The document discusses a leading provider of marketing intelligence that has been operating since 1926. It collects the largest advertising database in the world across over 20 countries, covering 96% of global ad activity. It aims to improve response times and accuracy for its clients by implementing a scalable on-demand architecture that utilizes distributed computing to execute massive analytics workloads and provide faster insights. The project was completed within 4 weeks and delivers faster data processing and a higher return on investment for clients' advertising spending through more timely data science.

7. 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

8. Comprehensive direct measurement Improve response time and
of advertising activity accuracy

9. Massive amounts of data Disparate locations No Big Data skills
No Hadoop
No cloud

10. • Expertise ✔
• Time to completion
• Scalability
✔
• Cost

11. • Scalable architecture
• Speed of execution on a data query
• Expandable analytics solution
Day 1 Week 1 Week 2 Week 3 Week 4
Technology review Solution selection Test-run against Project complete
Architecture design business and tech
goals
Engage data science

13. Time to completion: Project completed in 4 weeks.
Faster data capture = faster data science = higher
ROI of advertising $.
Scalability: Solution utilizes a scalable on-demand
architecture for supporting our business needs.

15. • 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.

16. New data and analytics platforms to store and
serve data to internal and external customers.

17. Massive amounts Operating in silos Lack of Big Data skills
of redundant data No Hadoop
No cloud

18. • Experienced team ✔
• Time to completion
• Cost ✔

19. • 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 3
Planning and Business and Analysis Big Data training
strategy technology brainstorm Project sequencing Project complete
Roadmap design

20. Time to completion: Project strategy and design
completed in 3 weeks.
Velocity: Solution design incorporates sophisticated use of
Big Data technologies and dramatically reduces our ad hoc
reporting costs.
Skills development & training: Our team is able to learn
from hands-on experts with deep expertise in Big Data.

22. 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

23. 22 Markets 20 Touchpoints (Channels)
13 Models (Products) 40 Control Variables

24. Sales
Spend
Sales

25. Upper Funnel
Brand Metrics
Sales
WoM Lower Funnel
Spend
Incentives
Sales

26. Upper Funnel
Brand Metrics
Sales
WoM Lower Funnel
Spend
Incentives
Sales

27. $15,000,000 in TV advertising generates $50,000,000 in sales
4800
4700
4600
4500
4400
4300
4200
4100
Media Spend
Generating a single analytic point requires sophisticated analytics

28. What is the optimal spending for a marketing channel?
4800
Historical
4700 Spend
Optimal
Weekly Sales (Units)
4600 Spend
4500
4400
4300
4200
4100
Media Spend
Distributed Computing to orchestrate analytic reports

29. What is the optimal spending for a marketing channel?
4800
Historical
4700 Spend
Optimal
Weekly Sales (Units)
4600 Spend
4500
4400
4300
4200
4100
Media Spend
Distributed Computing to orchestrate analytic reports

30. What is the optimal spending for a marketing channel?
4800
Historical
4700 Spend
Optimal
Weekly Sales (Units)
4600 Spend
4500
4400
4300
4200
4100
Media Spend
Distributed Computing to orchestrate analytic reports

31. 10s of Markets
for each customer
4800
Historical
4700 Spend
Optimal
Weekly Sales (Units)
4600 Spend
4500
4400
4300
4200
4100
Media Spend

32. 10s of Markets 10s of Products 10s of Touchpoints 100s of scenarios 100s of
for 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

33. 10s of Markets 10s of Products 10s of Touchpoints 100s of scenarios 100s of
for 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

34. Discover
Validate
Optimize
Update
Simulate
Merge
Version

35. AWS
Amazon EC2 Amazon EC2
Permanent Instances On-Demand/SPOT Instances
EC2 EC2 Amazon
Instance Instance Elastic MapReduce
Elastic 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

36. SOLUTION Model Management System on the Cloud
Analytic Request
Metadata manager Workload Statistics Distributed Models
Equations Compiler

37. Top issues in Big Analytics
Challenges in read write for big data analytics
Distributed Computing to orchestrate analytic reports
Resource management for big analytics

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