Presentation given to the Nikkei BP event on Big Data and Analytics in Tokyo, Japan on April 9, 2013.

1. DATA AND
DISILLUSIONMENT
SOLVEfor
INTERESTING
OTHERWISE LIFE IS DULL.

2. Volume
(the “big”
part)
Pick
any
Velocity
two Variety
(the “fast” (the
part) “anything” part)

3. Big Data is the Third Age of computing
Computing Networking Big Data
Automate Interconnect Predict & change
things things things
(Jim Stodgill of O’Reilly Radar said this.)

4. Enterprises expect Big Data to deliver better
decisions and improved customer experiences
What tangible benefits do you hope to achieve
through your big data initiatives?
NewVantage Partners LLC www.newvantage.com

5. (And apparently Hadoop is winning)
What data management approaches
are you considering?
NewVantage Partners LLC www.newvantage.com

6. The
relational
database
is a general-
purpose
tool.

7. A library is
a database
optimized
for retrieval
Photo by cybrgrrl (http://www.flickr.com/photos/cybrgrl/1295482521/) on Flickr

8. A change
counter is a
database
optimized for
insertion

9. An example:
eventual
consistency

10. “End of Day Balance will only appear for dates previous to
the last 2 business days.”
“Transactions from today are reflected in your balance, but
may not be displayed on this page if you recently updated
your bankbook, if a paper statement was recently issued, or
if a transaction is backdated. These transactions will appear
in your history the following business day.”

11. Relational
BIG
Statistical

12. http://www.flickr.com/photos/jenny-pics/3239638494/sizes/l/
Breadcrumb trail

13. The average enterprise has 178 social
media accounts
(According to @setlinger and the Altimeter group.)

14. Ward off disease.
Pinpoint disasters.
A force Reveal corruption.
for good.
Make cities smarter.
Improve how we teach.

15. Big healthcare

16. Big philanthropy

19. Big commuting

22. Erode our privacy.
Justify prejudices.
A force Polarize groups.
for bad.
Leak private truths.

23. Big prejudice

27. “…nobody notices offers they do not
get. And if these absent opportunities
start following certain social patterns
(for example not offering them to
certain races, genders or sexual
preferences) they can have a deep civil
rights effect.”
Anders Sandberg, Oxford University

28. Personalization looks a lot
like prejudice.

29. Big radio

31. Times a song in “heavy rotation”
is played each day
30
Every 55m
15
Every 4h
0
2007 2012

32. Humans are bad at data.

33. We prefer false positives.

34. Wooly mammoth
http://www.flickr.com/photos/pong/172438102/sizes/o/

35. Sun temple
http://www.flickr.com/photos/30787002@N02/3298693694/sizes/l/

36. Some proof.

41. It’s really hard to find people who can think
about data well
How challenging is it to source data scientists?
NewVantage Partners LLC www.newvantage.com

42. Mistake correlation for causality
Seek truthiness rather than fact
Find patterns where they don’t exist
Easily swayed by tone
Side with our tribes
Dig in and ignore new evidence

43. Athenian swimming pools

44. Volume
Big
Variety Data Good
data
Velocity
Veracity

45. 525,000 state & local officers
Under 25 officers per precinct
130 million incident reports
200,000 uses of force
31% keep computer files

47. Evidence.com

48. Hard drive

49. Big Data is not about data.

50. Big Data is about truth,
auditability, and the ability to
analyze data on a level
playing field.
It’s about analysis for
everyone.

51. Alistair Croll
@acroll
www.solveforinteresting.com
THANKS! alistair@solveforinteresting.com
SOLVEfor
INTERESTING
OTHERWISE LIFE IS DULL.