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

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

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.