Talk given at the Eco Club, Franco-American Institute, Rennes (2017-01-17)

1. Is Big Data Good or Evil?
Arthur Charpentier and Ewen Gallic
Eco Club
Institut Franco-Américain
Rennes, January 2017
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2. ● Arthur Charpentier ( @freakonometrics)
○ Assistant Professor, University of Rennes 1
○ Ph. D. in Applied Mathematics, KU Leuven
○ Fellow of the French Institute of Actuaries
○ MSc in Mathematics Applied to Economics, Paris Dauphine University
○ MSc in Statistics, ENSAE, Paris
● Ewen Gallic ( @3wen)
○ Ph. D. Student in Economics, University of Rennes 1
○ MSc in Econometrics, University of Rennes 1
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3. What is Big Data?
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4. Data from our activity
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Everyday, data are collected from our activities such as:
● Paying our basket at the grocery store, at the pharmacy, …
● Doing a Google search
● Listening to some music on Spotify
● Sending an e-mail
● Posting an update on a social network

5. Different kind of data are collected:
Data Created/Shared by us
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● Texts from conversations on our
phones or on Facebook, Twitter, …

6. Different kind of data are collected:
Data Created/Shared by us
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● Texts from conversations on our
phones or on Facebook, Twitter, …
● Photos posted on Instagram, Facebook,
Tumblr, Pinterest, …

7. Different kind of data are collected:
Data Created/Shared by us
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● Texts from conversations on our
phones or on Facebook, Twitter, …
● Photos posted on Instagram, Facebook,
Tumblr, Pinterest, …
● Videos uploaded on Snapchat,
YouTube, …

8. Different kind of data are collected:
Data Created/Shared by us
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● Texts from conversations on our
phones or on Facebook, Twitter, …
● Photos posted on Instagram, Facebook,
Tumblr, Pinterest, …
● Videos uploaded on Snapchat,
YouTube, …
● Data footprint
● …

9. Sensors record our activity:
Data Collected by Sensors
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● GPS tracking on our phones
Source: Michael Wallace
Source: Aaron Parecki, via FlowingData

10. Sensors record our activity:
Data Collected by Sensors
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● GPS tracking on our phones
● Activity trackers, Sleep trackers
Fitbit watch

11. Sensors record our activity:
Data Collected by Sensors
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● GPS tracking on our phones
● Activity trackers, Sleep trackers
● In-car tracking devices
YouDrive

12. ● Climate data from weather stations
Sensors record our activity:
Data Collected by Sensors
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● GPS tracking on our phones
● Activity trackers, Sleep trackers
● In-car tracking devices
● …
Weather Station

13. The 4 Vs of Big Data
Four specific attributes to define Big Data (the four Vs):
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VOLUME VELOCITY
VERACITY VARIETY

14. The 4 Vs of Big Data
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Source: IBM Big Data & Analytics Hub
= 2.5 x 1018
bytes = 2.5 billion gigabytes
~ 357,142,857 three hours HD movies
on Netflix

15. The 4 Vs of Big Data
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Source: IBM Big Data & Analytics Hub

16. The 4 Vs of Big Data
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Source: IBM Big Data & Analytics Hub
~15% of 2016 US GDP

17. The 4 Vs of Big Data
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Source: IBM Big Data & Analytics Hub
● Structured data, e.g.:
○ Time
○ Date
○ Value
○ …
● Unstructured data, e.g.:
○ Video
○ Podcast
○ Social Media Status
○ …

18. The angel...
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19. The Angel: Healthcare
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● Data-driven analysis to predict a
disease’s geographical spread
○ Ebola in 2014
Map of Ebola cases in West Africa from January
2014 to December 2015.
Source: World Health Organization

20. The Angel: Healthcare
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● Data-driven analysis to predict a
disease’s geographical spread
○ Ebola in 2014
● Google Flu Trend
○ Aims at predicting flu
○ Big failure in 2013
Divergence of Google Flu Trends
Source: How accurate is Google Flu Trends?
Keith Winstein (2013)

21. The Angel: Healthcare
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● Data-driven analysis to predict a
disease’s geographical spread
○ Ebola in 2014
● Google Flu Trend
○ Aims at predicting flu
○ Big failure in 2013
● Creation of Electronic Health Records
○ Better diagnostic
○ Reduced costs

22. ● Telemetry
○ Fairer premiums
○ Better knowledge of driving habits
○ Drop in number of accidents
The Angel: Insurance
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Source: TIA Technology
● Telemetry

23. ● Telemetry
○ Fairer premiums
○ Better knowledge of driving habits
○ Drop in number of accidents
The Angel: Insurance
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● Detection of fraudulent claims
○ In France, the insurance association
(FFA) estimates fraudulent claims to
amount 5% of claims
Fraudulent claims:
€2.5 Billion in 2015
Source: FFA via L'argus de
l'Assurance

24. 72%
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25. The Angel: Jobs
● Curricula processing
○ Faster
○ Reduce discrimination?
○ Help to find suitable candidates for a
position
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72% of résumés are never
seen by human eyes
Source: Cathy O’Neil, “Weapons of Math
Destruction” (2016) Crown

26. ● Adjusting schedules
○ During peak hours and off-peak hours
The Angel: Jobs
● Curricula processing
○ Faster
○ Reduce discrimination?
○ Help to find suitable candidates for a
position
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27. Nate Silver, the developer of PECOTA,
Editor-in-chief of FiveThirtyEight
The Angel: Sports
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● Forecasting Player Performances
○ Nate Smith’s PECOTA

28. ● Providing guidance
○ Kirk Goldsberry
The Angel: Sports
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● Forecasting Player Performances
○ Nate Smith’s PECOTA
Source: BallR by Todd W. Schneider
(Reproduction of Goldsberry’s chart)

29. … and the Demon
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30. ● Growing number of sensors to monitor
our “wellness”
○ Intrusive
○ Insurers get in the way
The Demon: Healthcare
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Apple Watch
● What about security of the standardized
medical data?
○ What happens if your future employer get his
hands on this kind of data?

31. The Demon: Insurance
● Moving towards the individual
○ Individual pricing
○ Opaque
○ Blind to inequalities
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32. The Demon: Jobs
● Optimized schedules
○ Just-in-time economy applied to human being
○ Work to live, or live to work?
○ Hits people in desperate need of money
● Curricula processing
○ Asymmetric information
○ No feedback on rejected candidates
● Algorithms designed to fire people
○ Opaque procedures
○ Unfair
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33. Recap
● Data are created or shared by us, or even recorded by sensors
● A huge amount is created every day, and comes in different forms (text, video, …)
● It may help to increase welfare, or understanding patterns around us…
● But it also contributes to an increase of inequalities
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34. To go further...
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35. 35
Thank you for your attention
Arthur Charpentier
@freakonometrics
Ewen Gallic
@3wen