Big Data - Trender och verklighet inom Information Management. Denna presentation hölls på IBM Data Server Day den 22 maj i Stockholm av Jacques Milman, Datawarehouse Architecture Leader, IBM

1. February 2012
IBM Netezza:
the data warehouse in a big data strategy
© 2012 IBM Corporation

2. Information Management
What is “BIG DATA”?
All kinds of data
Large• volumes
Existing sources of data continue to grow
Valuable insight, •but difficult to extract now available
New sources of data are
Often extremely • detailed customer data
time sensitive
• internet sources
• instrumentation
• Data arrives at an increasing rate
© 2012 IBM Corporation

3. Information Management
Utilities
Financial Services  Weather impact on power
 Fraud detection generation
 Risk management  Transmission monitoring
 360° View of the Customer  Smart grid management
Transportation IT
 Weather and traffic
 Transition log analysis for
impact on logistics and
multiple systems
fuel consumption  Cybersecurity
Health & Life Sciences
 Epidemic early warning Retail
 ICU monitoring  Customer 360° View
 Healthcare monitoring  Click-stream analysis
 Real-time promotions
Telecommunications Law Enforcement
 CDR processing  Real-time multimodal surveillance
 Churn prediction  Situational awareness
 Geomapping / marketing  Cyber security detection
 Network monitoring
© 2012 IBM Corporation

4. Information Management
What is “BIG DATA”?
MATHs
All kinds of data
Large volumes
Valuable insight, but difficult to extract
Often extremely time sensitive
© 2012 IBM Corporation

5. Information Management
Utilities
Financial Services  Weather impact on power
 Fraud detection generation
 Risk management  Transmission monitoring
 360° View of the Customer  Smart grid management
Variety: Manage the complexity of multiple
relational and non-relational data
Transportation types and schemas
IT
 Weather and traffic
 Transition log analysis for
impact on logistics and
multiple systems
Streaming data and large volume
fuel consumption Velocity:  Cybersecurity
data movement
Health & Life Sciences
 Epidemic early warning Retail
 ICU monitoring  Customer 360° View
 Healthcare monitoring Volume: Scale from terabytes to zettabytes
 Click-stream analysis
 Real-time promotions
Telecommunications Law Enforcement
 CDR processing  Real-time multimodal surveillance
 Churn prediction  Situational awareness
 Geomapping / marketing  Cyber security detection
 Network monitoring
© 2012 IBM Corporation

6. Information Management
Marketing to a segment of one
• Identifies items that shoppers are likely
to buy in future visits
• Coupon redemption rates as high as
24%
“Because of (Netezza’s) in-database technology, we believe we'll be able to do
600 predictive models per year (10X as many as before) with the same staff”.
- Eric Williams,
CIO & Executive VP
6
© 2012 IBM Corporation

7. Information Management
Netezza in-database analytics at Catalina Marketing
 35X improvement in staff productivity
– model development reduced from 2+ months to
2 days
– 90 models per year in 2006
– 900 models per year in 2011
• with the same staff
– model scoring time reduced from 4.5 hours to
60 seconds
 Increased depth of data per model
– 150 to 3.2 million features
– 1 million to 14.5 trillion records per analysis
 ROI on IT investment
– direct correlation between number of models
and revenue
© 2012 IBM Corporation
7

8. Information Management
Big Data Analytics in Smarter Hospitals
Big Data enabled doctors from University of Ontario to apply neonatal infant
monitoring to predict infection in ICU 24 hours in advance
IBM Data Baby
youtube.com
8 © 2012 IBM Corporation

9. Information Management
University of Ontario Institute of Technology
 Use case
– Neonatal infant monitoring
– Predict infection in ICU 24 hours in advance
 Solutions
– 120 children monitored :120K msg/sec, billion msg/day
– Trials expanding to include hospitals in US and China
Event Pre- Analysis
processer Framework
Sensor Stream-based Distributed Interoperable Solutions
Network Health care Infrastructure (Applications)
(
9 © 2012 IBM Corporation

10. Information Management
Vestas optimizes
capital investments
based on 2.5
Petabytes of
information.
 Model the weather to
optimize placement of
turbines, maximizing power
generation and longevity.
 Reduce time required to
identify placement of turbine
from weeks to hours.
 Incorporate 2.5 PB of
structured and semi-
structured information flows.
Data volume expected to
grow to 6 PB.
© 2012 IBM Corporation

11. Information Management
Big Data Made Easy for the Little Guy
USC’s Film Forecaster correctly predicted a clamor for "Hangover 2” that
resulted in $100 million opening over Memorial Day weekend
– Looked at 250K-500K Tweets and broke down positive and negative messages
using a lexicon of 1700 words
The Film Forecaster sounds like a
big undertaking for USC, but it really
came down to one communications
masters student who learned Big
Sheets in a day, then pulled in the
tweets and analyzed them
- Ryan Kim
© 2012 IBM Corporation

12. Information Management
IBM big data platform
InfoSphere BigInsights
Hadoop-based analytics for variety and volume
Hadoop
Information Stream
InfoSphere Information
Integration Computing InfoSphere Streams
Server
Low-latency Analytics for
High-volume data integration streaming data
and transformation
MPP Data Warehouse
IBM optimized workload data warehouses
Scalable, high-performance, mixed-workload analytics on structured data
© 2012 IBM Corporation

13. Information Management
IBM big data platform
© 2012 IBM Corporation

14. Information Management
IBM big data platform
InfoSphere BigInsights IBM Netezza InfoSphere Streams
Analytics on Big Data at Rest Analytics on
Unstructured Structured Big Data in Motion
© 2012 IBM Corporation

15. Information Management
IBM big data platform
© 2012 IBM Corporation

16. Information Management
IBM big data platform
• Big Data
• Volume
• Velocity
• Variety
• Combining data types & sources
• Combining technologies to analyse it
• Complementing the relational warehouse
© 2012 IBM Corporation

17. Information Management
© 2012 IBM Corporation