Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies.
Quality data is the key to and advancement, whether it's from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn't have to be such a mammoth task.
Data Quality Assessment is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors - the first step in any data quality program. Master techniques in:
ETDUG
Posted Mon Mar 30 00:00:00 EDT 2009
Data Quality Assessment by Arkady Maydanchik gave me both detailed introduction and also the advanced level on Data quality issues generally found in all organizations that deal with Data Analysis. Arkady has presented the Data Quality concepts in very easy to understand and structural manner. Arkady did a good job in giving new definition to the Data Quality Rules in daily data management processes that includes ETL,cleansing and purging data.The importance of Data quality in data integration and data management has been very well explained.
Especially Chapter 4 talked in very detail about attribute domain constraints.Chapter 4, chapter 5 and chapter 6 has done a good job of explaining in very detail about the importance of data quality and also the implementation of rules in data profiling and data modeling in 3NF models.
Rules for State Dependent objects and attribute dependency rules have been well explained and give a good understanding of the topic.
Fine tuning data quality rules and cataloguing errors complete the data quality assessment process with detailed instructions on how to organize metadata to leverage the benefits of implementing data quality rules.
As a consultant working in different Datawarehouse/BI projects and Data Integration projects, this book gave me good exposure to the common,general data quality problems and implementing techniques to leverage the benefits of analyzing the data.
Arkady has done a wonderful job of bringing all this experience and knowledge in the form of this title.
I would certainly recommend to own a copy of this book to any Software/Business analyst working in datawarehouse to data integration to EIM projects.
Overview
Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting ...