國立虎尾科技大學 |

Optimizing Data-to-Learning-to-Action = The Modern Approach to Continuous Performance Improvement for Businesses /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimizing Data-to-Learning-to-Action/ by Steven Flinn.
Reminder of title:	The Modern Approach to Continuous Performance Improvement for Businesses /
Author:	Flinn, Steven.
Description:	XIX, 191 p. 57 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-1-4842-3531-7
ISBN:	9781484235317

Optimizing Data-to-Learning-to-Action = The Modern Approach to Continuous Performance Improvement for Businesses /
Flinn, Steven.

Optimizing Data-to-Learning-to-ActionThe Modern Approach to Continuous Performance Improvement for Businesses /[electronic resource] :by Steven Flinn. - 1st ed. 2018. - XIX, 191 p. 57 illus.online resource.

Chapter 1: Case for Action -- Chapter 2: Roots of a New Approach -- Chapter 3: Data-to-Learning-to-Action -- Chapter 4: Tech Stuff and Where It Fits -- Chapter 5: Reversing the Flow: Decision-to-Data -- Chapter 6: Quantifying the Value -- Chapter 7: Total Value -- Chapter 8: Optimizing Learning Throughput -- Chapter 9: Patterns of Learning Constraints and Solutions -- Chapter 10: Organizing for Data-to-Learning-to-Action Success -- Chapter 11: Conclusion -- .

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. 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. 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.

ISBN: 9781484235317

Standard No.: 10.1007/978-1-4842-3531-7doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Optimizing Data-to-Learning-to-Action = The Modern Approach to Continuous Performance Improvement for Businesses /
LDR:04270nam a22003975i 4500 001 987126
003 DE-He213
005 20200706093600.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484235317 $9 978-1-4842-3531-7
024 7 $a 10.1007/978-1-4842-3531-7 $2 doi
035 $a 978-1-4842-3531-7
050 4 $a QA76.9.B45
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
100 1 $a Flinn, Steven. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1203111
245 1 0 $a Optimizing Data-to-Learning-to-Action $h [electronic resource] : $b The Modern Approach to Continuous Performance Improvement for Businesses / $c by Steven Flinn.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XIX, 191 p. 57 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1: Case for Action -- Chapter 2: Roots of a New Approach -- Chapter 3: Data-to-Learning-to-Action -- Chapter 4: Tech Stuff and Where It Fits -- Chapter 5: Reversing the Flow: Decision-to-Data -- Chapter 6: Quantifying the Value -- Chapter 7: Total Value -- Chapter 8: Optimizing Learning Throughput -- Chapter 9: Patterns of Learning Constraints and Solutions -- Chapter 10: Organizing for Data-to-Learning-to-Action Success -- Chapter 11: Conclusion -- .
520 $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. 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. 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.
650 0 $a Big data. $3 981821
650 0 $a Software engineering. $3 562952
650 1 4 $a Big Data. $3 1017136
650 2 4 $a Software Engineering/Programming and Operating Systems. $3 669780
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484235300
776 0 8 $i Printed edition: $z 9781484235324
776 0 8 $i Printed edition: $z 9781484246658
856 4 0 $u https://doi.org/10.1007/978-1-4842-3531-7
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)