This book aims at outlining a holistic data quality (DQ) approach that businesses can adopt to energize their DQ innovation processes, perfect their data gathering and usage practices and ensure robust and reliable data is available to make judicious decisions. It book will also address the end-to-end DQ deployment process with the DQ road map and an approach that is divided into four phases (Define, Assess, Improve and Control). The focus will be on the application of the DQ methodology to achieve desired levels of data quality in various aspects of an enterprise. DQ methodology is a structured, systematic, and disciplined approach to ensure that we are getting satisfactory levels of data to make correct business decisions by minimizing cost of low quality data. This in turn, strikes a balance between rigor and creativity resulting in reduced DQ assessment and issue resolution times. The book will include a large number of cases and \"lessons learned\" as companies from a broad range of sectors deployed a DQ strategy.
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