The document discusses big data, providing definitions and examples of big data problems and opportunities. It outlines a timeline of big data technologies and discusses best practices for big data success, including having a data-driven culture. The document argues that combining big data with machine learning algorithms and large computing power will lead to super-human machine intelligence within 10 years, and provides examples of current deep learning applications in areas like image and text recognition.

1. Christopher Nguyen, PhD
Co-Founder & CEO
Big Data, Big Opportunities
From Buzz to Biz
Presented on December 6, 2013

2. Step 1
Instrument Calibration

3. What
do you get when you cross …
Atlantic
Titanic

4. About
Halfway!!

5. What
do you get when you cross …

6. If
Big Data
Hadoop
are not enough
?
What
do we need?

7. WHAT is
Big Data
?

8. “Standard” Definition
Big Data
Problems
Huge Volume
High Velocity
Great Variety

9. BIG DA
TA=
PROBL
EMS
“Alternative” Definition
BIG DATA + BIG COMPUTE
=BIG OPPORTUNITIES
Learn from Data
Predict the Unknowns
from the Knowns
automatic customer segmentation

10. WHAT’s
the big
deal?
I’ve had
big data
since the 60’s

11. WHAT’s
the big
deal?
We’ve had
the sun
for a few
thousand years,
right?

12. Big Data:
The question
isn’t WHAT.
It’s WHY.

13. Why
Competitive Advantages It Brings
Holistic business insights
See underlying patterns
Predict unknowns from knowns
Automate decisions
Technology Cost/Benefit Threshold

14. WHERE did
Big Data
Technology
come from
?

15. A Timeline of
Big Data Techology
Doug
Cutting
Hortonworks
Qi Lu
MapReduce
Paper
Google
Search
Build biggest
Index of the
Internet
‘98
MapR
Cloudera
Eric14
Jeff
Dean
‘04
Hadoop
Hadoop
‘06
‘09
‘11

16. ?
WHAT SHOULD
Big Data Analytics
LOOK like

17. % Respondents with Big Data Projects
Already in Operation
100%
HOW
are people
operationalizing
Big Data
50%
2012
0%
2013
EMA Research, Operationalizing Big Data

18. Google BigQuery
Interactive,
Ad Hoc Business Query
Finance
Insight Discovery on
Aggregated
Operational Data
Marketing
Engineering
Sales
Employee Engagement with Operational Data goes through the roof

19. Mobile Ad
Platform
100+ Million Devices
Ad Targeting
CTR Prediction

20. Customer Service
Provider
Product Recommendation
Cross-channel
User Experience Optimization

21. Are there
Patterns of
Big-Data
SUCCESS
?

22. Have a
Data-driven
CULTURE
1

23. If we have data,
let’s go with that.
If all we have are opinions,
let’s go with mine.
—Jim Barksdale, former Netscape CEO

24. Google Search Latency Experiment
User Survey Opinion
“We prefer 30-result pages to 10-result pages”
Empirical Data
The extra 500ms causes users to search by 25% less
—Marissa Mayer, Google then-VP
That’s $12 Billion per year!

25. Distributed
Self Service Data Tools
Centralized
Data Service Bureau
VS.
2

26. Centralized
Data Service Bureau
Why?
Users didn’t ask enough questions.
!
Why not?
Didn’t
work out
Friction too high.

27. Distributed,
Self-service Data Tools
Team self collect, analyze, & learn from own data
Lower latency to insight
Positive feedback loop to improve tools

28. Watch
WHICH pattern
your Chief Data
Officer chooses

29. 3
Think BIG
about Big Data
Opportunities

31. Build Me the
Biggest Hangars & the
Longest Runways
I will make sure
the planes come
—Eric Schmidt, Google then-CEO

32. WHY
Think Big
about
Big Data
?

33. Big Data + Machine Learning
Algorithms
Brain
+
+
Data
Experiences
=
=
Models
Wisdom

34. Deep Learning Neuron
Human
Machine
(Source: http://capone.mtsu.edu/wlangsto/)

35. Deep Learning Neural Networks
Human
Machine
(Source: http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol2/cs11/article2.html)

36. Reading Digits in Zip Codes
Geoff Hinton, Yann Lecun, et al.
Demo
!
http://www.cs.toronto.edu/~hinton/adi/index.htm

37. Google Brain Project
16,000 Computers to Recognize Cats
and 19,999 other concepts
Andrew Ng et al.
Application
Unsupervised Image Classification
Source: http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html

38. Words are Vectors
Mikolov et al.
Portugal
-
China
+
Bejing
=
Lisbon
!
!!!
Source: http://gigaom.com/2013/08/16/were-on-the-cusp-of-deep-learning-for-the-masses-you-can-thank-google-later/

39. Machine Translation
Quoc V. Le et al.
Source: http://arxiv.org/pdf/1309.4168v1.pdf

40. Computing
Paradigm Shift
Computing Pattern
Implicit Pattern Learning
Abstractions by
Machine Intelligence
Computing Pattern
Explicit High-Level
Instructions
Abstractions from
Human Intelligence

41. Big Data + Big Compute
Why
will lead to
super-human
Machine Intelligence
in 10+ years

42. SUMMARY
WHAT & WHY of Big Data
EXAMPLES & BEST PRACTICES of Big Data
EXCITING FUTURE IMPLICATIONS of Big Data

43. Thank you!