EXECUTING DATA QUALITY PROJECTS EBOOK DOWNLOAD
EXECUTING DATA QUALITY PROJECTS EBOOK DOWNLOAD!
1st Edition. Paperback. Presents a systematic approach to improving and creating data and information quality within the enterprise. This title describes a. She is the author of Executing Data. Quality Projects: Ten Steps to Quality Data and Trusted Information(tm) (Morgan Kaufmann,. ). Her background as a. download Executing Data Quality rights please separated direct ready server We ca almost Save the download Executing Data Quality Projects: Ten Steps.
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Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inab Executing Data Quality Projects presents a systematic, proven approach to improving and creating data and information quality within the enterprise.
Simply put, information quality is providing the correct set of accurate information, at the correct time and place, to the correct people. However, ensuring quality information is far from simple. I used the material to create an Enterprise Level Data Quality Operations plan that was very well recieved by senior management.
Danette's book is easy to follow and excellent reference material for data quality. I highly recommend it to any DQ manager, Operations Manager, etc. By adopting the lessons presented in "Ten Steps" I believe that all data quality professionals will find this publication beneficial in helping take their career or business to the executing data quality projects level.
One source for help to address data quality challenges in projects is a book called Executing data quality projects Data Quality Projects: The Ten Steps process contains concrete instructions for executing information and data quality improvement projects.
To implement The Ten Steps process effectively, it is necessary to understand some concepts related to data quality; for this reason a Framework for Information Quality and several key concepts are presented in the early chapters.
Readers are given enough background on the executing data quality projects concepts to understand the components necessary for information quality and to provide the foundation for the step-by-step instructions. The instructions provide enough structure for readers to understand what needs to be done and why.
The beauty of the approach is that it provides just enough structure to know how to proceed, but flexibility so those using it can also incorporate their own knowledge, executing data quality projects, and techniques. It was written to fill the gap between, yet be a complement to, existing books that provide higher level concepts or processes and other books that dive deep into specific data-related subjects.
Organizations can choose the applicable steps, activities, and techniques for their situation and use the methodology: For information quality-focused projects, such as a baseline data quality assessment of a particular database or a business impact assessment to help determine appropriate investments in data quality activities.