The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK)

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.[...]

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.

It's probably clear for most people but it wasn't for me: what you get here is a 5Mb PDF saved in a CD, NOT a printed book WITH a companion CD. For this price, I was expecting a printed book. Anyway, it's probably worth it (haven't read it yet).Kind regards.

My DAMA days go back to Jo Meador (Boeing DRM / Puget Sound DAMA), Jo and Michael Brackett who both taught at the UW DRM program that I attended. The list of the contributors and reviews reads like a who's who.This is great information. This is the best tool that DAMA has created in years. Way to go!!!!mkp

The information within the book is great but the PDF is protected. I like to make notes in the pdf while reading books. The pdf that i received is protected and does not allow me to. At least the author should come up with a kindle version which is very manageable.

Excellent resource for all your Data Management needs.

I must admit that I have only read through the material once since it arrived only a day or so ago. It is delivered as one 400 page pdf from which you can cut and paste and search for terms using the appopriate reader. I assume it will also work well on kindle or that ilk. Bravo to the authors for distributing it this way, it makes it a valuable resource for practitioners.On the positive side, given the elapsed time from inception to publication as you would expect the overall framework is solid. The scope is broad covering governance through architecture. modelling, BI etc etc. And a lot of effort has been made to ensure each section breaks down to the same level of detail and uses a consistent structure.The material that is presented is valid and of great value, but the material that is not presented or covered (that you would expect even at overview level) is a bit of a disappointment. For example, the Master and Reference Data section (which feels very light indeed) does not cover fundamentals such as mastering strategies, consolidation and harmonisation nor the thorny issues of local attributes. Instead, it has a paragraph or so on replication. Similarly, data migration (a pretty broad topic) gets a paragraph. The data governance section is better but lacked substance, it talks about needing to adopt a different approach for different organisations but doesn't give any clues about what those differences might be. Indeed, this failing is prevalent throughout the material.Given the document was written by experts in each discipline I would have expected more insight borne of practice. Sadly this was lacking. The description is quite clear that this is an overview resource -- and that is what you get.So in terms of value for money it is a bit overpriced. But it will go into my back pocket as a handy resource and in consulting terms, it is a steal.

The quintessential reference for all data professionals, use it lots on a day to day basis.