Presentation given at the Facebook Marketing Event on June 11, Mediaplaza Utrecht.

1. June 11, 2013 Iskander Smit, http://about.me/iskandr
Big Data & Social Media
Facebook Marketing Event 2013
http://vimeo.com/12187317

2. June 11, 2013
Strategy director & managing labs.info.nl
member council Internet of Things
thinking online services since 1994

3. June 11, 2013
Info.nl
§ internet agency since 1994
§ connecting the dots for brands that value the power of online
§ digital strategy, digital services, digital marketing, labs
§ networked agency with full-service core
§ partnership for social marketing with 77Agency
§ that is one of the 13 Facebook sPMD
§ social services: strategy, ux, apps, media, e-commerce management
§ clients amongst others: BVA Auctions, Greenwheels, bol.com, ABN Amro

4. June 11, 2013
What is BIG in big data?

5. June 11, 2013
(What is big today will be
common tomorrow)

6. June 11, 2013
every day the amount of data
doubles
90% of all data originated in
the last 2 years
Google à 5000TB/h

7. June 11, 2013 https://www.facebook.com/facebook/photos_stream
110 songs have been played 40 billion times
150 billion friend connections

8. June 11, 2013
http://villevivante.ch

9. June 11, 2013
People
ThingsServices

10. June 11, 2013
Volume
Velocity Variety
http://solveforinteresting.com/big-data-is-our-generations-civil-rights-issue-and-we-dont-know-it/

11. June 11, 2013
Precogs
Memphis CRUSH, police combines
criminal data, personal data with
context data and real-time analytics

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13. June 11, 2013
Sources of data
§ Online behaviour. From search to browsing
§ Social. Facebook likes, youtube shares, linkedin connections, etc. etc.
§ E-mail. Google gmail
§ Internet of things. Just starting
§ Quantified self

14. June 11, 2013
Source: behavior
Bonus kaart

15. June 11, 2013
Source: our life
smart home

16. June 11, 2013
Source: social behavior
Livehoods based on Foursquare

17. June 11, 2013
Source: ourselves
From Fitbit to Moto X

18. June 11, 2013
Ubiquitous time/location stamps
iBeacons

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20. June 11, 2013
Combining data
Facebook customer audiences

21. June 11, 2013
Lookalike profiling expands the
profiles 10 times

22. June 11, 2013
Endless source of data

23. June 11, 2013
Technologies of Big Data
Hadoop, Mapreduce, Hive, …

24. June 11, 2013
Components in a Big Data system

25. June 11, 2013
3 layers (Cloudera)
§ substract
§ technologies: Open Commute, Linux, ZooKeeper, Puppetchef
§ services: Gimbal, Zigbee.
§ store
§ technologies: HDFS (Hadoop), Hive, Hbase
§ services: Amazon S3
§ compute
§ technologies: YARN, MapReduce, FlumeJava
§ software: Splunk, Palantir, Google Charts

26. June 11, 2013
McKinsey Global Institute, May 2011.

27. June 11, 2013
Predict behavior
ZestCash

28. June 11, 2013
Brokers of data
Bazaarvoice

29. June 11, 2013
Knowledge
medicine interactions

30. June 11, 2013
Combining sources
Bina

31. June 11, 2013
Big data analytics, still BI focused
MicroStrategy, Tableau

32. June 11, 2013
A promise of Big Data analytics:
Do-It-Yourself
Business Intelligence

33. June 11, 2013
Search the big data
Cloudera search

34. June 11, 2013
Big data is de new online value
pagerank network+effect conversa0ons big+data

35. June 11, 2013
Tiny services

36. June 11, 2013
The value of big data is in the
personal services you create

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38. June 11, 2013
http://www.youtube.com/watch?v=pPqliPzHYyc

39. June 11, 2013
With the predictive power of now,
you get just the thing you need to
know, right when you need it.

40. June 11, 2013
From search to relevant agent
Google Now

41. June 11, 2013
uncanny valley of relevancy

42. June 11, 2013
private
generic
1000

43. June 11, 2013
transparent
personal
1000

44. June 11, 2013
anonymous
relevant now
1000
big data

45. June 11, 2013
Products react to context
Nest

46. June 11, 2013
Data driven serendipity
Peel

47. June 11, 2013
The power of Big Data is
a new form of personal relevant
services

48. June 11, 2013
Tension
predictiveness and serendipity
can become Big Data flow

49. June 11, 2013
The value of big data is in the
personal services you create

50. June 11, 2013
We live in The Matrix
PRISM / Google

51. June 11, 2013
How to act
first steps in a big data strategy
https://vimeo.com/5550677

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53. June 11, 2013
Users
Rules
Data objects

54. June 11, 2013
Data objects
§ Connected to each other by the use of all users
§ And also by their meta data
§ Value of relations emerges from frequence
§ Negative relations can appear

55. June 11, 2013
Users
§ Relations per user with objects
§ Objects are no hub to relate users with each other
§ Objects are no hub to relate objects to users

56. June 11, 2013
Rules
§ Repeating intelligence
§ Basis is ad-hoc connecting,
§ Can be rules for recognized repeating behavior
§ Connects objects to data

57. June 11, 2013
Data intelligence
data science data intelligence
improvement 10% 10x
model goal predict/optimize create/change
decision algorithm human
data big/clean small/messy
communication equations stories
problem tactical strategic
Sean Gourley, Quid

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63. June 11, 2013
Creating an ad-hoc real-time
profile for the user of the
service.
Fueled by bol.com
recommendation API +
Facebook social graph

64. June 11, 2013
The recipe.

65. June 11, 2013
Steps in a datastrategy
1. Start with collecting data that is running through your systems
2. Appoint a special data team with several different roles
3. Look for the connections between the data streams
4. Develop a data vision
5. Make a plan, a strategy to execute
6. Learn & launch all the time

66. June 11, 2013
Big data for intelligence =
story based data design

67. June 11, 2013
Combine sources to
on-the-fly user profiles

68. June 11, 2013
So to end some attention points
§ Go for the delta in data
§ Mix moment and insight
§ Be humble
§ Create serendipity
§ Learn and tune
§ Combine sources
§ Build internal support
§ Start!

69. June 11, 2013
The value of big data is in the
(personal) services you create

70. June 11, 2013
Thanks!
Iskander Smit
http://labs.info.nl
iskander@info.nl
+31624532388
@iskandr