| Description |
xx, 156 pages : illustrations ; 24 cm |
| Contents |
Machine generated contents note: ch. 1 Challenges and Opportunities with Optimal Decision Making and How Advanced Analytics Can Help -- 1.1.How We Make Decisions and What Gets in the Way -- 1.1.1.Intuition Versus Analytical Thinking -- 1.1.2.Poor Intuitive Statisticians -- 1.1.3.Understanding Human Nature -- 1.1.4.Biases and Decisions -- 1.1.5.Big Data and Information Overload -- 1.1.6.The Problem with Certitude -- 1.1.7.Advanced Analytics Does Not Care Who It Annoys -- 1.1.8.Types of Decision Making -- 1.2.Rise of the Machines: Advanced Analytics and Decision Making -- 1.2.1.Advanced Analytics -- 1.2.2.Predicting Outcomes -- 1.2.3.Improper Linear Models: Combining Expert Intuition with Analytics -- 1.2.4.Artificial Intelligence and Machine Learning -- 1.3.Human and Machine: The Ideal Decision-Making Team -- 1.3.1.A Word About AI Tools -- ch. 2 Collaboration, Cooperation, and Reciprocity -- 2.1.Human Nature and Human Science -- 2.1.1.Reciprocity and Fairness -- |
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Contents note continued: 2.1.2.Selfish, Greedy, Lazy, and Dishonest -- 2.1.3.Human Nature 2.0 -- 2.1.4.Fierce Cooperation -- 2.1.5.Collaboration -- 2.1.6.Hard Wired to Share What We Know -- 2.1.7.Collective Intelligence -- 2.1.8.Asymmetric or Private Information -- 2.1.9.Game Theory 101 -- 2.2.The Power of Collaboration: The Scandinavian Model -- 2.2.1.What Kinds of Organizations Could Benefit from a High Degree of Collaboration? -- 2.2.2.The Benefits of Collaboration -- 2.2.3.The Bottom-Line Impact of Participative Decision Making -- 2.2.4.Organizational Culture -- 2.2.5.Optimal Incentive Contract for Collaboration: Sharing Control and Return Rights -- 2.2.6.Models of Collaboration -- 2.2.7.The SAS Institute -- 2.2.8.EMC |
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Contents note continued: 2.3.3.Deep Q&A Expert Systems -- ch. 3 Value Creation and Advanced Analytics -- 3.1.The Wealth of Organizations and What Advanced Analytics Can Do -- 3.1.1.Information Capital -- 3.1.2.Constant and Unrelenting Experimentation -- 3.1.3.Gold in Them There Databases: Human Capital Data -- 3.1.4.Not Only Human Experts Are Prone to Biases -- 3.2.Value and How to Create It: Intangible Capital -- 3.2.1.Who Really Holds the Keys to the Kingdom -- 3.2.2.The Nature of the Organization -- 3.2.3.The Cost of Employee Turnover -- 3.3.Strategic Choice and Advanced Analytics -- 3.3.1.HCM Practice Choice and Advanced Analytics -- 3.3.2.Business Intelligence Alignment of HCM Practices and Policies with Business Strategy -- 3.3.3.Decision Science, Business Intelligence, and Implications for HCM Decisions -- 3.3.4.Machine Learning and HR Practice Choice -- 3.4.Software Applications, Analytics, and HR Decisions -- 3.4.1.Software Options and Optimal HCM Practice -- |
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Contents note continued: 3.4.2.Enterprise Resource Planning Software -- 3.4.3.Talent Analytics -- 3.4.4.SAS Business Intelligence -- 3.4.5.Talent Scorecard -- 3.4.6.Talent Management Suites and Advanced Analytics -- ch. 4 Human Science and Selection Decisions -- 4.1.Optimizing Selection and Promotion Decisions -- 4.1.1.Performance and Selection -- 4.1.2.Making the Unobservable Observable -- 4.1.3.Eliminating Biases from Selection Decisions -- 4.1.4.Human Science and Employee Selection -- 4.1.5.Skills Shortages -- 4.2.Workforce Planning, Talent Acquisition, and Decision Analytics -- 4.2.1.Workforce Planning and Predictive Analytics -- 4.2.2.When Is Workforce Planning Necessary? -- 4.2.3.Challenges with Forecasting -- 4.2.4.External Big Data and Employee Recruitment and Selection -- 4.3.Human Science and Selection and Promotions Decisions -- 4.3.1.What We Have to Learn from the Use of Advanced Analytics for Player Selection in Professional Sports -- |
