This session is designed for banking and other financial services managers with technical experience or engineers. It will discuss business intelligence platform deployments on Hadoop including cost performance, customer analytics, value-at-risk analytics and IT SLA’s.

2. Most Promising Company Trend Setting Product Startup to Watch
(GigaOM 2010) (KMWorld 2011) (Dow Jones, 2010)

3. 19
97 History
20
00
20
03
20
05
20
09
20
11

4. Adoption

5. Hadoop
• Economics • Complexity
• Flexibility
• Scalability
but • Requires significant resources
• No packaged apps

6. Data Driven Business

7. Slow Expensive Expertise
Business
ETL Data Warehouse
Intelligence
Fast Economic Self Service
Drag and Drop
Raw Load Hadoop
Spreadsheet

8. The Datameer Solution
Seamless Data Integration Powerful Analytics Self-Service Dashboards
• Data as it comes • Interactive spreadsheet UI • Mashup anything
• 15+ Connectors • Over 180 built-in analytic functions • Integration into existing portals
• Connector plug-in API • Macros & function plug-in API • Visualization plug-in API

9. Some Problems....
Up to 8 copies of the
same data
One database for
every 8 employees
Billion dollar
From DW into
decision from data that is
Excel and worse from
disconnected and
Excel back into DW
inaccurate
Understanding our
data is the biggest
competitive advantage

10. #1 Service Level Agreement Analytics
What How Benefits
• Service Oriented Architecture • Aggregate logs into Json Format • 85% batch window improvement
• 1000’s of Servers log files • Sessionisation • New interaction/SLA insights
• System behavior
• User Interaction
}

11. #2 Loan Risk Analytics
What How Benefits
• Replaced a slow, complex ETL/DW • Identify the scoring features • Empowered business users do ‘ETL’
• Master/Slave Hadoop Cluster • Test model against existing data • Significant performance improvements
• Ad-hoc/weekly reports • Agility in testing new models
}

12. #3 Data Profiling (Dodd-Frank Act)
What How Benefits
• Improve data accuracy • Run complex data quality rules • Improved data accuracy
• Forecasts loan portfolio risk • Business users spreadsheets • Clear understand of exposed risk
• “Telescope to Microscope” • Risk analytics for defaults and fraud
}

13. #4 Fraud Prevention - 95% Cost Reduction
What How Benefits
• Point of sale system data • Time Series Analytics • Data online for business users
• Attack frequency analytics • Strategic insights
• Model testing on historic data • 95% cost Reduction
}

14. Time Series Analytics
Artificial Noise
Random Noise
(two differently parameterized uniform distributions)
500
500
400
400
300
300
value
200
200
100
100
0
0
0 500 1000 1500 2000 0 500 1000 1500 2000
time time
Fast Fourier Transformation Fast Fourier Transformation
(on random noise) Fourier Power (two differently parameterized uniform distributions)
frequency frequency

15. #5 Total Value At Risk
What How Benefits
• Packaged products with market data • Large number of data sources • Better understand market data impact
• Risk exposure in the market • Complex transformation
• Agility with spreadsheets
}

17. #6 Asset Movement and Fraud Warning
What How Benefits
• Internal asset movements • Monitor movement patterns • Warning system for rogue trader
http://dealbreaker.com/2008/02/jerome-kerviel-charting-his-trades/

18. www.datameer.com