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Download The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) Print Edition fb2, epub

by DAMA International

Download The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) Print Edition fb2, epub

ISBN: 1935504029
Author: DAMA International
Language: English
Publisher: Technics Publications, LLC; First edition (February 8, 2010)
Category: Business Technology
Subcategory: IT
Rating: 4.1
Votes: 344
Size Fb2: 1752 kb
Size ePub: 1543 kb
Size Djvu: 1399 kb
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DAMA International Guide to Data Management Body of Knowledge .

DAMA International Guide to Data Management Body of Knowledge (DAMA DMBOK ®). DAMA International Dictionary of Data Management Terms. Data Management Overview.

International Foundation DAMA International’s Data Management Body of DAMA-DMBO. 78 MB·74,825 Downloads.

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled.

Data Integration and Interoperability is central to the emerging area of Big Data management. With this approach, applications do not have to have direct interaction with or knowledge of the inner workings of other applications

Data Integration and Interoperability is central to the emerging area of Big Data management. Big Data seeks to integrate various types of data, including data structured and stored in databases, unstructured text data in documents or files, other types of unstructured data such as audio, video, and streaming data. With this approach, applications do not have to have direct interaction with or knowledge of the inner workings of other applications. SOA enables application independence and the ability for an organization to replace systems without needing to make significant changes to the systems that interfaced with them.

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

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.

The DAMA-DMBOK Guide deserves a place on every Data Management professionals bookshelf and for the General Manager, it will serve as a guide for setting expectations and assigning responsibilities for managing and practicing what has become the very most critical resource.

The DAMA-DMBOK Guide deserves a place on every Data Management professionals bookshelf and for the General Manager, it will serve as a guide for setting expectations and assigning responsibilities for managing and practicing what has become the very most critical resource owned by an Enterprise as it (the Enterprise) progresses into the Information Age: DATA!

Dama International, Mark Mosley.

Dama International, Mark Mosley. Escrito por mais de 120 praticantes de gerenciamento de dados, o guia DAMA(R) que estabelece um corpo de conhecimento em gerenciamento de dados (DMBOK(R)) é a mais impressionante compilação dos princípios do gerenciamento de dados e melhores práticas já montadas. Este guia oferece para o gerenciamento de dados, profissionais de TI, executivos, profissionais do conhecimento, educadores e pesquisadores uma estrutura de trabalho para gerenciar seus dados e as informações da infra-estrutura.

ISBN13: 9781935504023. More Books . ABOUT CHEGG. Terms of Use. Chegg Tutors Terms of Service. Your CA Privacy Rights.

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure.

This print edition is also available in electronic PDF format on a CD (see ISBN 9780977140084).

The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance Data Architecture Management Data Development Database Operations Management Data Security Management Reference & Master Data Management Data Warehousing & Business Intelligence Management Document & Content Management Meta Data Management Data Quality Management Professional Development As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: To build consensus for a generally applicable view of data management functions. To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology. To document guiding principles for data management. To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches. To clarify the scope and boundaries of data management. To act as a reference which guides readers to additional resources for further understanding.

Comments:

Dream
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.
[...]
Malalanim
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.
Jeyn
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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