Enterprise Knowledge Management - The Data Quality Approach - Loshin David

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Résumé :
Your company captures and stores tremendous amounts of information about every aspect of its business. But with this rise in the quantity of information has come a corresponding decrease in its quality. Now more than ever, reversing this trend may spell the difference between success and failure. How can you and your organization respond to this challenge? 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. Finally, it teaches proven techniques through which you can achieve meaningful advances in the quality of your business data, including domain- and mapping-based consolidation of enterprise knowledge. FEATURES: * Expert advice from a highly successful data quality consultant. * Rigorously methodical in its approach to the problem and the detailed solution it presents. * Teaches you to measure quality in real business terms and to achieve meaningful, demonstrable improvement. * Uniquely combines business acumen and technical expertise - an indispensable resource for managers and IT professionals alike. * Documents the high costs of bad data and details the options available to any company that wants to transform mere data into true enterprise knowledge.

Biographie:
David Loshin is the President and Chief Technology Officer of Knowledge Integrity Incorporated (www.knowledge-integrity.com), a technical consulting firm that helps businesses address problems arising from the collection, migration, transmission, and analysis of large sets of data. He holds an M.S. in Computer Science from Cornell University and is the author of High Performance Computing Demystified and Efficient Memory Programming. He bas been working in the areas of pattern recognition, data parsing, fuzzy pattern matching and searching, and data mining for over a decade.

Sommaire:
["Who owns information?","Data quality in practice","Economic framework of data quality and the value proposition","Dimensions of data quality","Statistical process control and the improvement cycle","Domains, mappings, and enterprise reference data","Data quality assertions and business rules","Measurement and current state assessment","Data quality requirements","Metadata, guidelines, and policy","Rule-based data quality","Metadata and rule discovery","Data cleansing","Root cause analysis and supplier management","Data enrichment\/enhancement","Data quality and business rules in practice","Building the data quality practice."]