This presentation outlines ways that data already affects our lives, how integration with social and mobile can sometimes lead to the wrong conclusion by not taking into consideration the motivation behind the data, and how big data could potentially work to our benefit.

1. #xSoMoBi
Tuesday, March 12, 13

2. Tuesday, March 12, 13

3. “... software is
eating the world”
Marc Andreessen - Entrepreneur/Investor
WSJ - 20AUG2011
Tuesday, March 12, 13

4. Tuesday, March 12, 13

5. “Data ...
LOTS of DATA.”
Tuesday, March 12, 13

6. Business
Intelligence is
dead
Tuesday, March 12, 13

7. Business
Intelligence is
dead
LONG LIVE
BUSINESS
INTELLIGENCE!
Tuesday, March 12, 13

8. WELCOME TO
Business Intelligence
2.0
Tuesday, March 12, 13

9. WELCOME TO
Business Intelligence
2.0
Tuesday, March 12, 13

10. WELCOME TO
BIG DATA
Tuesday, March 12, 13

11. Any information is
only as good as its
[SOURCE]
________
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12. “We have Petabytes
of Clickstream data”
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13. ...but, can you use
it ?
Tuesday, March 12, 13

14. Even if you can, will
it be #useful ?
Tuesday, March 12, 13

15. ...wait, what IS
“BIG DATA” ?
Tuesday, March 12, 13

16. It’s all about the
context.
INFORMATION
+ INSIGHTS
= CONTEXT
Tuesday, March 12, 13

17. +
Tuesday, March 12, 13

18. INFORMATION
TIME
DEVICE
} }{ =
ANYONE
ANYWHERE
BETTER
FASTER
BIG DATA
RIGHT DECISIONS
Tuesday, March 12, 13

19. Data + Transformation INFORMATION
Rules + Feedback +
Patterns
INSIGHTS
BIG
Information + Insights
DATA
BIG +
SERVICES $$$
DATA
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20. REAL WORLD EXAMPLE
Geo Locate the user.
Identify the IP address based on geo-
location.
Designated Market Area precision.
Geofences around “hot-spots”.
#Fail
Tuesday, March 12, 13

21. How does the “system” know - you are a
Mom ?
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22. HOW WE DO IT
First we “profile” a lot of users - Behavioral
Dynamics
Then we begin “associating” you with those
profiles - Heuristic driven rules.
We find out that you are a “woman” - Training
sets -> Increased Confidence
We then identify patterns - Clustering based
data mining
Tuesday, March 12, 13

23. ID’ed using Device
Data from the “same user”
+ +
impression
when they were near a school Pattern of a parent
in the morning on a weekday
+ +
and when they were at a nail Pattern of a female user
salon during school hours
+ +
and toy store browsing late in Re-affirmation of the
the afternoon.
} pattern
LIKELIHOOD OF BEING A MOM
Tuesday, March 12, 13

24. BIG DATA
IRRELEVANT RELEVANT
INFORMATION
INTERPRETABLE UNINTERPRETABLE
Tuesday, March 12, 13

25. NOISE
RELEVANT SIGNAL INSIGHT
UNINTERPRETABLE
Tuesday, March 12, 13

26. Unstructured Batch
Data
Variety Velocity
Streaming
Big Data
Structured Data
Data
Zettabytes Terabytes
Volume
Tuesday, March 12, 13

27. BIG DATA
LANDSCAPE
Tuesday, March 12, 13

28. BIG DATA LANDSCAPE
Log Data Apps Vertical Apps
Business Analytics and
Intelligence Visualization
Data Providers
Infrastructure Structured
As Databases
Analytics Operational
a
Infrastructure Infrastructure
Service Oracle
(IAAS) MySQL
Hadoop MapReduce Apache HBASE Cassandra
Tuesday, March 12, 13

29. DATA IN
MOTION
vs.
DATA AT REST
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30. APPLICATIONS
vs.
ANALYTICS
Tuesday, March 12, 13

31. DATA VELOCITY
vs.
JUDGEMENT
CALL
Tuesday, March 12, 13

32. HOW BIG IS BIG ?
$28 billion of IT spend through
2012
2 million jobs in the tech industry by
2015
6 million across other
industries.
Tuesday, March 12, 13

33. MOBILE +
BIG DATA
Tuesday, March 12, 13

34. WHERE DOES MOBILE FIT
IN?
Data Layer Transition is in full
swing
Real Time analysis using time, geo-
data and Social Updates
Push Notifications via Intelligent
Alerts
It’s not just about Push.
Tuesday, March 12, 13

35. WHERE DOES MOBILE FIT
IN?
INTERACTIVE
Pinch, Swipe, Zoom, and Drag/
Drop data sources
User specific themes - based on
memory
(usage + history)
Mutual value addition to the Data
Tuesday, March 12, 13

36. COEXISTENCE [SoMoBi]
60% 50%
60% 30%
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37. COEXISTENCE [SoMoBi]
Internet of Things
M2M --> P2M
70% abandonment rate
^ what does this mean?
Tuesday, March 12, 13

38. VERTICALS +
BIG DATA
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39. SALES
Social + Context + Location = $$$
Facebook + Twitter + Foursquare
notifications
Identify trends that lead to poor leads
+ losses
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40. TELECOM
Personalized products
Minutes not Hours.
Interpreting network data
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41. URBAN PLANNING
Aging Infrastructure
High Costs of Maintenance
Traffic Data, Sewer Level
monitoring
Fight Crime
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42. OTHER SECTORS
Fraud Analysis + Risk +
Compliance
Copyright + IPP
“This call may be recorded for
Quality Assurance and Training
purposes”
Sentiment Analysis and Social
Media
Tuesday, March 12, 13

43. McKinsey Report on Big Data - 2012
Tuesday, March 12, 13

44. Predicting Unemployment
$
Foreclosure
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45. BIG DATA
BECKONS...
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46. BIG DATA IN
ACTION
“Meta”
“Big”
“Swoooooosh”
“Privacy”
“Structure”
Tuesday, March 12, 13

47. BIG DATA
ADOPTION
Skynet’s here.
Pay for Privacy
Avoid Stalkers
76 working days
Privacy advocates vs Company
Policy
Tuesday, March 12, 13

48. “BIG DATA has it’s roots in good
data”
Data Exhaust is no longer an
excuse.
Not a replacement, but a
complement.
INTEGRATION.
Tuesday, March 12, 13

49. “BIG DATA has it’s roots in good
data” - anonymous brilliant thinker(s)
Data Exhaust is no longer an
excuse.
Not a replacement, but a
complement.
INTEGRATION.
Tuesday, March 12, 13

50. THANK YOU,
Tuesday, March 12, 13

51. 04.25.2013
#show&tell
(open call)
06.20.2013 #mobileGAMES
(all play & no work)
Tuesday, March 12, 13