This document discusses data, the cloud, and controls on data. It begins with an agenda covering data, the cloud, and controls on data. It then reviews the internet, convergence of technologies, and big data. The document discusses how the internet gave mobility to computers through smart phones and how this led to convergence. It explores new types of data available through big data and how this enables new insights and experiments. The document also covers the cloud computing model and issues around location of data, cross-border transfers, and jurisdictional regulation of data in the cloud.

1. DATA
HAS ALREADY CHANGED YOUR LIFE AND YOUR FUTURE
2017© Dr. Stan Benda

2. AGENDA
CLASS
• Data
• Cloud
• Controls on Data
QUICK REVIEW
• Internet
• Internet “Regulation”
• Domestic
• Net Neutrality
• Convergence
• International Censorship
• Values
• Politics
• Security

3. LEGACY
• Quiz
• Questions about
• Net
• Net “Regulation”
• Convergence

4. REVIEW

5. PP & PM

6. INTERNET:
CARRIAGE &
CONTENT

7. CONVERGENCE
INTERNET GAVE
COMMUNICATIONS TO
COMPUTERS
SMART PHONE GAVE
MOBILITY TO COMPUTERS

8. PP / PM / CARRIAGE /CONTENT / DATA =
CONVERGENCE
• Telecommunications became
broadcasting; broadcasting became
telecommunications
• You can use your phone for
• Telecommunications
• Broadcasting
• Reading
• Monitoring
• Environment
• Personal
• IoT
• Laws lag, CRTC gave freer reign.

9. SO HOW DID THE LAW RESPOND TO
CONVERGENCE
CRTC DECISIONS
• No regulation of New Media
• Net Neutrality Decision – ITMP
• Wholesale Billing
• Unbundling of TV on cable
• Loosening Cdn content requirements
PREMISE
• Innovation
• Transparency
• Clarity
• Competitive neutrality
• Consumer choice
• Pricing Discipline

10. INTERNET
Allows computers to
communicate
• Knowledge
• IoT
• Regulation
SMART PHONES
Mobility to Computers
Convergence
• Telecommunications
•PP
• Broadcasting
•PM
• Carriage
• Content
• Regulation
BIG DATA
Quantification of life
• Uncovering Networks
• New sources of knowledge
• Regulation
SOCIAL MEDIA
Mass to Mass
Conversation &
Broadcasting fuse PRIVACY & FOE

11. DATA N = EVERYTHING;
AND THE LAW = ?

12. OR THINK OF DATA AS SOMETHING YOU…
• Create
• Identify
• Associate
• Mine
• Extract
• Purify
• Blend
• Convert
• Aggregate
• Archive
12

13. APPLICATION OF
DISPARATE DATA
• US Navy Charts 1850
• 311 calls:
• Rats sightings,
• Chinese Restaurants with
health code violations,
• number of pedestrians at
certain locations / times,
• youngest zip codes,
• highest crime zip codes
13

14. DATA: CONNECT THE GARBAGE DOTS?
IF YOU CANNOT MEASURE IT
YOU CANNOT MANAGE IT
MEASURE WHAT?
• Weight
• Season
• Holidays – Stat and Religious
• Weather
• Neighbourhood (taxes)
• Traffic patterns
• Sporting Events
• Demographics
• Flu and Cold Season
14

15. DATA DATA DATA – TRIVIAL PROFOUND
OR OBSCENE?
15

16. SIZE OF DATA – WEATHER WAS THE FIRST
INDICATOR OF THE QUANTUM INVOLVED
• Slices the world into small squares
less than ½ mile X ½ mile
• Each square is sliced in about 150
levels per section altitude sectors
• Insert wind, moisture, clouds,
temperature
• Insert History
16

17. DATA
• How is it generated?
• Who is using it?
• What does it say about you?
• What does the law say, if
anything?
• Accumulation
• Content
• Use
• Storage
17

18. ALL DATA IS NOISE – UNTIL YOU SEE A PATTERN
Links different information from different sources historically 20 sources for a credit
rating, now thousands of sources
• Structuring equity derivatives
• Reducing loan losses
• Determining where you are physically and
• offering you coupons to nearby stores
• Baby coupons if shopping pattern indicates pregnancy
• Beer coupons for father since no more trips to pub
• if it is lunch time and it knows you like Italian it will send recommendations to nearby
restaurants
18

