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The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) Print Edition Perfect Paperback – February 22, 2010

4.2 out of 5 stars 22 customer reviews

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Editorial Reviews

About the Author

DAMA International is a not-for-profit, vendor-independent association of technical and business professionals dedicated to advancing the concepts and practices for data resource management and enterprise information. The primary purpose of DAMA International is to promote the understanding, development, and practice of managing data and information to support business strategies. DAMA International has chapters throughout the world. DAMA members network with other professionals to share ideas, trends, problems, and solutions. Visit dama.org to learn more.

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Product Details

  • Perfect Paperback: 430 pages
  • Publisher: Technics Publications, LLC; First edition (February 22, 2010)
  • Language: English
  • ISBN-10: 1935504029
  • ISBN-13: 978-1935504023
  • Product Dimensions: 8.5 x 1 x 11 inches
  • Shipping Weight: 2.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #85,564 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: CD-ROM Verified Purchase
No doubt, assembling a "body of knowledge" for the yet emerging profession of data management was a huge task. And, recognizing this, we must salute those who obviously toiled long and hard on putting this important resource together. Of course, the work has the look and feel of something that was assembled by a committee. It should, for it was. However, acknowledging this, we mustn't despise the importance of much of the information contained within the corpus of this text. Nor should we refrain from important, constructive criticism.

The strength of this work is its comprehensive nature. It really does provide something of a "soup to nuts" treatment of the Enterprise Data Management function. And those professionals today seriously involved in that function at any level would be well served by carefully reading and understanding this important material. The weakness of the work is what might be expected from such a communal effort: There is really no coherent philosophy of data management in evidence througout the entirety of the book. In particular, I was disappointed that the author(s) of the section on Data Warehousing seem(s) to have succombed to the sophistry that, in the data warehouse environment, it is permissable to disregard the rules of normalization. This is not true now, with the tremendous advances having been made in computer processing power. In fact, it may have never been true.

On the whole, we recommend this important work. Those who commit themselves to acquiring and reading the body of knowledge will probably already be familiar enough with the nature of corporate efforts of this sort that they will smile at some of my earlier comments. In any case, the work is well worth the time and effort for those who are truly serious about Enterprise Data Management as a profession. God bless.
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Format: Perfect Paperback Verified Purchase
Book Review--The Data Management Body of Knowledge (Oil IT Journal, June 2010)

DAMA Guide sets our to `professionalize' data management--does it succeed?

The DAMA Guide to the Data Management Body of Knowledge1 (DM-BOK) sets out to provide a compilation of principles and best practices and to provide practitioners with a framework to manage data and to `mature' their information infrastructure. These laudable aims can be judged at two levels. First by how well the book achieves its stated aim and second, how appropriate the isolation of enterprise data management is as a discipline--given that there is in every enterprise, a constellation of domain specialists, database managers, IT hardware and software experts who, to a greater or lesser extent already occupy the `space' delineated by the DM-BOK.

We were disappointed to find that the Guide does not contain a glossary or definitions--these were issues in a previous publicationThe DAMA Dictionary of Data Management. Also many interesting topics in the index--for instance `geospatial meta data standards' are one liner links to an external website. So to get the full benefit of DM-BOK you need to acquire a) the Dictionary and b) a few hundred reference works. This would not be too bad if there was any indication as to what the really essential works actually were. Another irritation is that, instead of a chapter on `master data' or `data quality' there are chapters on `master data management' and `data quality management.' This allows DM-BOK's authors to speak from the management high ground rather than addressing how things get done.

It is not all bad though.
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I liked this 1st edition when it came out, however, I will now lower it 2 grades for the following reasons. Commentary, age, and completeness isn't appropriate for a BOK on the data field in 2013. Note that the second edition is in process by DAMA.[...]

There are too many cases where information is presented with commentary superlatives in the tables and process. This processes are a body of knowledge, not a review of the literature. Such a commentary would be appropriate in the references as impact on the field.
Examples :
1) Some basic information is incorrect or incomplete : Page 96 Normalization is not performed just to "keep the data in one place", it is performed to reduce or remove anomalous behavior in data insertion, updating, deleting, and reading and to avoid query biases. .... The quality of the BOK topic should exceed or equal the quality of the wikipedia topic on a subject. There is a lack on information on Enterprise Buses.

2) Some superlatives : Page 208 "Kimball artfully uses the analogy of a kitchen."

3) 2009 BOK is aging rapidly which leaves gaps for the DM : This edition does/ did not cover the emergence of big data solutions (Today you need to know about open source Accumulo, ElasticSearch, Hadoop). An interim update process is needed for emerging BOK chapters. Does not cover partitioning/clustering and sharding concepts. Does not introduce paralellization (e.g. Map-Reduce). Big data concepts were around in academics 2006-2007, but these concepts did not make the 2009 edition.

4) Data Mining gaps : No process or selection criteria for selecting data mining approaches. A table of common data mining approaches and applicability should be in a BOK. I think this is a large weakness in the 2009 edition.
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