Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM), 2nd Edition
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Название: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM), 2nd Edition
Автор: Danette McGilvray
Издательство: Academic Press
Год: 2021
Страниц: 378
Язык: английский
Формат: pdf (true)
Размер: 32.8 MB
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work - with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The new Second Edition highlights topics such as Artificial Intelligence and Machine Learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, Big Data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.
Автор: Danette McGilvray
Издательство: Academic Press
Год: 2021
Страниц: 378
Язык: английский
Формат: pdf (true)
Размер: 32.8 MB
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work - with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The new Second Edition highlights topics such as Artificial Intelligence and Machine Learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, Big Data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.