Enterprise Performance Intelligence and Decision Patterns [Kõva köide]

(Corporate IT Strategy Consultant, Thane (West), India)
  • Formaat: Hardback, 262 pages, kõrgus x laius: 234x156 mm, kaal: 548 g, 50 Illustrations, black and white
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Auerbach Publishers Inc.
  • ISBN-10: 1498784690
  • ISBN-13: 9781498784696
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Enterprise Performance Intelligence and Decision PatternsSuurem pilt
  • Formaat: Hardback, 262 pages, kõrgus x laius: 234x156 mm, kaal: 548 g, 50 Illustrations, black and white
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Auerbach Publishers Inc.
  • ISBN-10: 1498784690
  • ISBN-13: 9781498784696
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Püsilink: https://www.kriso.ee/db/9781498784696.html
Märksõnad:
"Vivek Kale has written a great book on performance management that focuses on decision-making; on continuous, incremental improvement; and on identifying common patterns in becoming a more intelligent organization." James Taylor, CEO of Decision Management Solutions and author of Real-World Decision Modeling with DMN

"Introducing the concepts of decision patterns and performance intelligence, Vivek Kale has written another important book on the issues faced by contemporary organizations."Gary Cokins, author of Predictive Business Analytics and Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics

Enterprise Performance Intelligence and Decision Patterns unravels the mystery of enterprise performance intelligence (EPI) and explains how it can transform the operating context of business enterprises. It provides a clear understanding of what EPI means, what it can do, and application areas where it is practical to use.

The need to be responsive to evolving customer needs and desires creates organizational structures where business intelligence (BI) and decision making is pushed out to operating units that are closest to the scene of the action. Closed-loop decision making resulting from a combination of on-going performance management with on-going BI can lead to an effective responsive enterprise; hence, the need for performance intelligence (PI).

This pragmatic book:

Introduces the technologies such as data warehousing, data mining, analytics, and business intelligence systems that are a first step toward enabling data-driven enterprises.





Details decision patterns and performance decision patterns that pave the road for performance intelligence applications. Introduces the concepts, principles, and technologies related to performance measurement systems. Describes the concepts and principles related to balance scorecard systems (BCS). Introduces aspects of performance intelligence for the real-time enterprises.

Enterprise Performance Intelligence and Decision Patterns shows how a company can design and implement instruments ranging from decision patterns to PI systems that can enable continuous correction of business unit behavior so companies can enhance levels of productivity and profitability.
List of Figures
xiii
List of Tables
xv
Preface xvii
Acknowledgments xxi
Author xxiii
Other Books xxv
Vivek Kale
Chapter 1 Data-Driven Performance
1(12)
1.1 Agile Enterprises
1(10)
1.1.1 Stability versus Agility
4(3)
1.1.2 Aspects of Agility
7(1)
1.1.3 Principles of Agile Systems
8(1)
1.1.4 Framework for Agile Proficiency
9(2)
1.2 Data-Driven Performance Management
11(1)
1.3 Summary
12(1)
Chapter 2 Aligning Business and IT Strategy
13(22)
2.1 Business Strategy
14(8)
2.1.1 Evolution of Strategy Management
14(2)
2.1.2 Sustaining Competitive Advantage
16(2)
2.1.2.1 Porter's Five Forces Model
18(1)
2.1.2.2 Porter's Framework of Generic Strategies
18(3)
2.1.2.3 Porter's Value Chain
21(1)
2.2 Information Technology and Information Systems (IT/IS)
22(8)
2.2.1 Evolution of IT
22(3)
2.2.2 Evolution of IS
25(2)
2.2.3 Alignment of IT/IS with Business Strategy
27(3)
2.3 Summary
30(5)
Part I Genesis of Performance Intelligence
Chapter 3 Decision Support Systems
35(24)
3.1 Decisions
35(5)
3.1.1 Types of Decisions
38(2)
3.1.2 Scope of Decisions
40(1)
3.2 Decision-Making Process
40(3)
3.3 Decision-Making Techniques
43(3)
3.3.1 Mathematical Programming
43(1)
3.3.2 Multi-Criteria Decision-Making
44(1)
3.3.3 Case-Based Reasoning
44(1)
3.3.4 Data Warehouse and Data Mining
45(1)
3.3.5 Decision Tree
45(1)
3.3.6 Fuzzy Sets and Systems
46(1)
3.4 Decision Support Systems
46(10)
3.4.1 Multi-Source Driven DSS
46(3)
3.4.2 Generic DSS Architecture
49(7)
3.5 Summary
56(3)
Part II Road to Performance Intelligence
Chapter 4 Database Systems
59(20)
4.1 Database Management System
59(3)
4.1.1 DBMS Benefits
61(1)
4.2 Database Models
62(14)
4.2.1 Relational Database Model
63(3)
4.2.2 Hierarchical Database Model
66(3)
4.2.3 Network Database Model
69(1)
4.2.4 Object-Oriented Database Models
70(1)
4.2.5 Comparison of Models
71(1)
4.2.5.1 Similarities
71(4)
4.2.5.2 Dissimilarities
75(1)
4.3 Database Components
76(2)
4.3.1 Conceptual Level
76(1)
4.3.2 Logical Level
77(1)
4.3.3 Physical Level
78(1)
4.4 Summary
78(1)
Chapter 5 Data Warehousing Systems
79(22)
5.1 Relevant Database Concepts
79(3)
5.1.1 Physical Database Design
81(1)
5.2 Data Warehouse
82(13)
5.2.1 Multidimensional Model
85(1)
5.2.1.1 Data Cube
86(1)
5.2.1.2 Online Analytical Processing (OLAP)
87(3)
5.2.2 Relational Schemas
90(2)
5.2.3 Multidimensional Cube
92(3)
5.3 Data Warehouse Architecture
95(4)
5.3.1 Architecture Tiers
95(1)
5.3.1.1 Back-End Tier
95(1)
5.3.1.2 Data Warehouse Tier
96(2)
5.3.1.3 OLAP Tier
98(1)
5.3.1.4 Front-End Tier
98(1)
5.4 Summary
99(2)
Chapter 6 Data Mining Systems
101(20)
6.1 Data Mining
102(5)
6.1.1 Benefits
105(2)
6.2 Data Mining Applications
107(2)
6.3 Data Mining Analysis
109(4)
6.3.1 Supervised Analysis
109(1)
6.3.1.1 Exploratory Analysis
109(1)
6.3.1.2 Classification
110(1)
6.3.1.3 Regression
111(1)
6.3.1.4 Time Series
112(1)
6.3.2 Unsupervised Analysis
112(1)
6.3.2.1 Association Rules
112(1)
6.3.2.2 Clustering
112(1)
6.3.2.3 Description and Visualization
113(1)
6.4 CRISP-DM Methodology
113(7)
6.5 Summary
120(1)
Chapter 7 Analytics Systems
121(18)
7.1 Analytics
121(2)
7.1.1 Descriptive Analytics
122(1)
7.1.2 Predictive Analytics
122(1)
7.1.3 Prescriptive Analytics
123(1)
7.2 Data Science Techniques
123(10)
7.2.1 Database Systems
124(1)
7.2.2 Statistical Inference
124(1)
7.2.3 Regression and Classification
125(2)
7.2.4 Data Mining and Machine Learning
127(1)
7.2.5 Data Visualization
127(1)
7.2.6 Text Analytics
128(2)
7.2.7 Time Series and Market Research Models
130(3)
7.3 Snapshot of Data Analysis Techniques and Tasks
133(4)
7.4 Summary
137(2)
Chapter 8 Business Intelligence Systems
139(14)
8.1 Concept of Business Intelligence
139(1)
8.2 Business Intelligence
140(1)
8.3 Benefits of BI
140(3)
8.4 Technologies of BI
143(4)
8.4.1 Data Warehousing and Data Marts
143(1)
8.4.2 Data Mining
143(1)
8.4.3 Online Analytical Process
144(1)
8.4.4 Business Intelligence
145(2)
8.5 Applications of BI
147(2)
8.6 Summary
149(4)
Part III Performance Intelligence
Chapter 9 Decision Patterns
153(16)
9.1 Concept of Patterns
153(4)
9.2 Domain-Specific Decision Patterns
157(11)
9.2.1 Financial Decision Patterns
157(4)
9.2.2 CRM Decision Patterns
161(3)
9.2.2.1 CRM Decision Patterns through Data Mining
164(4)
9.3 Summary
168(1)
Chapter 10 Performance Decision Patterns
169(16)
10.1 Performance
169(1)
10.2 Cost Performance Drivers
170(6)
10.2.1 Company Performance
170(2)
10.2.2 Cost Performance
172(2)
10.2.3 Productivity Performance
174(2)
10.3 Noncost Performance Drivers
176(8)
10.3.1 Quality Performance
176(2)
10.3.1.1 Engineering and Manufacturing Internal Quality
178(1)
10.3.1.2 Purchasing and Vendor Quality Rating
178(1)
10.3.1.3 Quality Costs
179(1)
10.3.2 Time Performance
180(3)
10.3.3 Flexibility Performance
183(1)
10.4 Summary
184(1)
Chapter 11 Performance Intelligence
185(18)
11.1 Performance Intelligence
185(2)
11.2 PI Methodology
187(6)
11.2.1 PI Approaches
188(5)
11.3 Agile Principles
193(1)
11.4 PI Delivery Framework
194(5)
11.4.1 Discovery
194(1)
11.4.2 Architecture
195(1)
11.4.3 Design
196(1)
11.4.4 Development
197(1)
11.4.5 Testing
198(1)
11.4.6 Deployment
198(1)
11.5 Summary
199(4)
Part IV Performance Intelligence Applications
Chapter 12 Performance Intelligence Systems
203(12)
12.1 Performance Measurement System
204(3)
12.1.1 Performance Indicators and Measures
204(1)
12.1.2 PMS Architecture
205(1)
12.1.3 PMS Interfaces
206(1)
12.1.4 PMS Models
207(1)
12.2 Corporate Performance Management Framework
207(2)
12.2.1 Planning and Forecasting
207(1)
12.2.2 Balanced Scorecard and Dashboard
208(1)
12.2.3 Profitability and Cost Management
208(1)
12.2.4 Group Reporting and Financial Consolidation
208(1)
12.3 CPM Software
209(2)
12.3.1 SAP Business Objects
210(1)
12.3.2 Oracle Hyperion
210(1)
12.3.3 IBM Cognos
210(1)
12.3.4 SAS
211(1)
12.3.5 Microsoft
211(1)
12.4 CPM Project Enablers
211(2)
12.5 Summary
213(2)
Chapter 13 Balance Scorecard Systems
215(10)
13.1 Balance Scorecard (BSC)
215(6)
13.1.1 Financial Perspective
219(1)
13.1.2 Customer Perspective
220(1)
13.1.3 Internal Business Processes Perspective
220(1)
13.1.4 Learning and Growth Perspective
220(1)
13.2 Implementing a BSC
221(3)
13.3 Summary
224(1)
Chapter 14 Performance Intelligence for the Real-Time Enterprise
225(16)
14.1 Real-Time Enterprise
226(4)
14.2 Real-Time Performance Measurement
230(5)
14.2.1 Activity-Based Costing
233(1)
14.2.2 Resource Consumption Accounting
234(1)
14.3 Events
235(2)
14.3.1 Event-Driven Architecture
236(1)
14.3.2 Complex Event Processing
236(1)
14.4 Reference Architecture
237(1)
14.5 Financial Metrics
238(1)
14.6 Operational Metrics
239(1)
14.7 Summary
240(1)
Epilogue 241(8)
References 249(4)
Index 253
Vivek Kale has more than two decades of professional IT experience during which he has handled and consulted on various aspects of enterprise-wide information modeling, enterprise architectures, business process redesign, and, e-business architectures. He has been CIO of Essar Group, the steel/oil and gas major of India, as well as, Raymond Ltd., the textile and apparel major of India. He is a seasoned practitioner in transforming the business of IT, facilitating business agility and enabling the Process Oriented Enterprise. He is the author of Implementing SAP CRM: The Guide for Business and Technology Managers, Chapman & Hall (2014) and Guide to Cloud Computing for Business and Technology Managers: From Distributed Computing to Cloudware Applications, Chapman & Hall (2015).