Business Intelligence Strategy and Big Data Analytics

Opis

Business Intelligence Strategy in the Big Data Analytics: A General Management Perspective explains how to deliver competitive advantages and substantial economic benefits by overcoming commonly-encountered barriers to success. It includes lessons learned from leading companies that provide practical ideas for how to use the many different forms of BI to meet key business objectives. Further, it clarifies how BI initiatives are really business initiatives that require business units to change how they use information and analysis to drive and improve business results, particularly profits.

Business intelligence (BI) and business analytics are like a Swiss army knife—they can be used in many different ways to achieve many different business purposes. In working with leading companies in a wide range of industries to help them formulate and execute BI strategies and program plans, the author has seen firsthand that these successful companies struggle in two key areas: BI Strategy, which is understanding how they can leverage BI in core business functions such as marketing, sales, customer service, operations, distribution, supplier management, cost improvement, and financial management; and BI Program Execution, which is effectively aligning and executing the diverse workstreams that are critical for achieving a return on investment (ROI), including change management, business process and BI integration, and BI and data warehousing development.

Provides ideas for improving the business performance of one’s company or business functions
Emphasizes proven, practical, step-by-step methods that readers can readily apply in their companies
Includes exercises and case studies with road-tested advice about formulating BI strategies and program plans

Spis treści

Front Cover 2
Business Intelligence Strategy and Big Data Analytics 5
Copyright Page 6
Contents 7
About the Author 11
Foreword 13
Acknowledgments 17
Introduction 19
- The Challenge of Formulating Business Intelligence Strategy 19
- Overview of the Book 21
- Organization of the Book 22
- Closing the Loop 23
1 The Personal Face of Business Intelligence 25
- 1.1 BI Case Study Setting 25
  - 1.1.1 Industry Setting 25

