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Digitizing the World

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  • 1. Digitizing the World The Driving Force BehindHadoop’s Adoption Life Cycle Hadoop Summit 2012
  • 2. Digital Innovation Decades of Disruption to Business as Usual •  1980s: Digital Office Work •  Personal Computing, Office Automation20th Century •  B2E – documents, office mail, presentations, financial analysis •  1990s: Digital Value Chains •  Internet, ERP, Worldwide Web, e-Commerce •  B2B – outsourcing, globalization, business networks •  2000s: Digital Consumption •  eTail, Broadband, Mobile, Social Media, Self-Service21st Century •  B2C – media, entertainment, advertizing, entertainment, retail, travel •  2010s: Digital Engagement •  Marketing, Customer Service & Support, Account Management •  B2B2C – business, health care, education, local government 2
  • 3. Enterprise IT: Pre-DisruptionFocus was on Systems of Record•  Transaction systems for global commerce . . . •  Financials, Order Processing, Inventory, HR, CRM, Supply Chain •  Mainframes, minis, client-server, PC, Internet-enabled, SaaS•  Drove three decades of investment •  Data centers everywhere •  OLTP and Business Intelligence were the key drivers•  Y2K put the capstone on this trend •  Pulled forward a half decade of investment for effectiveness •  Focus in past decade has been on investment for efficiency
  • 4. IT Disruption: For the Past Decade Enterprise IT Consumer IT On Hold On Fire
  • 5. Consumer IT Redefines Human ExperienceThe Digitization of Culture Across the Globe •  Access - Universal •  Information, opinion, searchable, interactive •  Communications are globally democratized •  Microsoft, Google, Wikipedia •  Broadband - Emotional •  Pictures, music, video, voice, shopping •  IT touches both the mind and the heart •  Facebook, iTunes, YouTube, Skype, Zappos •  Mobile - Ubiquitous •  Anywhere, anytime, any income •  Culture becomes pervasive in all dimensions •  iPod, iPhone, iPad, Android, RIM, Windows8 How will this impact enterprise IT?
  • 6. Enterprise IT – 21st CenturySystems of Engagement for B2C •  Collaborative filtering •  Behavioral targeting •  Personalized transactions •  Location-based services •  Predictive analytics •  Machine learning •  Fraud detection •  Multi-channel communications •  Near-field transaction processing •  And lots more to come . . . Big Data, Analytics, Real-Time
  • 7. Enterprise IT – 21st CenturySystems of Engagement for B2B •  Enterprise Facebook •  Enterprise YouTube •  Enterprise Search •  Enterprise App Store •  Enterprise Twitter •  Enterprise Facetime •  Enterprise OpenTable •  Enterprise Dropbox •  Enterprise Foursquare •  And lots more to come . . . . Social, Mobile, Global
  • 8. The Evolution in IT InfrastructureDriving the Next $1Trillion of Spend in B2B2C Analytics Big Data Real-Time Morphing the Stack Social Global Mobile Clearly Hadoop has a very big role to play here The question is: When?
  • 9. Seeking the Tipping Point The Technology Adoption Life Cycle MAIN STREET TORNADO EARLY MARKET BOWLING CHASM ALLEY9
  • 10. Getting to Tipping Points Different Playbooks for Each Phase Early Bowling Main Tornado Market Alley Street Visionary LOB Target Customer Executive Compelling Competitive Reason to Buy Advantage Whole Bespoke Product Projects Model Systems Partners & Allies Integrators •  Media •  Intelligence •  Investing10
  • 11. Getting to Tipping Points Different Playbooks for Each Phase Early Bowling Main Tornado Market Alley Street Visionary LOB Pragmatist Business Target Customer Executive Manager Broken Mission- Compelling Competitive Critical Business Reason to Buy Advantage Process Whole Bespoke Solutions for Product Projects Use Cases Model Systems Vertical Application Partners & Allies Integrators Vendors •  Media •  Travel •  Intelligence •  Fraud •  Investing •  Retail11
  • 12. Getting to Tipping Points Different Playbooks for Each Phase Early Bowling Main Tornado Market Alley Street Pragmatist Visionary LOB Pragmatist Business Target Customer Infrastructure Executive Manager Manager Broken Mission- Keep up with the Compelling Competitive Critical Business Current Reason to Buy Advantage Process Standards Whole Bespoke Solutions for Products & Product Projects Use Cases Support Model Horizontal Systems Vertical Application Partners & Allies Application Integrators Vendors Vendors •  Media •  Travel •  Advertising •  Intelligence •  Fraud •  Wholesale •  Investing •  Retail •  Manufacturing12
  • 13. Getting to Tipping Points Different Playbooks for Each Phase Early Bowling Main Tornado Market Alley Street Pragmatist Conservative Visionary LOB Pragmatist Business Target Customer Infrastructure Technology Executive Manager Manager Specialists Broken Mission- Keep up with the Compelling Competitive Better Value with Critical Business Current Reason to Buy Advantage No Risk Process Standards Whole Bespoke Solutions for Products & Systems & Product Projects Use Cases Support Outsourcing Model Horizontal Administrative Systems Vertical Application Partners & Allies Application Application Integrators Vendors Vendors Vendors •  Media •  Travel •  Advertising •  Health Care •  Intelligence •  Fraud •  Wholesale •  Education •  Investing •  Retail •  Manufacturing •  Government13
  • 14. Final Thoughts for Hadoop Developers Common Theme: Optimizing Outcomes at Scale •  Media Content •  Intelligence Detection •  Investment Algorithms •  Advertising Performance Key Takeaway •  Fraud Prevention For the foreseeable future •  Regulation Compliance DOMAIN EXPERTISE •  Retail/Wholesale Inventory Turns is core to competitive advantage •  Manufacturing Supply Chain •  Health Care Patient Outcomes •  Education Learning Outcomes •  Government Citizen Services14
  • 15. Final Thoughts for Hadoop Developers Common Tactic is Big Data Analytics •  Systems of Record are one foundation •  Log files are the other foundation •  Batch analysis Key Takeaway drives pattern detection This should provide at least •  Algorithms approximate detected patterns •  Real-time a decade of entertainment into processes computing inserts algorithms for everyone present! •  Processes are optimized through real-time adjustments •  Outcomes are captured in log files •  Rinse and repeat15
  • 16. gmoore@geoffreyamoore.com twitter.com/geoffreyamoore16

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