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eBook Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems) ePub

eBook Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems) ePub

by David Loshin

  • ISBN: 0124558402
  • Category: Management and Leadership
  • Subcategory: Perfomance and Work
  • Author: David Loshin
  • Language: English
  • Publisher: Morgan Kaufmann; 1 edition (January 31, 2001)
  • Pages: 493
  • ePub book: 1350 kb
  • Fb2 book: 1167 kb
  • Other: lit txt doc lrf
  • Rating: 4.2
  • Votes: 628

Description

Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge.

Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the growing number of companies committed to wresting every possible advantage from their vast stores of business information. Expert advice from a highly successful data quality consultant

Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology .

Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge.

Enterprise Knowledge Management : The Data Quality Approach. At the time the book was published I worked as a data quality manager at the New Zealand Ministry of Health focusing on the implementation of the Data Quality Strategy for National Health Databases. It was a great help for us. We've implemented many of David Loshin's principles.

Enterprise Knowledge Management gives you just what you need: a precise yet adaptable methodology for defining, measuring, and improving data quality and managing business intelligence. This one-of-a-kind book begins by laying out an economic framework for understanding the real business value of data quality. It then outlines rules for measuring data quality and determining where it can and should be improved.

The book assumes you are working in a large scale environment where one person's job is to focus on data .

The book assumes you are working in a large scale environment where one person's job is to focus on data quality. If you wear more than one hat in your job many of the approaches are simply not practical in this book as their return on investment would not be worth the effort to implement. This might be excellent for Fortune 500 companies, but for smaller shops this book will not push you very far solving problems on a day to day basis.

Enterprise Knowledge Management. The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems). Published January 22, 2001 by Morgan Kaufmann. Knowledge management, Data warehousing.

Loshin, David, Enterprise Knowledge Management: The Data Quality Approach. San Francisco: Morgan Kaufmann, 2001. Redman, Thomas . Data Quality for the Information Age. Norwood, MA: Artech House, 1996. Data Quality: The Field Guide. Boston: Digital Press, 2001. Wang, Richard . Mostapha Ziad, and Yang W. Lee, Data Quality. Norwell, MA: Kluwer Academic Publishers, 2001. Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems). ISBN: 1558608915 EAN: 2147483647. Year: 2003 Pages: 133.

Master data management appears to be different because my perception was that it hinged upon some critical concepts that . Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems).

Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems).

Today, companies capture and store tremendous amounts of information about every aspect of their business: their customers, partners, vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality-a problem businesses recognize and are working feverishly to solve. Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. The Morgan Kaufmann Data Management Systems. David Loshin is President of Knowledge Integrity, In. a company specializing in data management consulting. The author of numerous books on performance computing and data management, including "Master Data Management" (2008) and "Business Intelligence - The Savvy Manager's Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.

Today, companies capture and store tremendous amounts of information about every aspect of their business: their customers, partners, vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality--a problem businesses recognize and are working feverishly to solve.

Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the growing number of companies committed to wresting every possible advantage from their vast stores of business information.

Expert advice from a highly successful data quality consultantThe only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionalsDetails the high costs of bad data and the options available to companies that want to transform mere data into true enterprise knowledgePresents conceptual and practical information complementing companies' interest in data warehousing, data mining, and knowledge discovery

Comments

Thetalen Thetalen
First of all, I really liked the fact that I could "rent" the book on Kindle. It's a great way to consume literature like this where the subject is vague enough that any argument might be carried out outside the reader's expectations.

I started reading the book because of the practical need for me to delivery an assessment on my company's data quality. That practicality was not delivered in this book. There is a lot of good concepts, but hardly usable out-of-the-box. Indeed, the author never claimed to address the practical side of the assessment.
An example of that is the scorecard, which enumerates the costs of the processes identified in the enterprise. As compelling as that idea is, I still have to figure out what the unit cost would be and what the time period for that analysis was (since all I have is a picture with numbers for me to guess about).

That is why I give 3 stars - and I would have given 4 if not for the fact that I personally do not like when authors write their own review giving themselves 5 stars... It is a readers' comments section after all.

Curiously, I note significant analogies to a few concepts articulated in Dr Redman's "Data quality: the field guide" published back in Jan 2001. Especially the use of statistical process control for quality measurements and even the year 2000 presidential election example. I have to be fair, and state that Loshin does indeed develop his original methodology in spite of a few repetitions.

All in all, the book is too long for what it actually offers. Clearly, to make it usable one should contract Mr Loshin's firm and have an assessment performed to see its workings in detail. I accept that as fair, just does not feel like 5 stars to me.
Yahm Yahm
I am a consultant in the area of knowledge management and data modeling, and I have read all the major books on the topic of data quality, and this book is, by far, the best treatement of the subject.
Enterprise Knowledge Management is a great handbook for both the manager and the practitioner - Loshin deals with the personal and political aspects of data ownership, buildingan ROI model for data cleansing, and a concise methodology about how to measure levels of data quality.
I have heard speeches by a handful of the major speakers in the area, and my impression is that they are willing to tell you to go and measure data quality, or to talk about data quality issues, but they would be hard-pressed to actually solve the problems. From reading this book, it is clear that Loshin is an expert in this area, and that he has not only dealt with the high level aspects of data management but also has experience in the trenches.
This book is perfect for both manager and technical people dealing with data warehousing or data migration projects.
Qulcelat Qulcelat
Most of the literature on Data Quality focuses on the challenges of creating and maintaining a data warehouse. Thankfully, for those of us trying to improve the integrity of the information in our OLTP databases, this book presents a methodology which is not specific to any one data environment.
This book is packed with lists of cases to consider for each step of the methodology. Each case is nicely documented. Actually, much of the book is taken filled with the documentation for each case, which may cause a person to lose sight of the methodology that is being presented.
I am person who prefers to learn concepts. I am not as interested in memorizing details. Hence, I would read this book, skipping most of the documentation in the lists, instead focusing on understanding the methodology. Thereafter, I would use this book as a reference when needing information on a particular step of the methodology.
Agrainel Agrainel
At the time the book was published I worked as a data quality manager at the New Zealand Ministry of Health focusing on the implementation of the Data Quality Strategy for National Health Databases. It was a great help for us. We've implemented many of David Loshin's principles. Most importantly it helped us to understand that the majority of our DQ problems were not due to the poor data management processes, but because of the inadequate system's design or poor data model, which was either conceptually or contextually incorrect, incomplete or inaccurate.
Akinonris Akinonris
While I am not a technical person, this booked helped me enormously to understand the management issues that surround data quality. In today's world, I am shocked that more companies are not using this approach to save massive sums of money. This book has given me the tools to do so within my company. I highly recommend it!
Tto Tto
David has written an excellent data quality book. He focuses on a real works around data quality. He presents a practical approaches how to solve a different types of quality defects and also pointed out main quality principles. But reader must think how to apply mentioned principles and approaches in reader's organization.

Simply, good reading with application on a real cases.
Llallayue Llallayue
This book is NOT about enterprise management, it's about SQL. If I had wanted a book on SQL, I would have bought a book about SQL. I wanted a book on Enterprise Knowledge Management. This is not it.