Front Cover 2
Business Intelligence Strategy and Big Data Analytics 5
Copyright Page 6
Contents 7
About the Author 11
Foreword 13
Acknowledgments 17
Introduction 19
- The Challenge of Formulating Business Intelligence Strategy 19
- Overview of the Book 21
- Organization of the Book 22
- Closing the Loop 23
1 The Personal Face of Business Intelligence 25
- 1.1 BI Case Study Setting 25
  - 1.1.1 Industry Setting 25
  - 1.1.2 Company Situation 26
- 1.2 BBF BI Opportunities 26
  - 1.2.1 The CEO’s View of Business Challenges and BIOs 27
  - 1.2.2 The Chief Operating Officer’s View of Business Challenges and BIOs 28
  - 1.2.3 The Chief Marketing Officer’s View of Business Challenges and BIOs 30
  - 1.2.4 The Chief Sales Officer’s View of Business Challenges and BIOs 33
  - 1.2.5 The Chief Financial Officer’s View of Business Challenges and BIOs 35
  - 1.2.6 The CIO’s View of Business Challenges and BIOs 38
- 1.3 The BBF BI Vision and BI Opportunity Portfolio & Business Case 39
  - 1.3.1 The BBF BI Vision 40
  - 1.3.2 The BBF BIO Portfolio 42
- 1.4 Generalizing From the BBF Case—BI Applications for Manufacturers 44
- 1.5 Lessons Learned for BI Strategy—BBF BI Progress 47
  - 1.5.2 Lesson 2—The Mission and Importance of BI Is Not Clear 48
  - 1.5.3 Lesson 3—No Sense of Urgency Among Upper Management 48
  - 1.5.1 Lesson 1—Lack of Understanding of BI Makes the Value Hard to Determine 48
- 1.6 Questions to Consider for Your Company or Function 49
2 Business Intelligence in the Era of Big Data and Cognitive Business 51
- 2.1 Getting Clear About Terminology—Business Definitions of Business Intelligence and Related Terms 52
- 2.2 The Hype Around BI, Big Data, Analytics, and Cognitive Business 54
- 2.3 A Business View of Big Data 57
- 2.4 A Business View of Cognitive Business 59
- 2.5 BI and Analytics—Is There a Difference? 61
- 2.6 Beyond the Hype—What BI Success Looks Like 63
  - 2.6.1 Industry Views of BI Success 63
- 2.7 Summary—Industry Views of BI Success 71
  - 2.7.1 Job Function Views of BI Success 71
- 2.8 Recap of Some Key Points 73
3 The Strategic Importance of Business Intelligence 75
- 3.1 A Business View of BI 76
  - 3.1.1 Styles of BI 77
  - 3.1.2 An Effective BI Environment Provides Integrated Operational and Financial Views of Facts About Business Performance 79
- 3.2 How BI Enhances Business Processes and Business Performance 80
  - 3.2.1 Review of Business Processes Improvement Thinking 82
  - 3.2.2 Decision-Making Can Be a BI-Enabled, Defined Business Process 84
- 3.3 The Strategic Importance of BI 86
  - 3.3.1 Some Examples of the Strategic Importance of BI 87
    - 3.3.1.2 Grocery Stores 87
    - 3.3.1.3 Government Agencies 87
    - 3.3.1.1 Financial Services Industry 87
    - 3.3.1.4 Manufacturers 88
  - 3.4 Skill Development Opportunity: The Strategic Importance of BI 90
    - 3.4.1 Objectives 90
  - 3.5 Summary of Some Key Points 91
- 4 BI Opportunity Analysis 93
  - 4.1 BI Opportunity Analysis Provides the Economic Rationale for BI 93
  - 4.2 Top-Down BI Opportunity Analysis 96
  - 4.3 Using Strategy Maps to Discover Bios 99
  - 4.4 Using Structured Interviews to Discover BIOs 102
    - 4.4.1 Typical “Conversation Starters” for Structured Interviews 102
  - 4.5 Factoring in Big Data and Cognitive Business Opportunities 103
  - 4.6 Documenting BIOs 106
  - 4.7 Skill Improvement Opportunity: Discovering BIOs and Mapping to BI Styles 107
    - 4.7.2 Case Study Information (Sourced From Public Documents) 107
    - 4.7.1 Key Objectives 107
  - 4.8 Summary of Some Key Points 109
- 5 Prioritizing BI Opportunities (BIOs) 111
  - 5.1 BI Portfolio Planning and the BI Portfolio Map 111
    - 5.1.1 Business Impact Versus Execution Risk 111
    - 5.1.2 The BIO Portfolio Map (Also Known As BI Portfolio Map or BI Portfolio) 112
  - 5.2 Factors to Consider When Prioritizing BIOs 114
    - 5.2.1 Some Business Factors to Consider 114
    - 5.2.2 Some Technical Factors to Consider 115
  - 5.3 Approaches to Prioritizing BIOs 115
    - 5.3.1 Multiattribute Scoring Model With Voting 116
    - 5.3.2 Discounted Cash Flow ROI Model 117
  - 5.4 Skill Development Opportunity: Develop and Justify a BI Portfolio Map 119
    - 5.4.1 Key Objectives 119
    - 5.4.2 BIO Summaries 119
    - 5.4.3 BIO Execution Risk Summaries 121
  - 5.5 Summary of Some Key Points 122
- 6 Leveraging BI for Performance Management, Process Improvement, and Decision Support 123
  - 6.1 BI as a Key Enabler of BPM 124
    - 6.1.1 Characteristics of an Effective, BI-Enabled BPM System 126
    - 6.1.2 BPM System Example: BI-Enabled Production Performance Management 129
    - 6.1.3 Using a Performance Scorecard to Present Performance Variances 132
    - 6.1.4 Using BI to Analyze Unfavorable Performance Variances 134
    - 6.1.5 BI-Enabled BPM: A Tool for Decision Support 139
    - 6.1.6 BI Enhances Close-Looped BPM 142
    - 6.1.7 Summary: BI Enables Efficient and Effective BPM 142
  - 6.2 BI as a Key Enabler of Business Process Improvement 143
    - 6.2.1 BI Is a Key Tool in the Business Process Improvement Toolkit 145
    - 6.2.2 Determining How to Leverage BI for Business Process Improvement 147
    - 6.2.3 Leveraging BI for Improving Performance Management Processes 151
    - 6.2.4 Leveraging BI to Improve Revenue Generation Processes 155
      - 6.2.4.1 Leveraging BI for Enhanced Revenue Generation in the Financial Services Industry 155
      - 6.2.4.2 Leveraging BI for Enhanced Revenue Generation in the Consumer Packed Goods Industry 157
      - 6.2.4.3 Leveraging Big Data and Cognitive Business Techniques for Shopper Marketing in the Retail Industry 158
    - 6.2.5 Leveraging BI to Improve Operating Processes 159
      - 6.2.5.1 Leveraging BI to Enhance Operating Processes in the CPG Industry 161
      - 6.2.5.2 Leveraging BI to Enhance Operating Processes in the Grocery Industry 162
    - 6.2.6 Summary—Leveraging BI for Business Process Improvement 164
  - 6.3 BI as a Key Enabler of High-Impact Business Decisions 165
    - 6.3.1 The Evolution of Computer-Assisted Decision Support Systems 167
    - 6.3.2 BI as a Decision Support Tool 169
  - 6.4 Skill Development Opportunity 171
    - 6.4.1 Insert BI Into a Business Process 171
      - 6.4.1.1 Key Objectives 171
    - 6.4.2 Design a Performance Scorecard 172
      - 6.4.2.1 Key Objectives 172
    - 6.5 Summary of Some Key Points 173
  - 7 Meeting the Challenges of Enterprise BI 175
    - 7.1 A General Management View About BI Success 176
      - 7.1.1 Major Workstreams Required for Enterprise BI Success 176
      - 7.1.3 Identifying Risks and Barriers to Success 179
        
