The DAMA Dictionary of Data Management Review

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(More customer reviews)Confused about WSDL, SOX or DML? Not only does the DAMA Dictionary of Data Management give you the key to unlocking hundreds of acronyms, it will also give you the definition for them. Great for figuring out what they meant in those presentations where they used all those 3-letter abbreviations designed to confuse the rest of us.

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If you think enterprise data and geospatial data describe Star Trek episodes, you could use the DAMA Dictionary of Data Management. This glossary contains over 800 terms defining a common data management vocabulary for IT professionals, data stewards and business leaders. It is in pdf format embedded with links for easy navigation between terms, and delivered to you on CD-ROM. An index is included with the dictionary, which organizes the terms by topic.
Topics include:
Finance & Accounting
Business
Marketing & Customer Relationship Management
Planning
Project Management
Knowledge Management
Process Management
Roles
Information Technology
Standards Organizations
Data
Data Management
DAMA & Professional Development
Data Governance and Stewardship
Architecture
Data Modeling
Normalization
Related Modeling and Analysis
Databases & Database Design
Structured Query Language (SQL)
Object-Orientation
Software Development
Artificial Intelligence
XML Development
Parallel Database Processing
Database Administration
Geospatial Data
Data Security Management
Data Movement & Integration
Data Warehousing & Business Intelligence
Analytics & Data Mining
Multi-dimensional / OLAP
Reference & Master Data Management
Meta Data Management
Data Quality Management
Document, Record & Content Management
Semantic Modeling


Press release:
The Data Management Association International (DAMA) Releases DAMA Dictionary of Data Management Industry Takes an Environment-Friendly Stand on Standard Definitions
DAMA Dictionary of Data Management availability was announced at the DAMA International Symposium & Wilshire Meta Data Conference in San Diego March 16-20, 2008.
Over 800 terms defining a common data management vocabulary for IT professionals, data stewards and business leaders.
Over 40 topics including finance and accounting, knowledge management, architecture, data modeling, XML, and analytics.
Authored by DAMA International, published by Technics Publications, LLC, and edited by Mark Mosley, this dictionary will promote a standard set of data management terms within the field of Information Technology (IT).
Mosley says "As the premiere organization for data management professionals, DAMA seeks to lead the data management profession to maturity. One of the hallmarks of a mature profession is a common vocabulary with clearly understood definitions. DAMA offers the Dictionary to a field in great need of clarity in its terminology and semantics. We hope the Dictionary will be a useful tool for data management professionals, IT colleagues, managers, data stewards and business leaders. All these people share responsibility for data management, so it's very important for all parties to speak a common language."
Deborah Henderson, President of DAMA International Foundation and VP Education and Research for DAMA International, believes the CD-ROM format will not only be easier to use than the traditional book format, it is also more compact and environmentally responsible. "Publishing this work on CD-ROM saves over 4000 pounds of paper and conservatively 700 pounds of Greenhouse gases."

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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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Data Modeling Master Class Training Manual: Steve Hoberman`s Best Practices Approach to Understanding and Applying Fundamentals Through Advanced Modeling Techniques (Take It With You) Review

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(More customer reviews)This Master Class captures in one place much of the innovative stuff that Steve Hoberman has brought to the field of data modeling. It provides a great foundation to become a truly skilled data modeler. You can also follow Steve's continuing advances in the field as well as his design challenges at www.stevehoberman.com.

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This is the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman`s website.
The Master Class is a complete course on data modeling, containing four days of practical techniques for producing solid relational and dimensional data models. After learning modeling concepts and terms, you will apply a best practices approach to building and validating data models through the Data Model Scorecard™. You will learn not just how to build a data model, but also how to build a data model well. Challenging exercises and workshops will reinforce the material and enable you to apply these techniques in your current projects.
Steve is an excellent presenter, keeping his audience engaged while covering in-depth relational and dimensional modeling techniques. His entertaining style combined with his many real world examples and exercises, held our interest throughout the course. S. DeCandia, Pfizer Global Manufacturing
In my long professional career, I have participated in many training seminars but I have never encountered a class in which the subject had been so thoroughly considered and presented in such a clear and engaging manor. As a fairly new, but full time data modeler, I expect to use the things that I learned in this class every day. My only regret is that the class had to end. G. Schmid, Travelers Insurance
This was the most comprehensive, informative, energetic, interesting and just plain FUN class I have ever taken on the subject of Data Modeling. G. Werner, Long Island Railroad
Part 1: Modeling Basics Assuming no prior knowledge of data modeling, we will begin this section with an entertaining exercise that will illustrate an important gap filled by data models. Next, we will explain data modeling concepts and terminology. We will also explore each component on a data model and practice reading business rules.
Part 2: Overview to the Data Model Scorecard™The Scorecard is a set of ten categories for validating a data model. We will explore best practices from the perspectives of both the modeler and reviewer, and you will be provided with a template to use on your current projects.
Part 3: Understanding subject area, logical, and physical data modelsThe subject area model captures a business need within a well-defined scope; the logical data model captures an application-independent business solution; and the physical data model captures the technical solution by focusing on factors such as performance and security.
Part 4: Ensuring the model captures the requirementsWe will focus on techniques such as the use of spreadsheets and business assertions to ensure the data model meets the business requirements.
Part 5: Validating model scopeWe will focus on techniques for validating that the scope of the requirements matches the scope of the model. If the scope of the model is greater than the requirements, we have a situation known as scope creep. If the model scope is less than the requirements, we will be leaving information out of the resulting application.
Other training modules include:
Following acceptable modeling principles
Determining the optimal use of generic concepts
Applying consistent naming standards
Arranging the model for maximum understanding
Writing clear, correct, and consistent definitions
Matching the model with the enterprise
Comparing the meta data with the data

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Data Quality Assessment Review

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(More customer reviews)My business, Northwest Database Services, has cleaned clients' data for over 20 years. In all that time I've only met two or three people who do this kind of work professionally on a regular basis. (Our conventions are small.)
With this in mind, it is easy to see why I was so pleased and surprised to find someone had written a book about the subject; especially as thoughtful and insightful a one as Quality Data Assessment.
Arkady Maydanchik brings years of experience and first-hand knowledge to the table, while organizing it into a logical, sequential and, most important, understandable manual. This book goes into the typical causes of data degradation as well as how to find it and begin the process of fixing it.

You can't even begin to fix your data until you have a clear picture of what's going on "in there", so data assessment is the first and maybe the most important step in achieving data consistency and reliability. If your work involves data assessment, migration creation or maintenance, you should have this book on your shelf. It's that simple.
But wait, there's more. This is just the first volume in a set of data assessment and cleaning processes, tips, tricks and tools books that will be forthcoming. I'm told that the second volume in this series will be published in October 2008. I know it sounds incredibly geeky, but I can hardly wait.


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Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies.Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task.DATA QUALITY ASSESSMENT is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors.Master techniques in data profiling and gathering metadata, designing data quality rules, organizing rule and error catalogues, and constructing the dimensional data quality scorecard.David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners."

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