Book DescriptionPublication Date: December 5, 2012 | ISBN-10: 1461460794 | ISBN-13: 978-1461460794 | Edition: 2013
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.


Editorial ReviewsFrom the Back CoverBusiness analytics is used to help people to make and execute rational decisions. 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 during all stages of decision-making and execution.

The domain of business analytics is becoming recognized and established as a distinct profession. Many companies have created specialized business analytics teams, and several educational institutions offer courses and degrees in analytics. The people in these groups draw upon Operations Research, Statistics, and the Information Technology practices for Business Intelligence, Analytics & Optimization. Practitioners, academics, and consultants are working craft the concepts, processes, and structures needed to establish business analytics capabilities in their specific organizations. This book offers a set of proven concepts, processes, and structures that can help organizations to set up and evolve their analytics capabilities.

The word "analytics" conjures up different images for different people depending on the function that they work in. Business and academic organizations share an enthusiastic appreciation of the realm of business analytics, but they do not necessarily have a common understanding of all that it comprises. This divergence presents organizational challenges in terms of organizational design, roles, skills development planning, educational needs, and career paths in analytics organizations. Effective application of analytics involves a confluence of traditional business, mathematical modeling and information technology capabilities. This book provides a framework for the effective interplay of these capabilities to go from ideas to execution.

The framework for business analytics is also used to embed the use of business analytics into the business culture. It lays out the approach for analytics and provides guidance on how to scale analytics and how to develop analytics teams. It offers a set of proven concepts, processes, and structures that show how organizations can set up and evolve their analytics capabilities in order to achieve benefits in their strategy and operations.

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. It includes several real world case studies on applying the concepts of business analytics to decision making to help the practitioner understand the framework and extend it to their specific need.

About the AuthorRahul Saxena is the Director in Advanced Services at Cisco Systems. He is an MBA from the A. B. Freeman School of Business at Tulane University in New Orleans. Rahul has worked in business analytics, operations management, and management consulting roles in the USA, India, and Latin America. Prior to assuming his current position at Cisco Systems, Rahul held various positions at McAfee, IBM and the Indian Railways. He has also 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.






Contents

1 A Framework for Business Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

A Brief History of Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Business: The Decision-Making and Execution Perspective. . . . . . . 4

Analytics: The Techniques Perspective. . . . . . . . . . . . . . . . . . . . . . . 5

IT: The Tools and Systems Perspective. . . . . . . . . . . . . . . . . . . . . . . 5

A Framework for Business Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Analytics Domain Context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Rational Decisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Decision Needs and Decision Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Models: Connecting Decision Needs to Analytics. . . . . . . . . . . . . . . . . . . 15

Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Roles: Connecting Stakeholders to Analytics. . . . . . . . . . . . . . . . . . . . . . . 17

3 Decision Framing: Defining the Decision Need . . . . . . . . . . . . . . . . . . . 19

Big Y, Little Y and Decision Framing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Decision Framing for Decision Layers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

The Airline Partnership Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Aligning the Layers: Tying the Decision Frame . . . . . . . . . . . . . . . . . . . . 27

Decision Frames Set Business Expectations . . . . . . . . . . . . . . . . . . . . . . . 28

4 Decision Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Types of Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Context Diagrams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Mathematical Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Big Data and Big Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Network Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Capability Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Control Systems Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Expertise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Learning by Asking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Learning by Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Value Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Optimization Systems Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Workflow Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Modeling Processes and Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 60

Modeling Assignment and Dispatch . . . . . . . . . . . . . . . . . . . . . . . . . 61

Modeling Events and Alerts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Transparency, Integrity, Validity and Security. . . . . . . . . . . . . . . . . . . . . . . 62

Deliverables from Decision Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5 Decision Making. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

The Role of the Decision Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

The Decision Making Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Set Context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Step 1: Frame. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Step 2: Debate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Step 3: Decide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Decision Making Roles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Biases, Emotions, and Bounded Rationality. . . . . . . . . . . . . . . . . . . . . . . . 74

Managing Irrationality: Removing Bias from Analytics. . . . . . . . . . . . . . . 76

6 Decision Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Align & Enable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Observe & Report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Communicate & Converse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7 Business Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

A Brief History of Data Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Business Intelligence for Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Business Intelligence in the Analytics Framework. . . . . . . . . . . . . . . . . . . 88

Data Sourcing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Transaction Processing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Benchmarks and External Data Sources. . . . . . . . . . . . . . . . . . . . . . 90

Survey Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Analytical Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Data Loading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Solve Data Quality IT Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Analytical Datasets and BI Assets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Operational Data Store. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Data Warehouse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Data Mart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Data Structuring and Transformation. . . . . . . . . . . . . . . . . . . . . . . . . 95

Business Analytics Input Databases. . . . . . . . . . . . . . . . . . . . . . . . . . 95

Business Analytics Ready Databases. . . . . . . . . . . . . . . . . . . . . . . . . 96

Analytics Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Dashboards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Modeling Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Spreadsheets and Microsoft Office Integration. . . . . . . . . . . . . . . . . 97

Data Stewardship and Meta Data Management. . . . . . . . . . . . . . . . . 98

Collaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Inline Analytics Tools Deployment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

8 Data Stewardship: Can We Use the Data?. . . . . . . . . . . . . . . . . . . . . . . 101

Initial Data Provision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

First-Cut Review of the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Sorts, Scatters and Histograms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Fitness for Use. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Privacy and Surveillance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Ongoing Data Provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Ongoing Data Sourcing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Ongoing Data Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Data Scrubbing and Enrichment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Data Scrubbing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Data Enrichment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

On Hierarchies, Tagging, and Categorizations. . . . . . . . . . . . . . . . . . 108

Manage Data Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Work with IT to Solve IT Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Work with Business to Solve Business Issues. . . . . . . . . . . . . . . . . . 111

Manage Data Dictionary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

9 Making Organizations Smarter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Why Bother with Analytics?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Analytics Culture Maturity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Actionable Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Measure the Value of Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Scaling the Decision Culture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Lies, Damn Lies and Statistics (or Analytics). . . . . . . . . . . . . . . . . . 118

Value Management: From Assessment to Realization . . . . . . . . . . . . . . . . 118

Make a Plan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Criticize the Plan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Execute the Plan, Re-assess at Checkpoints . . . . . . . . . . . . . . . . . . . 120

10 Building the Analytics Capability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Analytics Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Placing Analytics Capabilities in the Organization. . . . . . . . . . . . . . . . . . . 125

Analytics Team Skills and Capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Analytics Scheduling and Workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Tracking the Value of Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Analytics Maturity Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

11 Analytics Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Process Value Management (Experiment to Evolve) . . . . . . . . . . . . . . . . . 133

Capability Value Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Organizational Value Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Concept to Value Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Criteria for Selecting the Analytics Method. . . . . . . . . . . . . . . . . . . . . . . . 138

12 Analytics Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Case Study: Product Lifecycle and Replacement. . . . . . . . . . . . . . . . . . . . 142

Decision Framing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Data Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Data Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Decision Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Decision Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Case Study: Channel Partner Effectiveness. . . . . . . . . . . . . . . . . . . . . . . . . 146

Decision Framing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Data Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Data Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Decision Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Decision Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Case Study: Next Likely Purchase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Decision Framing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Data Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Data Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Decision Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Decision Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Case Study: Resource Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Decision Framing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Data Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Data Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Decision Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Decision Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

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