19. CONNECT THE BLACK DOTS

20. CONNECT THE DOTS?
TED Manuel Lima, A Visual History of Human Knowledge
TED, How Algorithms Shape our World, Kevin Slavin
Starling Murmuration

21. (BIG) DATA

22. BIG DATA
– IS IT OIL, FOOD
OR SOIL IN THE
INFORMATION
ECONOMY?

23. BIG DATA IS
BETTER DATA
THAT’S THE
GOSPEL
• TED Big Data is Better Data
Kenneth Cukier
ASK YOURSELF what are the impacts of big data on:
 science
 government
 commerce
 individual
 And so the role if any of the law

24. BIG DATA IS
NOT IMMUNE
FROM OLD
FASHION BIAS
• Cathy O'Neil - Blind Faith in BD must end
• How can you be victimized by big data
• Wrong algorithm?
• Embedded bias / opinions?
• Codified bigotry or sexism?
• Illusion of science / objective?
• Automate status quo?
• Data laundering
• Distinction between intentions and consequences?

25. BIG DATA &
HUMAN
INSIGHTS
• Tricia Wang - BD and Human Insights
• Investing in big data projects generally fail?
• Noika or Blackberry stories?
• How to use big data?
• Quantified CONTAINED systems predictable
• Big data alone gives the illusion of all
encompassing knowledge
• Quantification bias?
• Binge watching & House of Cards scenarios?
• Sebastian Wernicke - BD and Hit TV Shows

26. BIG DATA, THE
BEGINNING
• Flu Mapping by Google
• Looked for correlations
between the frequency of
certain search queries and
the spread of flu
• 45 million models and
came up with 45 search
terms

27. DEFINITION
OF BIG DATA
• Things that one can do at a large scale that CANNOT
be done at a smaller one, to extract new insights or
create new forms of value in ways that change markets,
organizations, the relationships between citizens and
governments
• Big Data doesn’t get old
• Big Data doesn’t give causality only correlation
• Big Data doesn’t provide “why” only “wha”t
• Big Data can be live and immediate
• Big Data is about PREDICTION

28. BIG DATA’S
POWER COMES
FROM
New types of data
Honest Data
Enables subsets
Allows casual experiments

29. NEW TYPES OF DATA (OVERCOME LIES)
• People confess on google
• What do porn sites tell us
• Unemployed surf porn more, so porn
searches a clue to unemployment
• What to inquiries tell us
• Survey: sex once a week; inquiry: sexless
marriage
• Bodies of Data
• Horses, wine
• Words as data
• Pictures and faces as data

30. BIG DATA: HONESTY THROUGH “CONFESSION”
• Searches
• by gay individuals in discriminating
jurisdictions
• by people concerning plastic surgery
(insecurities)
• For abortions in restrictive jurisdictions
• Not a echo chamber?
• Few massive news sites
• People with strong opinions migrate to
opposing sites
• More friends on FB than in life so broader
spectrum of views
• Prejudice Revealed
• Black: rude, racist, stupid, ugly, lazy
• Jews: evil, racists, ugly, cheap, greedy
• Christians: stupid, crazy, dumb, delusional, wrong
• Muslims: evil, terrorists, bad, violent, dangerous
• Truth about Customers
• P. 155

31. BIG DATA: ZOOMING IN
• Who is your favourite baseball team:
• Males determined at age 8
• Females age 22
• Political Views
• Fixed at age 18
• Chances of being rich if parents
are poor
• States below Canada and UK
• But if geography inserted San
Jose and Washington D.C.
equal or exceed Canada

32. BIG DATA: EXPERIMENTS AND CAUSALITY (A/B
TESTING)
• Experiments can millions of
dollars and years to execute
• Net: randomized experiments
are cheap and fast
• Click on ads, which shade of
blue works better
• Which Obama poster works
better
• Which Headline gets readers

33. WHAT IS ’BIG’
• Banks now are keeping “zettabytes” of data
• I zettabyte = 1 trillion gigabytes
• See Economist, May 19th, 2012, Banking Insert, p. 11.
33

34. 34

35. STATISTICS OR DAMN LIES
• 2007 only 7% of the world’s data
was analog
• Google processes 24 petabytes of
data a day
• Facebook tracks the 3 billion “likes”
a day
• Sloan Digital Sky Survey
(astronomy)
• 2000 amassed more data in
weeks in the previous human
history
• 2010 amassed 410 terabytes in
10 yr
• 2016 will acquire 410
terabytes every 5 days

36. BIG DATA AND
PERSPECTIVE
• TED, The Beauty of Data Visualization,
David McCandless
• ASK YOURSELF
 Can data sets change mind sets?
 Are data sets dangerous? How?
 How can data sets influence law
making?
 Recall the limitations?

