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Business Analytics: A Practitioners Guide 2013 ed. [Kõva köide]

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Business Analytics: A Practitioners Guide 2013 ed.Suurem pilt
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Püsilink: https://www.kriso.ee/db/9781461460794.html
Märksõnad:
This book offers a practitioner's guide to the extensive and comprehensive use of data, data analysis, explanatory and predictive models and methods, and management based on facts, in support of business decisions and actions. Includes real-world case studies.

This book provides a guide to businesses on how to use analytics to help drive from ideas to execution. Analytics used in this way provides “full lifecycle support” for business and helps during all stages of management decision-making and execution.The framework presented in the book enables the effective interplay of business, analytics, and information technology (business intelligence) both to leverage analytics for competitive advantage and to embed the use of business analytics into the business culture. It lays out an approach for analytics, describes the processes used, and provides guidance on how to scale analytics and how to develop analytics teams. It provides tools to improve analytics in a broad range of business situations, regardless of the level of maturity and the degree of executive sponsorship provided.As a guide for practitioners and managers, the book will benefit people who work in analytics teams, the managers and leaders who manage, use and sponsor analytics, and those who work with and support business analytics teams.

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From the book reviews:

In Business Analytics: A Practitioners Guide, Rahul Saxena and Anand Srinivasan show an intimate understanding of this challenge. They provide background material on decision making in organizations, outline challenges that these organization face, and then point out methods for overcoming the challenges. With so many organizations trying to build analytical capabilities, this book is an excellent resource for those early in their journey. It will help them understand more clearly what is ahead and the pitfalls to avoid. (Jack Levis, Interfaces, Vol. 44 (6), November-December, 2014)

1 A Framework for Business Analytics
1(8)
A Brief History of Analytics
3(3)
Business: The Decision-Making and Execution Perspective
4(1)
Analytics: The Techniques Perspective
5(1)
IT: The Tools and Systems Perspective
5(1)
A Framework for Business Analytics
6(3)
2 Analytics Domain Context
9(10)
Rational Decisions
9(1)
Decision Needs and Decision Layers
10(5)
Models: Connecting Decision Needs to Analytics
15(2)
Stakeholders
17(1)
Roles: Connecting Stakeholders to Analytics
17(2)
3 Decision Framing: Defining the Decision Need
19(12)
Big Y, Little Y and Decision Framing
19(3)
Decision Framing for Decision Layers
22(5)
The Airline Partnership Model
23(4)
Aligning the Layers: Tying the Decision Frame
27(1)
Decision Frames Set Business Expectations
28(3)
4 Decision Modeling
31(36)
Types of Models
32(4)
Context Diagrams
33(1)
Data Visualization
34(1)
Mathematical Models
35(1)
Big Data and Big Models
36(1)
Network Models
37(6)
Capability Models
43(4)
Control Systems Modeling
47(12)
Expertise
47(2)
Learning by Asking
49(2)
Learning by Experiment
51(2)
Value Improvement
53(5)
Optimization Systems Modeling
58(1)
Workflow Modeling
59(3)
Modeling Processes and Procedures
60(1)
Modeling Assignment and Dispatch
61(1)
Modeling Events and Alerts
62(1)
Transparency, Integrity, Validity and Security
62(1)
Deliverables from Decision Modeling
63(4)
5 Decision Making
67(12)
The Role of the Decision Modeler
68(1)
The Decision Making Method
69(4)
Set Context
70(1)
Decision Process
71(1)
Step 1 Frame
72(1)
Step 2 Debate
72(1)
Step 3 Decide
72(1)
Decision Making Roles
73(1)
Biases, Emotions, and Bounded Rationality
74(2)
Managing Irrationality: Removing Bias from Analytics
76(3)
6 Decision Execution
79(6)
Align & Enable
79(2)
Observe & Report
81(1)
Communicate & Converse
82(3)
7 Business Intelligence
85(16)
A Brief History of Data Infrastructure
85(2)
Business Intelligence for Analytics
87(1)
Business Intelligence in the Analytics Framework
88(2)
Data Sourcing
90(2)
Transaction Processing Systems
90(1)
Benchmarks and External Data Sources
90(1)
Survey Tools
91(1)
Analytical Output
92(1)
Data Loading
92(1)
Solve Data Quality IT Issues
93(1)
Analytical Datasets and BI Assets
93(3)
Operational Data Store
94(1)
Data Warehouse
94(1)
Data Mart
94(1)
Data Structuring and Transformation
95(1)
Business Analytics Input Databases
95(1)
Business Analytics Ready Databases
96(1)
Analytics Tools
96(2)
Reporting
96(1)
Dashboards
97(1)
Data Visualization
97(1)
Modeling Capabilities
97(1)
Spreadsheets and Microsoft Office Integration
97(1)
Data Stewardship and Meta Data Management
98(1)
Collaboration
98(1)
Inline Analytics Tools Deployment
98(3)
8 Data Stewardship: Can We Use the Data?
101(12)
Initial Data Provision
101(3)
First-Cut Review of the Data
102(1)
Sorts, Scatters and Histograms
102(1)
Fitness for Use
103(1)
Privacy and Surveillance
104(1)
Ongoing Data Provision
104(1)
Ongoing Data Sourcing
104(1)
Ongoing Data Assessment
105(1)
Data Scrubbing and Enrichment
105(5)
Data Scrubbing
106(1)
Data Enrichment
106(2)
On Hierarchies, Tagging, and Categorizations
108(2)
Manage Data Problems
110(1)
Work with IT to Solve IT Issues
110(1)
Work with Business to Solve Business Issues
111(1)
Manage Data Dictionary
111(2)
9 Making Organizations Smarter
113(10)
Why Bother with Analytics?
113(1)
Analytics Culture Maturity
114(2)
Actionable Analytics
116(2)
Measure the Value of Analytics
117(1)
Scaling the Decision Culture
118(1)
Lies, Damn Lies and Statistics (or Analytics)
118(1)
Value Management: From Assessment to Realization
118(5)
Make a Plan
119(1)
Criticize the Plan
119(1)
Execute the Plan, Re-assess at Checkpoints
120(3)
10 Building the Analytics Capability
123(10)
Analytics Ecosystem
123(2)
Placing Analytics Capabilities in the Organization
125(1)
Analytics Team Skills and Capacity
126(3)
Analytics Scheduling and Workflow
129(1)
Tracking the Value of Analytics
130(1)
Analytics Maturity Model
130(3)
11 Analytics Methods
133(8)
Process Value Management (Experiment to Evolve)
133(2)
Capability Value Management
135(1)
Organizational Value Management
135(2)
Concept to Value Realization
137(1)
Criteria for Selecting the Analytics Method
138(3)
12 Analytics Case Studies
141(16)
Case Study: Product Lifecycle and Replacement
142(4)
Decision Framing
142(1)
Data Collection
143(1)
Data Assessment
143(1)
Decision Modeling
143(2)
Decision Making
145(1)
Decision Execution
145(1)
Case Study: Channel Partner Effectiveness
146(2)
Decision Framing
146(1)
Data Collection
146(1)
Data Assessment
147(1)
Decision Modeling
147(1)
Decision Making
148(1)
Decision Execution
148(1)
Case Study: Next Likely Purchase
148(4)
Decision Framing
148(1)
Data Collection
149(1)
Data Assessment
149(1)
Decision Modeling
150(1)
Decision Making
151(1)
Decision Execution
151(1)
Case Study: Resource Management
152(5)
Decision Framing
153(1)
Data Collection
153(1)
Data Assessment
154(1)
Decision Modeling
155(1)
Decision Making
155(1)
Decision Execution
156(1)
References 157(2)
Index 159
Rahul Saxena is the Director for Smart Global Delivery Transformation & Operations at Cisco Advanced Services. He is an MBA from the A.B. Freeman School of Business at Tulane University. Rahul has worked on operations management, process improvement, and analytics in India, USA, and Latin America. Prior to assuming his current position at Cisco Systems, Inc., Rahul held various positions at IBM, McAfee and the Indian Railways. He has extensive speaking experience, most recently as a panel speaker at the INFORMS Annual Conference in San Diego 2009, and has co-authored an IBM Redbook on Business Architecture.

Anand Srinivasan is the founder and CEO of Dsquare Solutions, a boutique analytics services and consulting firm. He holds a BS degree in Chemical Engineering from the Indian Institute of Technology and an MS (Industrial Engineering) from Purdue University. Prior to assuming his current position Anand held various positions at Sabre Airline Solutions, Mu Sigma Business Solutions and Dell, all of them focused on building state of the art business analytics and optimization solutions.