Big Data, We Have a Communication Problem by Daniel Tunkelang Presented on April 30, 2013 at the TTI/Vanguard Conference on Ginormous Systems http://www.ttivanguard.com/conference/2013/ginormous.html It's a cliché that we live in a world of Big Data. But the bottleneck in understanding data is not computational. Rather, the biggest challenge is designing technical solutions that effectively leverage human cognitive ability. Data analysis systems should augment people's capabilities rather than replace them. This argument is as old as computer science itself: in 1962, Doug Engelbart said that the goal of technology is “the enhancement of human intellect by increasing the capability of a human to approach a complex problem situation.” Algorithms extract signal from raw data, but people fill in the gaps, creating models and evaluating analyses. Empowering people to understand data is not just a surface problem of building better interfaces and visualizations. We need to interact with data not only after performing computational analysis, but throughout the analysis process in order to improve our models and algorithms. In order to do so, we need tools and processes specifically designed to offer people transparency, guidance, and control. Human-computer information retrieval has been revolutionizing our approach to information seeking -- no modern search engine limits users to black-box relevance ranking and ten blue links. We need to take similar steps in our analysis of big data, making people the center of the analysis process and developing the technical innovations that enable people to fulfill this role.

1. BIG DATA,
We have a communication problem.
GINORMOUS SYSTEMS
April 30–May 1, 2013
Washington, D.C.
Daniel Tunkelang
Head of Query Understanding, LinkedIn

2. BIG DATA IS EVERYWHERE

3. BIG DATA POWERS EVERYTHING

4. DATA SCIENTISTS WORRY ABOUT
VOLUME, VELOCITY, VARIETY, …

5. BUT THE BOTTLENECK
ISN’T COMPUTATIONAL
IT’S COGNITIVE

6. TOOLS AUGMENT
HUMAN INTELLECT
BIG DATA IS A TOOL
Doug Engelbart, inventor of
the mouse, hypertext, etc.

7. NOT EVERYONE SUBSCRIBES
TO THIS POINT OF VIEW…
Claudia Perlich, Chief Scientist of media6degrees, speaking at
TTI/Vanguard 2012 Conference on Understanding Understanding:

8. SHE HAS A POINT

9. BUT PREDICTIVE MODELING
IS NOT ENOUGH

10. TRAINING
DATA?
OBJECTIVE
FUNCTION?

11. WE NEED A
PEOPLE-CENTRIC
APPROACH TO
BIG DATA
INTERPRETABILITY
INTERACTION
INSIGHT

12. LET’S START WITH
INTERPRETABILITY

13. EXAMPLE:
SVM
vs.
DECISION TREE

14. DECISION TREES HAVE FLAWS…
DISCRETE

15. BUT THEY
COMMUNICATE
(if they’re shallow)
early splits provide
big picture…
fat leaves guide
feature engineering
…or reveal training
data problems

16. ITERATION

17. INTERPRETABILITY DELIVERS
Key search leader favors rule-based
approach for key scoring algorithms.
Replaced regression with decision
tree in local search model: gained
accuracy and insight.
Using trees to recognize spam,
analyze search abandonment,
model / quantify social proof.

18. GO DEEP vs INTERPRETABILITY
A KEY DATA SCIENCE TRADE-OFF

19. ON TO
INTERACTION

21. DON’T OVERPAY FOR PRECISION

22. BE FAST, CHEAP, AND 98% RIGHT
http://metamarkets.com/2012/fast-cheap-and-98-right-cardinality-estimation-for-big-data/

23. ARE PEOPLE THAT IMPATIENT?
tolerable wait time for web users
0.1s increase in latency significantly
reduces # of searches, ad revenue
tl;dr: YES

24. IMPATIENCE IS GOOD
SPEED MATTERS

25. INSIGHT

26. http://blog.takejune.com/archives/52334044.html

27. BE TRENDY AND NORMALIZE
vs

28. Sept. 11th
Abu Ghraib
Weapons Inspectors
SOLVE FOR INTERESTINGNESS

29. COMPUTE POTENTIAL INSIGHTS
APPLY HUMAN INTUITION

30. SUMMARY: Let’s have a
conversation with Big Data.
INTERPRETABILITY
INTERACTION
INSIGHT