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The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) Print Edition
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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 8, 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:
3.8 out of 5 stars
26 customer reviews
Amazon Best Sellers Rank:
#862,704 in Books (See Top 100 in Books)
For decades, having a comprehensive and integrated approach for data management was the holy grail for IT professionals. This was one the most overlooked aspect in ICT industry, as IT professionals tend to see Data from their silo-ed functional standpoint (Data Modeling, Database Administration, Data Security, Databases development, etc.,..). It was not until the publishing of DAMA DMBOK that we had this framework, as DAMA-DMBOK provides a solid framework to address Data Management covering 10 different functional areas.Things that I hope the next editions of this wonderful guide to focus on are:- More elaboration on the NoSQL databases.- It is highly valuable if the guide provides short descriptions of the techniques used in DM, such as Data Modeling (relational, multi-dimensional, NoSQL models, etc. ), Ontologies modeling, Information value chain modeling, data flows, data lineages, data integration modeling, etc.- I think the guide could elaborate a bit on the links and touch points between data management and knowledge management.- How the data management will be utilized or affected by Service-oriented architecture concepts.All in all, we are eagerly waiting the next edition of DMBOK this year.
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. Even for it's time.Example of what might be in a data mining techniques table.Common Data Mining challenge : Weighing the importance of selected attributes in explaining predictions.Data and Text mining common technique : Feature Selection.In data mining "Features" are attributes of data. In mining, features are selected (Feature Selection) for their importance in explaining the predictiveness of a data mining model based on a sample of data. An example of data mining task employing feature selection may be to determine which factors (marital status, car ownership, etc) determine household spending based on an representative sample of data. Feature selection is used in seeking possibly meaningful outliers of data. A large sample set may discover a meaningful relationship that all owners of hybrid cars purchase 3 times more birthday cards. Simplifcation of features is called feature extraction. Feature selection and extraction is often a preliminary process in data mining that employes Support Vector Machine processes or some Bayesian processes. Minimum Description Length (MDL) is a common algorithm applied for feature selection mining by assuming that the simplest possible sample attributes is regular. MDL avoids overfitting (introducing error) in feature selection and extraction. [...]Lesser issues ...5) Update Roles and responsibilities of the Data Professional related to standards development. Standards bodies that are key in 2013 such as NIEM, OGA, and ISO. More on DoD standards bodies would be useful to many.6) Common optimization techniques: (E.G. What is an "explain plan" do ? What is an access path? What is profiling? What is a cost based optimizer? How do common DBMS products support optimization and what features should the DBA expect? )7) 2nd Edition hope... A basis of estimation process would be useful if anyone has the data for data management projects based on DAMA processes in the BOK (A COCOMO for data projects.)A new version is due 2014 Q4. See the below link. This 2009 version is valid but aged.[...]
It is wonderful that the leadership team at DAMA has now seen to the publication of a print version of their terribly important Guide to the Data Management Body of Knowledge. Like another reviewer, I own both versions. And also like that other insightful reviewer, I enjoy having the print copy that enables me to highlight critical points and make notes in the margin.The DAMA Guide to the Data Management Body of Knowledge is arguably the most important material that has yet been published relative to the emerging profession of Data Management. Any serious folks who today are privileged to work in this profession should own a copy. But it is clearly not enough to own it. There is a wealth of important information contained within the corpus of this text that the aspiring, and even fully arrived, data management professional really ought to internalize.Another reviewer has roundly complained that the book, as it now stands, merely provides a reference to hundreds of other publications. This is an unfair charge, in my view. In truth, the Guide is what it is: a compendium of best practices and generally sound wisdom compiled by a large committee of dedicated data management professionals. Certainly, there are hundreds of excellent and important references. But if the Guide was not a Guide but an Encyclopedia, it would have been many volumes, rather than just over four hundred pages, including the appendices. I, for one, am extremely grateful for the treasure trove of information contained within this Guide to the Data Management Body of Knowledge. It truly is essential material for our emerging profession.On the occasion of acquiring the print version of the Guide, I, of course, read all of the material over again. And this journey back through the accumulated wisdom once again served me very well. I must register, though, my one criticism once more: the section on Data Warehousing and Business Intelligence is really quite weak in relation to the rest of the material, and mars the overall quality of the Guide. Absent the lack of quality of this chapter, the Guide would certainly merit five stars rather than four. However, I am confident that the wonderful leadership team at DAMA will substantially improve the quality of this section in future editions of the Guide. Even now, the Guide is tremendous, and ought to occupy an honored place in the personal library of any person seriously interested in a successful and rewarding career in the Data Management discipline. God bless.
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