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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Enterprise Model Patterns: Describing the World (UML Version) Review

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(More customer reviews)I have to say before starting this review, that I played a role in publishing this book and David Hay is a personal friend of mine. However, I am also a data modeling practitioner and trainer and author, and hopefully these qualifications outweigh my subjectivity.
This book is a very important book for the data management industry. With the challenges of having to complete designs in unrealistic timeframes plus the trend in having people that have not been formally trained in data modeling completing some or all of the data modeling activities, there is a need more than ever to have sound data models as a foundation for our applications. This book provides a collection of sound data models for us to use and customize for our projects.
Here are my Top 5 favorite things about this book:
Levels of abstraction. Models can be used and customized at different levels of detail, depending on the analyst's or modeler's needs. There are four levels of modeling abstraction in this book. Level 0 contains the generic information assets and accounting areas, Level 1 contains people and organizations, geography, physical assets, activities and time. Level 2 models specific functional areas within an organization such as HR and marketing, and Level 3 consists of models specific to various industries. The models are extremely comprehensive and well connected. There are over 100 data models provided spanning close to 700 pages of text.
Applicability. I personally benefited from how the book takes real examples such as Highway Maintenance and Banking and connects them to the generic patterns, making them real and easy to apply to our own situations.
UML connection. The book uses the Unified Modeling Language to depict the models and contains a detailed explanation of how to read the UML class diagram and how it relates to relational modeling. Great comparison!
History of data modeling. The book contains a brief explanation of the history of modeling which I found very interesting.
Style of writing. I really like Dave's style of writing. He is selective of every word chosen and maintains consistency and clarity and humor throughout the text.


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This book teaches you how to capture and communicate both the abstract andconcrete building blocks of your organization's data, in order to provide acoherent and comprehensive foundation for systems development.
'Thisbook presents the most comprehensive treatment of high-level abstractionsI've seen. Any event, business, and/or systems analyst should have thisbook available, both as a learning text and as an indispensible referencebook. The knowledge packed away in this book takes decades to acquire andgestate. We are all fortunate to have it in a single volume."JamesOdellCo-chair, OMG - Analysis and Design "UML and SoaML" TaskForce
"David addresses a key, difficult, challenge for data modelling(and ontology) in this book - extracting the common pattern that underliesand unifies the variety of real data models that people use. And, what isalmost as important to many readers, he does this in a clear andunderstandable way."Chris PartridgeChief Ontologist, The BOROCentre

"A great data model, one that lays the essence of a businessbare, is a thing of beauty. It simplifies process, eases communication, andbrings order to chaos. A great data model serves for a lifetime. Powerfulstuff, this."Tom Redman, PresidentNavesink Consulting Group,LLC

"Finally, choosing a level of abstraction for a data model isaddressed methodically. David should be applauded for grasping this thornyissue and producing a wonderfully readable book. Every data modeler shouldhave one".Cliff Longman, PresidentAdaptable Data

In 1995,David Hay published Data Model Patterns: Conventions of Thought - thegroundbreaking book on how to use standard data models to describe thestandard business situations. Enterprise Model Patterns: Describing theWorld builds on the concepts presented there, adds 15 years of practicalexperience, and presents a more comprehensive view.

This modeladdresses your enterprise via four levels of abstraction:

Level0: An abstract template that underlies the Level 1 model, plus two metamodels: Information Resources and Accounting. Each of these itselfrepresents the rest of the enterprise, so to model it is to 'model a model",so to speak.

Level 1: An enterprise model that is genericenough to apply to any company or government agency, but concrete enough tobe readily understood by all. It describes people and organizations,geographic locations, (physical) assets, activities, and time.

Level 2: A more detailed model describing specific functionalareas: facilities and other addresses, human resources, communications andmarketing, contracts, manufacturing, and the laboratory.

Level3: Examples of the details that can be added to a model to address whatis truly unique in a particular industry. Here you see how to address theunique bits in areas as diverse as criminal justice, microbiology, banking,oil field production, and highway maintenance.

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Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides) Review

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(More customer reviews)Good handbook on Data Modeling High Level or Conceptual Data Model. The emphasis is on starting out with clear and concise High Level Data Models, which closely match the business requirements. Very useful book that not only gives you best practices but leaves you with a step-by-step methodology you could start using immediately. The book has a good flow with excellent illustrations, examples and case studies.

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Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse - without them dozing off?
Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives.
Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.
This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you will read about is a great way to ensure you will retain the new skills you learn in this book.
As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general.
This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization - between both businesspeople and IT.
Data modeling is one of the under-exploited, and potentially very valuable, business capabilities that are often hidden away in an organizations Information Technology department. Data Modeling for the Business highlights both the resulting damage to business value, and the opportunities to make things better. As an easy-to follow and comprehensive guide on the why and how of data modeling, it also reminds us that a successful strategy for exploiting IT depends at least as much on the information as the technology. Chris Potts, Corporate IT Strategist and Author of fruITion: Creating the Ultimate Corporate Strategy for Information Technology
The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements. Len Silverston, author of The Data Model Resource Book series

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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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