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Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics

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Features

  • Shows how to systematically infuse science into the art of business decision-making, leveraging today's latest advances in business analytics
  • Reflects powerful lessons learned inside and throughout IBM, as it has pioneered analytics across the enterprise
  • Focuses on one of today's best opportunities to improve competitiveness -- as an executive, manager, student, or organization

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  • Copyright 2014
  • Dimensions: 6" x 9"
  • Pages: 224
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-383303-8
  • ISBN-13: 978-0-13-383303-4

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

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Why Big Data and Analytics?

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Table of Contents

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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