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.
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
Tuesday, March 12, 13
20. REAL WORLD EXAMPLE
Geo Locate the user.
Identify the IP address based on geo-
location.
Designated Market Area precision.
Geofences around âhot-spotsâ.
#Fail
Tuesday, March 12, 13
21. How does the âsystemâ know - you are a
Mom ?
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22. HOW WE DO IT
First we âproïŹleâ a lot of users - Behavioral
Dynamics
Then we begin âassociatingâ you with those
proïŹles - Heuristic driven rules.
We ïŹnd out that you are a âwomanâ - Training
sets -> Increased ConïŹdence
We then identify patterns - Clustering based
data mining
Tuesday, March 12, 13
23. IDâed using Device
Data from the âsame userâ
+ +
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
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25. NOISE
RELEVANT SIGNAL INSIGHT
UNINTERPRETABLE
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26. Unstructured Batch
Data
Variety Velocity
Streaming
Big Data
Structured Data
Data
Zettabytes Terabytes
Volume
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
34. WHERE DOES MOBILE FIT
IN?
Data Layer Transition is in full
swing
Real Time analysis using time, geo-
data and Social Updates
Push NotiïŹcations via Intelligent
Alerts
Itâs not just about Push.
Tuesday, March 12, 13
35. WHERE DOES MOBILE FIT
IN?
INTERACTIVE
Pinch, Swipe, Zoom, and Drag/
Drop data sources
User speciïŹc themes - based on
memory
(usage + history)
Mutual value addition to the Data
Tuesday, March 12, 13
39. SALES
Social + Context + Location = $$$
Facebook + Twitter + Foursquare
notiïŹcations
Identify trends that lead to poor leads
+ losses
Tuesday, March 12, 13
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
âThis call may be recorded for
Quality Assurance and Training
purposesâ
Sentiment Analysis and Social
Media
Tuesday, March 12, 13
46. BIG DATA IN
ACTION
âMetaâ
âBigâ
âSwooooooshâ
âPrivacyâ
âStructureâ
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47. BIG DATA
ADOPTION
Skynetâs here.
Pay for Privacy
Avoid Stalkers
76 working days
Privacy advocates vs Company
Policy
Tuesday, March 12, 13
48. âBIG DATA has itâs roots in good
dataâ
Data Exhaust is no longer an
excuse.
Not a replacement, but a
complement.
INTEGRATION.
Tuesday, March 12, 13
49. âBIG DATA has itâs roots in good
dataâ - anonymous brilliant thinker(s)
Data Exhaust is no longer an
excuse.
Not a replacement, but a
complement.
INTEGRATION.
Tuesday, March 12, 13