The document discusses big data analytics. It defines big data analytics as extracting value from large volumes of a wide variety of data by analyzing it to reveal patterns, trends and associations. This analysis can help companies gain competitive advantages by using the insights for applications like predictive analytics, risk analysis, recommendation systems, and more. It notes that while big data analytics can provide opportunities, it also presents challenges due to its technical complexity. It proposes a big data analytics suite that would make these tools more accessible to businesses.

1. What is Big Data
Analytics?
Guy Harrison
Executive Director,
R&D Information Management
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8. What is Big
Data?
Value
Competitive or Collective
advantage
Variety
Volume
Structured
Unstructured
Human Generated
Machine Generated
Terabytes
Petabytes
Exabytes
Zetabytes
Velocity
User populations x
Transaction rates x
Machine data
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9. An Industrial
revolution of
data
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10. Fed by cloud,
social and
mobile
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11. The revolution
continues
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14. The instrumented
human
Compass
Camera
Mike/earphones
Heads up display
Emotion/Attention
monitor
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Bluetooth Personal
Area Network
3G/WiFi Wide Area
Network
GPS
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Storage
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Pulse, temp
monitor
Silent alarms
Pedometer, sleep
monitoring
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15. In an
instrumented
world
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16. Big Data
Analytics
More Data
Smarter
Algorithms
Operational
Integration
Competitive Advantage
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17. Social Media
Demographics
Online Systems
Marketing
Automation
Dynamic
Pricing
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Big Data
Analytic
Algorithms
Business
Intelligence
Customer
Acquisition
Personalization
Customer
Retention
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18. Big Data
Analytics
Big Data Analytics
AKA Data Science
Collective
Intelligence
Machine
Learning
Programs that use
inputs from
“crowds’ to seem
intelligent
Programs that
evolve with
“experience”
Predictive
Analytics
Programs that
extrapolate from
existing data into
the future
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27. Search
Optimization
Advertising
• Targeting
• Tailoring
Game
optimization
Recommendation
Systems
Big Data
Analytics
Medical
• Risk analysis
• Diagnosis
• Prognosis
Security
• Vulnerability
• Penetration
Detection
Fraud Detection
Predictive Analytics
• Churn
• Defaults
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28. Big Data
Analytics is
HARD!
• Machine learning, collective
intelligence, Hadoop,
predictive analytics, R, Weka,
Mahout, are HARD
• Small-medium businesses
need help to compete
• Data scientists to the rescue?
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30. We need a Big
Data Analytics
Suite
Kitenga
Analytics Suite
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31. Thanks!
Guy Harrison
guy.harrison@software.dell.com
guyharrison.net
Twitter: @guyharrison