Analytics across the enterprise : how IBM realizes business value from big data and analytics
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書誌事項
Analytics across the enterprise : how IBM realizes business value from big data and analytics
IBM Press/Pearson plc, c2014
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Includes bibliographical references and index
内容説明・目次
内容説明
How to Transform Your Organization with Analytics: Insider Lessons from IBM's Pioneering Experience
Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn't just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won't happen overnight; however, it is absolutely achievable, and the rewards are immense.
This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM's pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn't work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business.
Coverage Includes
Creating a smarter workforce through big data and analytics
More effectively optimizing supply chain processes
Systematically improving financial forecasting
Managing financial risk, increasing operational efficiency, and creating business value
Reaching more B2B or B2C customers and deepening their engagement
Optimizing manufacturing and product management processes
Deploying your sales organization to increase revenue and effectiveness
Achieving new levels of excellence in services delivery and reducing risk
Transforming IT to enable wider use of analytics
"Measuring the immeasurable" and filling gaps in imperfect data
Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too.
Learn more about IBM Analytics
目次
- Foreword xix Preface xxi Chapter 1: Why Big Data and Analytics? 1 Why IBM Started an Enterprise-Wide Journey to Use Analytics 3 Big Data and Analytics Demystified 4 Descriptive and Predictive Analytics 5 Prescriptive Analytics 6 Social Media Analytics 6 Entity Analytics 7 Cognitive Computing 7 Big Data 8 Why Analytics Matters 9 Governance 10 Proven Approaches 12 Gauging Progress 13 Overview of Nine Journeys 14 Emerging Themes 15 How to Use This Book 17 Endnotes 18 Chapter 2: Creating a Smarter Workforce 21 Perspective: Applying Analytics to the Workforce 21 Challenge: Retaining High-Value Resources in Growth Markets 25 Outcome: Attrition Rate Declined
- Net Benefits Exceeded Expectations 26 Challenge: Gaining an Accurate View of What Employees Are Thinking 26 Outcome: Ability to Act on Real Insights About Employees 27 Lessons Learned 29 Endnotes 31 Chapter 3: Optimizing the Supply Chain 33 Perspective: Applying Analytics to the Supply Chain 33 Challenge: Detecting Quality Problems Early 36 Outcome: Significant Cost Savings, Improved Productivity, Improved Brand Value, and Two Awards 38 Challenge: Providing Supply/Demand Visibility and Improved Channel Inventory Management 39 Outcome: Reduced Price Protection Expense, Reduced Returns, and Two Industry Awards 41 Challenge: Improving the Accounts Receivable Business Process and Collector Productivity 41 Outcome: Better Visibility to Track the Total Receivables View Across the Entire Collection Process and Reduction in Labor Cost 43 Challenge: Predicting Disruptions in the Supply Chain 43 Outcome: Number of Listening Events Increased Tenfold and Local Language Listening Proved Valuable 44 Lessons Learned 45 Endnotes 48 Chapter 4: Anticipating the Financial Future 51 Perspective: Big Data and Analytics Increase Value of Finance Team 51 Getting the Basics in Place 52 Creating an Analytics Culture 53 Challenge: Attaining Operational Efficiency, Managing Risk, and Informing Decisions 55 Tracking Spending: The Worldwide Spend Project 55 Outcome: More Efficient and More Effective Spend Forecasting 57 Keeping Up with Reporting Requirements: The Accelerated External Reporting (AER) System 58 Outcome: Improved Statutory and Tax Reporting and Analytics 59 Challenge: Balancing Risk and Reward 59 Country Financial Risk Scorecard 59 Outcome: Country Financial Risk Scorecard Uses Big Data to Monitor Trends and Minimize Risk 61 Challenge: Validating Acquisition Strategy 62 The Mergers and Acquisitions Analytics Project 62 Outcome: Mergers and Acquisitions Analytics Improves Success Rate 62 The Smarter Enterprise Enablement (SEE) Initiative 64 Outcome: SEE Project Transforms Strategic Planning and Its Novel Approach Leads to Patent Applications 64 What's Next for IBM Finance? 