guide to data modeling and design. It is a nice, easy to read introduction to data modeling. Think of it as "Data Modeling for Dummies" that has substance and covers most of the bases a person encounters when working with a data model.

I was disappointed to read this book as I'm a fan of the author, S. Hoberman, who usually writes good articles on the subject of modeling.

This book is similar to the "...for Idiots" series with the difference that this book is actually for idiots unlike that popular anthology.

Re: the book's title, it is clear that the audience mentioned on the cover page is a mere marketing ploy. IT professionals can safely ignore this title unless they are brand-new to the field (<1-3 months).

Some business/non-technical people might find this content useful but then again why waste money on this book when you can get similar content on the Web for free?