Free Downloads Measuring Data Quality For Ongoing Improvement: A Data Quality Assessment Framework (The Morgan Kaufmann Series On Business Intelligence)

                
              
Free Downloads 
Measuring Data Quality For Ongoing Improvement: A Data Quality Assessment Framework (The Morgan Kaufmann Series On Business Intelligence)
The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challengesEnables discussions between business and IT with a non-technical vocabulary for data quality measurementDescribes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation
File Size: 3369 KB
Print Length: 376 pages
Publisher: Morgan Kaufmann; 1 edition (December 31, 2012)
Publication Date: December 31, 2012
Sold by:  Digital Services LLC
Language: English
ASIN: B00AWOTAQE
Text-to-Speech:  Enabled
X-Ray:     Not Enabled
Word Wise: Not Enabled
Lending: Not Enabled
Enhanced Typesetting:  Not Enabled
Best Sellers Rank: #477,868 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #122 in Books > Computers & Technology > Programming > Software Design, Testing & Engineering > Structured Design   #241 in Books > Politics & Social Sciences > Social Sciences > Library & Information Science > Library Management   #349 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design
Many systems are developed with Data Quality as an after-thought. The writer clearly outlines the reasons why Data Quality should be thought of as a strategy, not just a one-time activity or the result of using a specific methodology (e.g., Profiling).This should be required reading for Data Quality Practioners, and other related data stakeholders such as Data Architects, Data Modelers, and others who lead data warehousing projects. Those who are in the beginning phases of a project need to understand that this is a shared responsibility and need to structure systems to incorporate appropriate strategies from the onset.The writer has a background in communications and developing web content. Because of this, the writing is well-organized and supremely logical. The book starts with a high-level overview, then drills down to more specific details. For me, it was a little frustrating because I like to jump in "feet first" and get details rapidly. But I found that slowing down and studying the early sections/chapters provided a good foundation for the later material.Be aware that the book may not meet all of your needs and expectations. In the introduction, the author makes an important statement: "...it is important also to point out what the book will not do. It does not, for example, present 'code' for implementing these measurmnents. Although it contains a lot of technically oriented information, it is not a blueprint for a technical implementation. In defining requirements for measurement types, it remains business-oriented and technology independent. It also does not advocate for the use of particular tools."The strength of this book lies in the author's statement: "Many people want to buy tools before they define their goals for measuring.
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