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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Building the Unstructured Data Warehouse Review

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(More customer reviews)I currently work for an insurance company and we are in the process of gathering requirements to bring lots of text into our data warehouse from the customer service area. This book could not have come out at a better time. Our data warehouse contains mostly structured data and our initial approach was to bring all of the text in and treat it no differently than the more structured data such as dates and codes. We did not have the knowledge base on our team to do something different with the text.
This book greatly helped our data warehouse team in two areas. The first is in explaining terms to our managers so they understand the complexities with analyzing text. The book has a clear way of explaining concepts so that we can then use this same approach when talking with management. Phrases such as the possibility of creating a "Data Junkyard" resonated very well with management. The second area is in storage and indexing strategies. Originally our plan was to bring in all of the text from the customer service area. We got the idea from this book to bring in only the essential data and then point back to the source where the actual data lives. There are over a dozen different indexing strategies discussed in this book, many of which our database team had never considered.
They say a book meets its expectations if you can garner a couple of gems from it. This book definitely has lots of gems. A must read if you currently have a data warehouse and have the business requirements to analyze text.


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Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now!
Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.
Master these ten objectives:
Build an unstructured data warehouse using the 11-step approach
Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure
Overcome challenges including blather, the Tower of Babel, and lack of natural relationships
Avoid the Data Junkyard and combat the Spider's Web
Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development
Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement
Design the Document Inventory system and link unstructured text to structured data
Leverage indexes for efficient text analysis and taxonomies for useful external categorization
Manage large volumes of data using advanced techniques such as backward pointers
Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances
The following outline briefly describes each chapter's content:
Chapter 1 defines unstructured data and explains why text is the main focus of this book.
Chapter 2 addresses the challenges one faces when managing unstructured data.
Chapter 3 discusses the DW 2.0 architecture, which leads into the role of the unstructured data warehouse. The unstructured data warehouse is defined and benefits are given. There are several features of the conventional data warehouse that can be leveraged for the unstructured data warehouse, including ETL processing, textual integration, and iterative development.
Chapter 4 focuses on the heart of the unstructured data warehouse: Textual Extract, Transform, and Load (ETL).
Chapter 5 describes the 11 steps required to develop the unstructured data warehouse.
Chapter 6 describes how to inventory documents for maximum analysis value, as well as link the unstructured text to structured data for even greater value.
Chapter 7 goes through each of the different types of indexes necessary to make text analysis efficient. Indexes range from simple indexes, which are fast to create and are good if the analyst really knows what needs to be analyzed before the indexing process begins, to complex combined indexes, which can be made up of any and all of the other kinds of indexes.
Chapter 8 explains taxonomies and how they can be used within the unstructured data warehouse.
Chapter 9 explains ways of coping with large amounts of unstructured data. Techniques such as keeping the unstructured data at its source and using backward pointers are discussed. The chapter explains why iterative development is so important.
Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. In addition, the data warehouse appliance is discussed.
Chapters 11, 12, and 13 put all of the previously discussed techniques and approaches in context through three case studies.

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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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