37. BD TRAITS &
NEW PROCESS
• Granular possible, not before with samples
• More trumps better
Messy but big
Exactitude not important
• Correlation is a proxy for causality
• Non-linear relationships (networks) identified
• Probability sought
Shift away from causality

38. DATAFICATION
• Taking all the information possible irrespective of relevance and converting /
quantifying it into data
• Determine latent implicit value of the information
• See connections in real time and outliers (credit card fraud)
• Birth of “culturomics” you an mine the use of words through history, identify
censorship
• Spatial location (GPS)

39. QUANTITATIVE CHANGES
LEADS TO QUALITATIVE
CHANGES
• Seeing
• Drawing
• Photographing
• To 26 photographs a
second (movie)

40. DIGITIZATION & DATAFICATION
THE BOOK AS AN EXAMPLE
AMAZON
• Sells works which can be digitized
• Expressions / Reading Words
• CR issues
• Experience the contents
GOOGLE
• Silos of data
• Improve voice recognition
• Content personalization
• Cultural genome – trends over
centuries
• Key ideas evolution
• Data / Reusing Words
• Works for search, analysis, mining
CR?
• Value Extraction not just information
retrieval

41. CARS & DATAFICATION
• Time of day
• Speed
• Location
• Braking
• Distances
• Shift from pooled risk to individual
action in insurance
• UPS used it to follow the most
efficient route(s) in 2011
• Shaved 30 million miles
• Saved 3 million gallons of fuel

42. HUMAN RELATIONSHIPS –
DATAFICATION
• Looking at the past – Linked In
• Looking at the present – Facebook
• Looking at thoughts / interactions – Twitter
• Metadata on Twitter 33 items
• Looking at your purchasing habits – Loyalty Cards

43. DATA EXHAUST – SEARCH
ITEMS
FACEBOOK
• Why people would take action
• Because their friends did
• Site redesigned to make friends
activities visible
GOOGLE
• Used its search questions to identify
misspelled words
• Provided alternative wording
• Used that to
• improve its spell check
• confirm the accuracy of its proposed
words
• Built up auto-correct, auto-complete,
google docs, translations
MICROSOFT
• Used it to improve its spell
check

44. IMPLICATIONS
MINDSET V DATASET
• MoneyBall
• Marketing
• Investing
• IoT – Airplane telemetry, Smart Meters
• Oval Office / Legislatures

45. IMPLICATIONS - PRIVACY & FREEDOM
• Increased the risk BUT also increased the character of
the risk
• New uses for old data, but permission to use ossified
• Anonymization does not work with Big Data
• Government – e.g. NSA stores 1.7 billion emails /
phone calls a day in 2010 : looking for relationships –
the penumbra of data that surrounds a person
• Minority Report policing
• Remember Big Data gives correlation not causation

46. YOU GENERATE THIS EVERY DAY
• TED, Birth of a Word, Deb Roy
• ASK YOURSELF:
Is big data about the big picture or the microscopic picture
Does big data discover the granular level of our lives and to
what ends?

47. LETS LOOK AT THE PATH AGAIN
47
Internet
• Computers Communicate
Smart Phones
• Computer Mobility
Convergence
• Telecommunications and
broadcasting
• IoT
Big Data
• Quantitative Changes
• Predictability

48. CONSEQUENCES – WHAT DO WE PROTECT?
Control of the “press” controls
knowledge
Use of the big data “threatens” the
sanctity of the Individual?

