1st Edition

Evidence-Based Decision-Making How to Leverage Available Data and Avoid Cognitive Biases

By Andrew D. Banasiewicz Copyright 2019
    282 Pages
    by Routledge

    282 Pages
    by Routledge

    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.

    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

    Biography

    Dr. Andrew D. Banasiewicz is the director of data science and analytics programs at Merrimack College, a professor of business analytics at Cambridge College, and the founder of Erudite Analytics, a data analytical consultancy focused on risk assessment.