64 Lessons Learned 65 Endnotes 66 Chapter 5: Enabling Analytics Through Information Technology 67 Perspective: Applying Analytics to IT and Enabling Big Data and Analytics Across an Enterprise 67 Challenge: Deciding When to Modernize Servers 69 Outcome: Increase in Application Availability 70 Challenge: Detecting Security Incidents 71 Outcome: Increased Detection of Security Incidents 71 Enabling the Transformation to a Smarter Enterprise 71 Developing Enterprise-Wide Big Data and Analytics Applications 71 Partnering with Business Areas to Develop Social Media Analytic Solutions for Customer-Centric Outcomes 73 Developing an Information Agenda and Processes for Governance and Security of Data 73 Providing a Big Data and Analytics Infrastructure 76 Lessons Learned 77 Endnotes 78 Chapter 6: Reaching Your Market 81 Perspective: Using Analytics to Reach and Engage with Clients 81 A Signature Client Experience 83 Marketing-Related Analytics Hiring Soaring 84 Agility Is Key 84 Challenge: Developing the Data Foundation and Analytics Capability to Enable a Signature Client Experience 85 Outcome: Individual Data Master to Provide Client-Level Insights 87 Challenge: Providing a Real-Time View into Effectiveness of Marketing Actions: Performance Management 87 Outcome: Marketing Efficiencies Realized and Transformation of Marketing Enabled 88 Challenge: Going Beyond Correlation to Determine Causal Effects of Marketing Actions 90 Outcome: System Deals with Special Terms and Conditions Added Grew from 67% to 98% over Three Quarters 90 Challenge: Tapping into Analytics Passion to Provide New Insights to Inform IBM's Digital Strategy 92 Outcome: Insights from Diverse Teams Provided the Evidence Needed to Make Changes to the Digital Strategy 93 Lessons Learned 94 Endnotes 94 Chapter 7: Measuring the Immeasurable 97 Perspective: Software Development Organization Optimizes the Highly Skilled Workforce 97 Challenge: Creating a Common View of Development Expense to Enable Decision Making 99 Development Expense Baseline Project 99 Outcome: Development Expense Baseline Project Proves That the Immeasurable Can Be Measured 105 Lessons Learned 105 Endnotes 106 Chapter 8: Optimizing Manufacturing 107 Perspective: Applying Analytics to Manufacturing and Product Management 107 Challenge: Scheduling a Complex Manufacturing Process in a Semiconductor Fab 108 Outcome: Reduced Production Times 111 Challenge: Enhancing Yield in the Manufacturing of Semiconductors 111 Outcome: Cost Savings Due to Yield Improvement 112 Challenge: Reducing the Time to Detect Aberrant Events 113 Outcome: Engineers Take Action 114 Challenge: Simplifying the Hardware Product Portfolio 115 Outcome: Significant Reduction of Hardware Product Portfolio 116 Lessons Learned 117 Endnotes 117 Chapter 9: Increasing Sales Performance 121 Perspective: Using Analytics to Optimize Sales Performance--Inside and Out 121 How IBM Approached Leveraging Analytics in Sales Organizations 122 Using Analytics to Build a Business Case for Inside Sales 123 Challenge: Deploying Sellers for Maximum Revenue Growth by Account 124 Outcome: Increased Sales Performance 126 Challenge: Deploying Sellers Within a Territory 126 Outcome: Increased Territory Performance 127 Challenge: Determining the Optimal Sales Coverage Investment by Account 128 Outcome: Increased Revenue and Increased Productivity 130 Online Commerce 130 Challenge: Creating a Smarter Commerce B2B Solution to Drive Cross-Company Efficiencies 132 Outcome: An Analytics-Based, Client-Focused Business Case Wins Approval 133 Lessons Learned 135 Endnotes 137 Chapter 10: Delivering Services with Excellence 139 Perspective: Leveraging Analytics in a Services Business 139 Challenge: Developing New Business 141 Outcome: Increased Signings, Revenue, and Pipeline 142 Challenge: Predicting Risk of Contracts 142 Outcome: Deployment of Financial Risk Analytics 143 Challenge: Optimizing Workforce Performance 143 Outcome: Large Cost Savings, Improved Productivity, and Faster Client Response Times 147 Challenge: Getting Early Warning About Problems 147 Outcome: Timely Intelligence to Delivery Teams to Help Satisfy Clients 148 Lessons Learned 148 Endnotes 149 Chapter 11: Reflections and a Look to the Future 151 The Journey Continues 151 Reflections 153 Transactional Data 155 Simulation 156 Alerts 157 Forecasting 158 The Future 159 Growth of Data 160 Unstructured Data 161 Cognitive Computing 163 Endnotes 164 Appendix A: Big Data and Analytics Use Cases 165 Glossary: Acronyms and Definitions of Key Big Data and Analytics Terms 175 Index 183
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