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Optimizing data-to-learning-to-action : the modern approach to continuous performance improvement for businesses /
Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization's data-to-learning-to-action processes. This book descri...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[United States] :
Apress,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Case for Action; The Economic Imperative; Disruptive Technologies; Cloud Computing; Internet of Things; Big Data and Business Intelligence; Machine Learning/AI; Enterprise Collaboration; The Combinatorial Effect; The Confusion; Why Have Legacy Approaches Come up Short?; The Case for Data-to-Learning-to-Action; Summary; Chapter 2: Roots of a New Approach; Root 1: Process Improvement; Root II: Theory of Constraints; Root III: Decision Science; The Emergence of the New Approach; Summary.
- Chapter 3: Data-to-Learning-to-ActionElements of the Chain; Data Acquisition; Data Filtering; Information Management; The Architecture of Learning; Anticipatory Computing; Neural Networks; Information, Knowledge, and Learning; Search and Discovery; Predictive Analytics; Process and Collaborate; Decide and Act; Summary; Chapter 4: Tech Stuff and Where It Fits; General-Purpose Applications; Document and Content Management; Business Intelligence; Enterprise Social Networks; Function-Specific Applications; R & D: High-Throughput Experimentation Technologies.
- Sales and Marketing: Marketing AnalyticsSales and Marketing: Customer Relationship Management; Supply and Manufacturing: Supply Chain Management; Human Resources: Talent Management; Summary; Chapter 5: Reversing the Flow: Decision-to-Data; Value Drivers; Decisions, Decisions; Working Backward; Constraints on Value; Resolving Constraints; Summary; Chapter 6: Quantifying the Value; Value of Learning; Calculating Learning Value; Encoding Uncertainties; Value of Learning with Perfect Predictability; Value of Learning with Imperfect Predictability; Modeling Refinements; Learning Value Modeling.
- Alternative Modeling PerspectivesThe Bayesian Approach; Summary; Chapter 7: Total Value; Total Value; Business-Renewal Lifecycle; Optimizing Total Value; Retrospective Value; Summary; Chapter 8: Optimizing Learning Throughput; Additional Strategies; Parallel Versus Sequential Debottlenecking; Learning Synergies; Constraint Look-ahead; Anticipated Capabilities; Rationalizing to the Limiting Constraint; Full Method Overview; Summary; Chapter 9: Patterns of Learning Constraints and Solutions; Determining the Value; Process and Collaborate Constraints; Predictive Analytics Constraints.
- Search and Discovery ConstraintsInformation Management Constraints; Data Filtering Constraints; Data Acquisition Constraints; Summary; Chapter 10: Organizing for Data-to-Learning-to-Action Success; Scoping the Project; Gaining Executive Buy-in; Organizing the Initiative; Dedicated Resources; Phasing an Individual Project; Change Management: What's in It for Me?; Measuring Success; Skeptics to Champions; We already do that . . .; It's too much work . . .; It doesn't apply to the creative parts of the organization ...