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Evidence-Based Decision-Making

Evidence-Based Decision-Making : How to Leverage Available Data & Avoid Cognitive Biases. 実証ベースの意思決定

・ISBN 978-1-138-48519-8 hard GB£ 155.00

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・ISBN 978-1-138-48529-7 paper GB£ 41.99

¥13,302.- (税込) (※)価格はご注文時の参考価格となります。
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電子版あり 大学・学術機関向け電子ブック(eBook)ISBN 978-1-351-05007-4

著者・編者Banasiewicz, Andrew,
出版社(Routledge, UK)
出版年月2019
ページ数286 pp.
言語ENG
ニュース番号<649-854 649-P1854>

解説

Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence, primarily derived from a variety of data available to organizations, can be used to improve the quality of business decision-making, by helping decision makers circumvent the various cognitive biases that adversely impact how we all think.

The book is built on the following premise: During the past decade, the new 'data world' emerged, in which the rush to develop competencies around business analytics and data science can be characterized as nothing less than the new commercial arms race. The ever-expanding volume and variety of data are well known, as are the great advances in data processing/analytics, data visualization, and related information production-focused capabilities. Yet, comparatively little effort has been devoted to how the informational products of business analytics and data science are 'consumed' or used in the organizational decision-making processes, as the available evidence shows that only some of that information is used to drive some business decisions some of the time.

Evidence-Based Decision-Making details an explicit process describing how the universe of available and applicable evidence, which includes organizational and other data, industry benchmarks, scientific studies, and professional experience, can be assessed, amalgamated, and funneled into an objective driver of key business decisions.

Introducing key concepts in relation to data and evidence, and the history of evidence-based management, this new and extremely topical book will be essential reading for researchers and students of data analytics as well as those working in the private and public sectors, and in the voluntary sector.