語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Optimizing data-to-learning-to-actio...
~
SpringerLink (Online service)
Optimizing data-to-learning-to-action = the modern approach to continuous performance improvement for businesses /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Optimizing data-to-learning-to-action/ by Steven Flinn.
其他題名:
the modern approach to continuous performance improvement for businesses /
作者:
Flinn, Steven.
出版者:
Berkeley, CA :Apress : : 2018.,
面頁冊數:
xix, 191 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
標題:
Decision making - Data processing. -
電子資源:
http://dx.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-action
the modern approach to continuous performance improvement for businesses /[electronic resource] :by Steven Flinn. - Berkeley, CA :Apress :2018. - xix, 191 p. :ill., digital ;24 cm.
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:
568242
Decision making
--Data processing.
LC Class. No.: HD30.23
Dewey Class. No.: 658.05
Optimizing data-to-learning-to-action = the modern approach to continuous performance improvement for businesses /
LDR
:03809nam a2200289 a 4500
001
925411
003
DE-He213
005
20180406140554.0
006
m d
007
cr nn 008maaau
008
190625s2018 cau s 0 eng d
020
$a
9781484235317
$q
(electronic bk.)
020
$a
9781484235300
$q
(paper)
024
7
$a
10.1007/978-1-4842-3531-7
$2
doi
035
$a
978-1-4842-3531-7
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
HD30.23
082
0 4
$a
658.05
$2
23
090
$a
HD30.23
$b
.F622 2018
100
1
$a
Flinn, Steven.
$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.
260
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2018.
300
$a
xix, 191 p. :
$b
ill., digital ;
$c
24 cm.
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
Decision making
$x
Data processing.
$3
568242
650
0
$a
Machine learning.
$3
561253
650
0
$a
Management information systems.
$3
561123
650
1 4
$a
Computer Science.
$3
593922
650
2 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 eBooks
856
4 0
$u
http://dx.doi.org/10.1007/978-1-4842-3531-7
950
$a
Professional and Applied Computing (Springer-12059)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入