49. THE CLOUD A PLANETARY BRAIN?
49

50. CLOUD
50
• What is it?
• Where is it?
• Who is it?
• Cloud Infra-
structure
• Cloud Issues
• Associated Privacy
Issues
• Bankrupt
Dating Site

51. WHERE ARE
ALL THESE
ZETTABYTES?
IN THE
CLOUD!
A style of computing where scalable
and elastic IT capabilities are
provided as a service to multiple
customers using internet
technologies
51

52. DEFINITION
• On demand self service
• Broad Network Access
• Resource Pooling
• Virtualization: pooling of storage, network, OS, computing
(processing/memory) are bundled together
• Rapid Elasticity
• Measured Service
52

53. WHY?
NO PROCURE / MAINTAIN / OPS + NO INFRASTRUCTURE
• Built in Business continuation
• Improved Network Access
• More/ Better Application
development tools
• Lower Unit IT costs
• Avoid investment for peak periods
only
• Access to skills
• Better Security
• Common Platform for multi-
organization projects
53

54. WHY? 2
• Better confidentiality
• Reducing Time spent on IT
• Reduce IT risk
• Commodification of storage
capability
• Start up in any location globally
• Minimize need to application
development
• Reduce / Refocus existing IT
resources
54

55. WHY? 3
• Location independence
• Seamlessly accessed and
released by demand
• Charged by volume used
• Free then threshold
charge
• Reduction in capital expenditures for
infrastructure
• Dynamically improving without client
upset
• Green – energy efficient to have
servers in huge banks
55

56. WHY 4
TRADE SECRETS ARE IN THE
CLOUD AND INACCESSIBLE

57. CLOUD PARTIES
• Cloud Broker
• Service Intermediation
• Service Aggregation
• Service Arbitrage
• Cloud Auditor
• Independent security
• Performance monitoring
• Cloud Carrier
• Connectivity & transmission
• Cloud Consumer
• Consumer / SME / Corporate –
Public
• Cloud Provider
• Software (SaaS)
• Platform (PaaS)
• Infra-structure (IaaS)
57

58. 58

59. SAAS, PAAS, IAAS DELIVERED
CLOUD TYPES
• Service deployment
• Service orchestration
• Service management
• Security
• Privacy
CLOUD PROVIDER
• Public
• Private
• Community
• Hybrid
59

60. TERMS OF SERVICE (SLA)
• AUPs (acceptable use terms)
• Non criminal (no spamming, phishing)
no obscenity
• Facebook – no convicted sex offenders
• Iron Mountain – only business backup
• No access to those resident in Iran,
North Korea, Cuba
• For what
• Security
• Control
• Configuration
• Whose law
• Arbitration
60

61. DISCLAIMER
• No liability of loss of data, integrity,
availability, confidentiality
• Obligation on consumer to backup or
encrypt
• Charge extra to avoid the foregoing
61

62. END OF
CONTRACT
Data kept for a set
period of time then
deleted
Deleted immediately
May be deleted at
any time post
termination
Delete if account
dormant for 120
days
62

63. DISCLOSE TO 3P
If necessary in good
faith to protect
cloud provider
01
You authorize
disclosure to police
forces
02
IBM – all cloud data
deemed non-
confidential
03
Facebook Rules
04
63

64. MONITORING
Silent
For statistical purposes
For self-preservations, AUP enforcement
64

65. JURISDICTIONAL
REGULATION
A WORK IN PROGRESS

66. LOCATION OF DATA
• Anywhere
• Amazon, You select
Regional zones
• USA, long arm
statutes
• EU, on the continent
• Protection in
transit??
66

67. CROSS BORDER
ISSUES
67
• Uniting and Strengthening
America by Providing
Appropriate Tools Required to
Intercept and Obstruct
Terrorism Act
• USA Patriot Act
• S. 215, Access Records held in
the Private Sector (Airlines,
Banks)
• US Subsidiary in Canada
• Buffalo problem?

68. EU PROTECTION OF PRIVACY V
PERSONAL DATA
Any information related to the
identified or identifiable natural
person; an identifiable person is one
who can be identified, directly or
indirectly, in particular by reference to
an identification number or to one or
more factors, specific to his physical,
mental, economic, cultural or social
identity Directive 95/46/EC

69. PRIVACY – Next week
MIDTERM – 2 weeks after 1st March
69

70. PRIVACY PREP FOR NEXT CLASS
• The class in split into theory and law
• Theory: read Brandeis, Prosner, and Peppet
• Law: read PIPEDA etc
• Ask yourself
• Impact of human nature on privacy
• Are we protecting the vault or the contents of the vault
• What about so-called abuses and control by Google, Facebook

71. TAKE AWAYS??
• What surprised you?
• What one thing sticks in your
mind?
• What if anything blew you away?