Evidence-based decision-making : how to leverage available data and avoid cognitive biases

著者

    • Banasiewicz, Andrew D.

書誌事項

Evidence-based decision-making : how to leverage available data and avoid cognitive biases

Andrew D. Banasiewicz

Routledge, 2019

  • : hbk

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注記

Includes index

内容説明・目次

内容説明

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.

目次

Part I: Decision-Making Challenges Chapter 1: Subjective Evaluations Thinking and Games Mind vs. Machine Learning and Remembering The Decision-Making Brain Chapter 2: Non-Generalizable Objectivity Familiar Clues Anecdotal Evidence Best Practices & Benchmarks Non-Representative Samples Chapter 3: Mass Analytics Digitization of Life Data as the New Normal Data in Organizations The Analytics Industry Part II: Evidence-Based Practice Chapter 4: Evidence-Based Movement The Practice and Science of Management Evidence-Based Practice The Road Ahead Chapter 5: The Essence of Evidence What is Evidence? Empirical Evidence Research Evidence Experiential Evidence Internalizing Evidence Part III: The Empirical & Experiential Evidence Framework Chapter 6: Probabilistic Thinking Decision Uncertainty Evidence Pooling Cross-Type Amalgamation Chapter 7: The 3E Framework Organizational Decision-Making The Empirical & Experiential Evidence Framework Insight Extraction Believability of Evidence Chapter 8: Sourcing & Assessing: Operational Data Data, Research, and Decision-Making Probabilistic Analyses of Organizational Data Operational Data and Databases Getting Started with Operational Data Exploring Operational Data Exploratory Data Analysis Confirmatory Data Analysis Chapter 9: Sourcing & Assessing: Research, Norms, and Judgment Thematic Analyses of Empirical Research Summarizing Norms & Standards Pooling Expert Judgment Part IV: Evidence-Based Decision-Making in Organizations Chapter 10: Internal Design & Dynamics Organizations as Human Collectives Business Organizations Organizations and Decision-Making The 3E Framework & Organizational Dynamics Chapter 11: External Forces & Influences External Forces Non-Systematic Influences

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