        7.1.3.1 Risk Factor #1—Ability to Align and Govern 183
        
        7.1.3.2 Risk Factor #2—Ability to Leverage 184
        
        7.1.3.3 Risk Factor #3—Ability to Execute 184
      - 7.1.2 Workstream Details 179
      - 7.1.4 Summary: General Management for BI Success 185
    - 7.2 Challenges for BI Success 185
      - 7.2.1 Challenge: Lack of a Business-Driven BI Strategy 186
        
        7.2.1.2 Link Between BIOs, Business Performance, and Business Process Improvement 186
        
        7.2.1.1 BI Mission 186
        
        7.2.1.3 BI Barriers and Risks 187
      - 7.2.2 Challenge: Higher IT Priorities Slow BI Deployment 187
      - 7.2.3 Challenge: Higher Priorities Impede Business Engagement 188
      - 7.2.4 Challenge: BI Enveloped by a Broader Data Management Initiative 190
      - 7.2.5 Challenge: BI Managed Under Typical IT Policies and Methods 193
        
        7.2.5.1 The IT Shared Services Mindset 193
        
        7.2.5.2 Best Practices Development Methodologies for IT Projects and BI Projects are Different 196
        
        7.2.5.3 What is Being Optimized? 197
      - 7.2.6 Challenge: Barriers to Data Access 198
      - 7.2.7 Summary—Challenges for BI Success 199
    - 7.3 Organizational Design for BI Success 199
      - 7.3.1 Organizational Approaches to BI 201
      - 7.3.2 Organizational Experimentation and Exploitation of Big Data and Cognitive Business Techniques 204
    - 7.4 Skill Development Opportunity: Assess BI Challenges, Risks, and Barriers 206
      - 7.4.2 Topic List: BI Challenges, Risks, and Barriers 206
      - 7.4.1 Key Objectives 206
    - 7.5 Summary of Some Key Points 207
  - 8 General Management Perspectives on Technical Topics 209
    - 8.1 The Technical Landscape for BI Program Execution 209
    - 8.2 Technical Infrastructure for BI 213
      - 8.2.1 IT Infrastructure for BI 213
        
        8.2.1.1 The Challenge of IT Infrastructure as a Shared Service to the BI Program 213
        
        8.2.1.2 Providing Autonomous and Dedicated IT Infrastructure Assets to the BI Program 215
        
        8.2.1.3 Considering Cloud-Based IT Infrastructure for BI 216
      - 8.2.2 BI Infrastructure for BI Programs 218
        
        8.2.2.1 Contribution to Competitive Differentiation 218
        
        8.2.2.4 BI and Data Warehousing Appliances 220
        
        8.2.2.2 Switching Costs 220
        
        8.2.2.3 Total Cost of Ownership (TCO) 220
      - 8.2.3 Big Data Technical Considerations 221
      - 8.2.4 Summary—Technical Infrastructure for BI 222
    - 8.3 Data Infrastructure for BI 223
      - 8.3.1 Establishing the Data Flow Value Chain for BI 224
      - 8.3.2 Designing the BI Data Infrastructure 225
        
        8.3.2.1 Business-Driven Data Architecture 226
        
        8.3.2.2 Methods for Providing BI to Business Users 228
      - 8.4 BI and the Cloud 229
      - 8.5 Summary 232
    - Bibliography 233
    - Index 235
    - Back Cover 242

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Steve Williams: Founder, DecisionPath Consulting, Gaithersbur

A General Management Perspective

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