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Analytics across the enterprise : how IBM realizes business value from big data and analytics

Author: Brenda L Dietrich; Emily C Plachy; Maureen F Norton; International Business Machines Corporation.
Publisher: Upper Saddle River, NJ : IBM Press, ©2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Analytics systematically informs human judgment with data-driven insight, improving decision-making and enabling greater innovation and creativity in support of strategy. This book hows how IBM has successfully leveraged analytics across the enterprise, worldwide. Through 31 case studies, the book shares real-world perspectives on what does and doesn't work and how to start or accelerate the business transformation.  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Brenda L Dietrich; Emily C Plachy; Maureen F Norton; International Business Machines Corporation.
ISBN: 9780133835922 0133835928 9780133835885 013383588X
OCLC Number: 881383301
Description: 1 online resource (1 volume) : illustrations
Contents: Foreword xixPreface xxiChapter 1: Why Big Data and Analytics? 1Why IBM Started an Enterprise-Wide Journey to Use Analytics 3Big 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 8Why Analytics Matters 9Governance 10Proven Approaches 12Gauging Progress 13Overview of Nine Journeys 14Emerging Themes 15How to Use This Book 17Endnotes 18Chapter 2: Creating a Smarter Workforce 21Perspective: Applying Analytics to the Workforce 21Challenge: Retaining High-Value Resources in Growth Markets 25 Outcome: Attrition Rate Declined; Net Benefits Exceeded Expectations 26Challenge: Gaining an Accurate View of What Employees Are Thinking 26 Outcome: Ability to Act on Real Insights About Employees 27Lessons Learned 29Endnotes 31Chapter 3: Optimizing the Supply Chain 33Perspective: Applying Analytics to the Supply Chain 33Challenge: Detecting Quality Problems Early 36 Outcome: Significant Cost Savings, Improved Productivity, Improved Brand Value, and Two Awards 38Challenge: Providing Supply/Demand Visibility and Improved Channel Inventory Management 39 Outcome: Reduced Price Protection Expense, Reduced Returns, and Two Industry Awards 41Challenge: 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 43Challenge: Predicting Disruptions in the Supply Chain 43 Outcome: Number of Listening Events Increased Tenfold and Local Language Listening Proved Valuable 44Lessons Learned 45Endnotes 48Chapter 4: Anticipating the Financial Future 51Perspective: Big Data and Analytics Increase Value of Finance Team 51 Getting the Basics in Place 52 Creating an Analytics Culture 53Challenge: 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 59Challenge: Balancing Risk and Reward 59 Country Financial Risk Scorecard 59 Outcome: Country Financial Risk Scorecard Uses Big Data to Monitor Trends and Minimize Risk 61Challenge: 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 64What's Next for IBM Finance? 64Lessons Learned 65Endnotes 66Chapter 5: Enabling Analytics Through Information Technology 67Perspective: Applying Analytics to IT and Enabling Big Data and Analytics Across an Enterprise 67Challenge: Deciding When to Modernize Servers 69 Outcome: Increase in Application Availability 70Challenge: Detecting Security Incidents 71 Outcome: Increased Detection of Security Incidents 71Enabling 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 76Lessons Learned 77Endnotes 78Chapter 6: Reaching Your Market 81Perspective: Using Analytics to Reach and Engage with Clients 81 A Signature Client Experience 83 Marketing-Related Analytics Hiring Soaring 84 Agility Is Key 84Challenge: Developing the Data Foundation and Analytics Capability to Enable a Signature Client Experience 85 Outcome: Individual Data Master to Provide Client-Level Insights 87Challenge: Providing a Real-Time View into Effectiveness of Marketing Actions: Performance Management 87 Outcome: Marketing Efficiencies Realized and Transformation of Marketing Enabled 88Challenge: 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 90Challenge: 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 93Lessons Learned 94Endnotes 94Chapter 7: Measuring the Immeasurable 97Perspective: Software Development Organization Optimizes the Highly Skilled Workforce 97Challenge: 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 105Lessons Learned 105Endnotes 106Chapter 8: Optimizing Manufacturing 107Perspective: Applying Analytics to Manufacturing and Product Management 107Challenge: Scheduling a Complex Manufacturing Process in a Semiconductor Fab 108 Outcome: Reduced Production Times 111Challenge: Enhancing Yield in the Manufacturing of Semiconductors 111 Outcome: Cost Savings Due to Yield Improvement 112Challenge: Reducing the Time to Detect Aberrant Events 113 Outcome: Engineers Take Action 114Challenge: Simplifying the Hardware Product Portfolio 115 Outcome: Significant Reduction of Hardware Product Portfolio 116Lessons Learned 117Endnotes 117Chapter 9: Increasing Sales Performance 121Perspective: 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 123Challenge: Deploying Sellers for Maximum Revenue Growth by Account 124 Outcome: Increased Sales Performance 126Challenge: Deploying Sellers Within a Territory 126 Outcome: Increased Territory Performance 127Challenge: Determining the Optimal Sales Coverage Investment by Account 128 Outcome: Increased Revenue and Increased Productivity 130Online Commerce 130Challenge: Creating a Smarter Commerce B2B Solution to Drive Cross-Company Efficiencies 132 Outcome: An Analytics-Based, Client-Focused Business Case Wins Approval 133Lessons Learned 135Endnotes 137Chapter 10: Delivering Services with Excellence 139Perspective: Leveraging Analytics in a Services Business 139Challenge: Developing New Business 141 Outcome: Increased Signings, Revenue, and Pipeline 142Challenge: Predicting Risk of Contracts 142 Outcome: Deployment of Financial Risk Analytics 143Challenge: Optimizing Workforce Performance 143 Outcome: Large Cost Savings, Improved Productivity, and Faster Client Response Times 147Challenge: Getting Early Warning About Problems 147 Outcome: Timely Intelligence to Delivery Teams to Help Satisfy Clients 148Lessons Learned 148Endnotes 149Chapter 11: Reflections and a Look to the Future 151The Journey Continues 151Reflections 153 Transactional Data 155 Simulation 156 Alerts 157 Forecasting 158The Future 159 Growth of Data 160 Unstructured Data 161 Cognitive Computing 163Endnotes 164Appendix A: Big Data and Analytics Use Cases 165Glossary: Acronyms and Definitions of Key Big Data and Analytics Terms 175Index 183
Responsibility: Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton.

Abstract:

Analytics systematically informs human judgment with data-driven insight, improving decision-making and enabling greater innovation and creativity in support of strategy. This book hows how IBM has successfully leveraged analytics across the enterprise, worldwide. Through 31 case studies, the book shares real-world perspectives on what does and doesn't work and how to start or accelerate the business transformation. 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; and "measuring the immeasurable" and filling gaps in imperfect data.

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"Few major high-tech companies eat their own dog food. But give IBM credit: It practices what it preaches. This book provides an inside look at how IBM has applied analytics to nine business Read more...

 
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