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BDT304 Big Data Masterclass - AWS re: Invent 2012
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BDT304 Big Data Masterclass - AWS re: Invent 2012

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Learn how engineers at startups and larger enterprises use data to drive greater insight into their operations, customers, and business in this lively discussion of big data techniques and tools. From …

Learn how engineers at startups and larger enterprises use data to drive greater insight into their operations, customers, and business in this lively discussion of big data techniques and tools. From Hadoop to data warehouses, this panel discusses the tools, techniques, tips, and tricks for building data driven teams and delivering cost optimization at scale.


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  • 1. LEADING PROVIDER OF MARKETING INTELLIGENCE SINCE 1926.• Largest advertising database in the world• Direct operations in >20 countries• Reporting on 96% of global ad activity
  • 2. Comprehensive direct measurement Improve response time and of advertising activity accuracy
  • 3. Massive amounts of data Disparate locations No Big Data skills No Hadoop No cloud
  • 4. • Expertise ✔• Time to completion• Scalability ✔• Cost
  • 5. • Scalable architecture • Speed of execution on a data query • Expandable analytics solution Day 1 Week 1 Week 2 Week 3 Week 4Technology review Solution selection Test-run against Project complete Architecture design business and tech goals Engage data science
  • 6. Time to completion: Project completed in 4 weeks.Faster data capture = faster data science = higherROI of advertising $.Scalability: Solution utilizes a scalable on-demandarchitecture for supporting our business needs.
  • 7. • NASDAQ OMX is the world’s largest Exchange Company.• Manages 1 in 10 of the world’s security transactions.• First U.S. equity trading platform with a price-size priority model.• First to offer data on demand.• First Green Exchange in the world (Helsinki).• Operates in 6 continents.
  • 8. New data and analytics platforms to store andserve data to internal and external customers.
  • 9. Massive amounts Operating in silos Lack of Big Data skillsof redundant data No Hadoop No cloud
  • 10. • Experienced team ✔• Time to completion• Cost ✔
  • 11. • Co-develop Big Data vision and strategy • Gap analysis of existing architecture • Goal architecture design • Training of internal staff Day 1 Week 1 Week 2 Week 3Planning and Business and Analysis Big Data training strategy technology brainstorm Project sequencing Project complete Roadmap design
  • 12. Time to completion: Project strategy and designcompleted in 3 weeks.Velocity: Solution design incorporates sophisticated use ofBig Data technologies and dramatically reduces our ad hocreporting costs.Skills development & training: Our team is able to learnfrom hands-on experts with deep expertise in Big Data.
  • 13. Bringing modeling & big data to marketing Risky Strong Strong Bets Contenders Performers Leaders ThinkVine Marketing Management Analytics Marketing Analytics Symphony IRI Group Ninah Nielsen Current Offering MarketShare MarketShare Planner™ Price™ Market presence MarketShare MarketShare 360™ Optimizer™ Full Vendor Participation Incomplete Vendor Participation MarketShare Platform Weak Strategy Strong Cloud modeling | Saas infrastructure | Data connectors
  • 14. 22 Markets 20 Touchpoints (Channels)13 Models (Products) 40 Control Variables
  • 15. Sales SpendSales
  • 16. Upper Funnel Brand Metrics Sales WoM Lower Funnel SpendIncentives Sales
  • 17. Upper Funnel Brand Metrics Sales WoM Lower Funnel SpendIncentives Sales
  • 18. $15,000,000 in TV advertising generates $50,000,000 in sales48004700460045004400430042004100 Media Spend Generating a single analytic point requires sophisticated analytics
  • 19. What is the optimal spending for a marketing channel? 4800 Historical 4700 Spend OptimalWeekly Sales (Units) 4600 Spend 4500 4400 4300 4200 4100 Media Spend Distributed Computing to orchestrate analytic reports
  • 20. What is the optimal spending for a marketing channel? 4800 Historical 4700 Spend OptimalWeekly Sales (Units) 4600 Spend 4500 4400 4300 4200 4100 Media Spend Distributed Computing to orchestrate analytic reports
  • 21. What is the optimal spending for a marketing channel? 4800 Historical 4700 Spend OptimalWeekly Sales (Units) 4600 Spend 4500 4400 4300 4200 4100 Media Spend Distributed Computing to orchestrate analytic reports
  • 22. 10s of Marketsfor each customer 4800 Historical 4700 Spend Optimal Weekly Sales (Units) 4600 Spend 4500 4400 4300 4200 4100 Media Spend
  • 23. 10s of Markets 10s of Products 10s of Touchpoints 100s of scenarios 100s offor each customer for each Market for each Product for each curve Customers 10 * 10 * 10 * 100 * 100 = 10,000,000 model executions At 1 minute per execution = 6940 days = 20 years
  • 24. 10s of Markets 10s of Products 10s of Touchpoints 100s of scenarios 100s offor each customer for each Market for each Product for each curve Customers Sophisticated Modeling = Elastic Cloud 10 * 10 * 10 * 100 * 100 = 10,000,000 model executions At 1 minute per execution = 6940 days = 20 years
  • 25. Discover ValidateOptimize Update Simulate Merge Version
  • 26. AWS Amazon EC2 Amazon EC2 Permanent Instances On-Demand/SPOT Instances EC2 EC2 Amazon Instance Instance Elastic MapReduceElastic Load Balancer Web App Server Server AWS Amazon EC2 Amazon Software Services Managed Storage Elastic Cache SWS RDS Database Amazon Simple Instance Storage Service (S3) Web App Server Server
  • 27. SOLUTION Model Management System on the CloudAnalytic RequestMetadata manager Workload Statistics Distributed ModelsEquations Compiler
  • 28. Top issues in Big Analytics Challenges in read write for big data analytics Distributed Computing to orchestrate analytic reports Resource management for big analytics
  • 29. We are sincerely eager to hear your feedback on thispresentation and on re:Invent. Please fill out an evaluation form when you have a chance.