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Contents note continued: 4.3.2.Biases and the Selection Decision -- 4.3.3.Selection Tools: Augmented Biographical Survey -- 4.3.4.Challenges with the Use of Bio Data -- 4.4.Applications of Human Science to Selection Decisions -- 4.4.1.The Application of Expert Intuition to Selection and Promotion Decisions -- 4.4.2.Applied Game Theory and Selection Decisions -- 4.4.3.Deep Q&A Expert Systems and Selection Decisions -- 4.4.4.Predictive Modeling and Selections Decisions -- 4.4.5.Applied Econometric and Machine Learning Techniques -- ch. 5 Human Science and Incentives -- 5.1.Human Science and Incentives -- 5.1.1.Incentives, Motivation, and Human Science -- 5.1.2.Incentive Contracts -- 5.1.3.Collaboration and Tournament Compensation Do Not Go Together -- 5.1.4.We Get What We Pay For -- 5.2.Human Science and Motivation -- 5.3.Performance Management -- 5.3.1.Biases Impacting Performance Management and Compensation Decisions -- 5.3.2.Strategy Maps and Performance Management -- |
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Contents note continued: 5.4.Applying Human Science to Incentive Contracts -- 5.4.1.Irrational, Cooperative, and Looking for Meaning -- 5.4.2.Complexity Theory and Incentive Contracts -- 5.4.3.The Application of Expert Intuition to Incentive and Motivation Issues -- 5.4.4.Applied Game Theory and Incentive Contracts -- 5.4.5.Deep Q & A Expert Systems and Incentive Contract Decisions -- 5.4.6.Predictive Modeling and Incentive Contracts -- 5.4.7.Applied Econometric and Machine Learning Techniques -- 5.5.Application of Human Science to Specific Incentive Issues -- 5.5.1.Executive Compensation -- 5.5.2.Other Possible Human Science Incentive Applications -- Garbage In -- Our Argumentative Natures -- Advanced Analytics and Diagnosis of HCM Issues -- The Science (and Art) of Prediction -- The Challenges with Being Empirically Declarative -- Decision-Making Authority and Cooperation -- Sharing Control and Return Rights -- Individualization |
| Summary |
"Dramatically improve human capital management decisions by applying advanced analytics and "Big Data" technologies and processes! Pioneering HR technology expert James Sesil identifies widespread flaws in today's HR decision-making processes, and reveals how advanced analytics can help organizations make far more robust decisions about employee selection, performance management, strategy alignment, collaboration, and more. In this book he shows how to integrate Business Intelligence, ERP, Strategy Maps, Talent Management Suites, and advanced analytics -- and use them together to make far better decisions. You'll learn how to measure and improve the value of HCM decision-making in workforce/succession planning, talent acquisition, career development, corporate learning, and beyond. Sesil teaches key lessons from sources ranging from online dating services to Moneyball-style sports player selection processes. He shows how to systematically improve decision-making through more complete and sophisticated collaboration and new Collective Intelligence approaches. You'll learn how to use both internal and external data sources more effectively, and review a wide variety of advanced tools now available from vendors such as OrcaEyes, Vemo, Aruspex, Peoplefluent, Infor/Lawson, DecisionLens, Oracle, Ultimate, Cogniti, IBM, SAP, and Microsoft. Sesil concludes by demonstrating how to build "data driven" cultures and organizations that truly want to bring objectivity to decision-making, and will actually use these remarkable new tools. This book will be an invaluable resource for every HR executive, manager, analyst, and IT professional seeking new opportunities for competitive advantage through human capital and technological innovation." -- Publisher's description |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references (pages 133-148) and index |
| Subject |
Personnel management -- Data processing.
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Personnel management -- Decision making.
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| LC no. |
2013943653 |
| ISBN |
9780133064605 |
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0133064603 |
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