Viral Data in Soa: An Enterprise Pandemic by Neal Fishman (9780137001804)
Neal Fishman
Release Date: 31 July 2009 Format: Paperback Pages: 288 Category: Network Management Publisher: IBM Press ISBN: 9780137001804 ISBN-10: 0137001800
In an SOA/web services world, excellent data quality is more crucial than ever: this book shows how to finally get just that
Offers practical solutions for assessing, improving, and sustaining data quality.
Explains why data governance is so critical, and offers realistic models for implementing it.
For every IT and business decision-maker or practitioner who must provide highquality data, or rely on the data they receive via web services.
By one of IBM's leading data quality experts.
In service-oriented environments, web services share data more widely and rapidly than ever before. If that data isn't accurate, it can powerfully impact larger areas of the enterprise than ever before. It's easier than ever for incorrect data to trigger inappropriate actions, prevent urgent actions from being taken, disrupt customer transactions and relationships, generate compliance problems, and damage business performance. Viral Data in SOA offers a comprehensive blueprint for ensuring the reliability of data in today's SOA environments. It explores the subject from four critical perspectives:
Data management and governance.
Understanding data quality through observation.
Sustaining data quality through conditioning.
Evaluating data quality for learning and optimization.
The author presents key concepts in the context of a 'journey': a story that will help business and technical practitioners identify the unique challenges of data quality in their own SOA environments, and implement solutions that work. As they follow this journey, readers will discover how to think about data quality issues on a risk/reward basis... why effective data governance is so important, and how to achieve it... how to assess data quality 'pre-flight, in-flight, and post-flight'... how to overcome the inevitable 'decay' in business, system, reference, and 'meta' data... and, last but not least, how to actually drive a data quality initiative that succeeds.