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|a 1484246659
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|a 9781484235324
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|a 1484235312
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|a 9781484246658
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|a 1484235320
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|a 9781484235317
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|a HD30.23
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|a Flinn, Steven
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|a Optimizing data-to-learning-to-action
|b the modern approach to continuous performance improvement for businesses
|c Steven Flinn
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|a [United States], New York, NY
|b Apress, Distributed to the Book trade worldwide by Springer
|c 2018
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|a 1 online resource
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|a 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
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|a Includes bibliographical references and index
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|a 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 ..
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|a 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
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|a 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
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|a 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
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|a BUSINESS & ECONOMICS / Management Science / bisacsh
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|a BUSINESS & ECONOMICS / Management / bisacsh
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|a Management information systems / http://id.loc.gov/authorities/subjects/sh85080359
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|a Decision making / Data processing
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|a Machine learning / fast
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|a Apprentissage automatique
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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|a BUSINESS & ECONOMICS / Organizational Behavior / bisacsh
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|a Systèmes d'information de gestion
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|a Management Information Systems
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|a Management information systems / fast
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|a Machine Learning
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|a BUSINESS & ECONOMICS / Industrial Management / bisacsh
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|a Software Engineering / bicssc
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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|a 10.1007/978-1-4842-3531-7
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|a 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 describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today's business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks.
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|a What You'll Learn: Understand data-to-learning-to-action processes and their fundamental elements Discover the highest leverage data-to-learning-to-action processes in your organization Identify the key decisions that are associated with a data-to-learning-to-action process Know why it's NOT all about data, but it IS all about decisions and learning Determine the value upside of enhanced learning that can improve decisions Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes Evaluate people, process, and technology-based solution options to address the constraints Quantify the expected value of each of the solution options and prioritize accordingly Implement, measure, and continuously improve by addressing the next constraints on value
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|a And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time. In today's dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value.
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