This document summarizes a presentation on analyzing human data ethically. It discusses how data collection has become more intimate, consumers lack control over their data and distrust how it is used. Five principles of ethical data use are outlined: being beneficial, progressive, sustainable, respectful and fair. Case studies demonstrate how companies apply these principles in areas like continuous improvement, transparency and considering all stakeholders. The presentation argues for a privacy-first approach to data insights that provides aggregate, anonymized analysis to benefit both businesses and consumers in a way that builds trust.

1. Staying on the
Right Side of the Fence
when Analyzing Human Data

2. Susan Etlinger
Industry Analyst
ALTIMETER GROUP
Tim Barker
CEO
DATASIFT

3. Data Ubiquity and the Trust Imperative
What we’ll cover today
1
2
3
4
5
Principles of Ethical Data Use
How Social Networks are changing to privacy-first
approaches
Audience vs Individual Insights
Discussion / Q&A

4. 4
Susan Etlinger
Industry Analyst, Altimeter Group,
A Prophet Company
@setlinger
The Trust
Imperative
A Framework for
Ethical Data Use

5. 5
An Origin Story (2012)

6. 6
A Tipping Point (2013)
“Paging through the catalog, we
realized to our dismay that whoever
had sent us this thing knew us.
They’d nailed our demographic
precisely. They even knew what kind
of convertible car seat we’d want!
Who were these people, or should I
say, machines?!?”
− Alexis Madrigal

7. 7
“ “Legislation can’t keep up with
technology, which makes it a flawed
vehicle to govern what
happens in this space.”
− Judy Selby,
Partner, Information Governance
BakerHostetler

8. 8
1. Data Collection Has Become More Ambient—and
Intimate
2. Consumers Don’t Control Their Personal
Information
3. Consumers Report Distrust of Data Use
4. Trust is a Major Concern for CEOs
5. Distrust Has Quantifiable Impact on Business
Performance
Trust is a brand issue

9. 9
Consumers do not trust data use

10. 10
They feel “resigned”
“Most Americans disclose their personal
data to companies for discounts
because they believe that marketers will
harvest the data anyway.”
Joseph Turow, Ph.D., Michael Hennessy, Ph.D., Nora
Draper, Ph.D., “The Tradeoff Fallacy: How Marketers are Misrepresenting
American Consumers and Opening them Up to Exploitation,” University of
Pennsylvania Annenberg School of Journalism, June 1, 2015.

11. 11
Lack of trust has clear consequences

12. 12
“
“Just complying with the law is not
going to be nearly enough to make
consumers comfortable.”
− Jennifer Glasgow,
Chief Privacy Officer, Acxiom

13. 13
Principles of Ethical Data Use*
* Developed by the Information Accountability Foundation (IAF)
Beneficial
• Does our use of data benefit consumers as much as it benefits us?
Progressive
• Do we have a culture of continuous improvement and data minimization?
Sustainable
• Are the insights we identify with data sustainable over time?
Respectful
• Have we been clear, transparent and inclusive?
Fair
• Have we thought through the potential impacts of our data use on all interested parties?

14. 14
“Before conducting any type
of new analysis, we ask
ourselves whether it will
bring benefit to customers in
addition to the company. If it
doesn’t, we won’t do it.”
Joshua Kanter, Senior Vice President,
Revenue Acceleration, Caesars
Entertainment
Benefit in Action

15. 15
“Organizations should not create the risks associated
with big data analytics if there are other processes
that will accomplish the same objectives with fewer
risks.”
− Information Accountability Foundation
Progressiveness in Action

16. 16
Senate Bill 576, “GPS Data Privacy for Mobile
Devices,” (California)
“[R]equires that consumers get a clear
notice explaining how their location
information will be used and shared
when they install a new app.” It also
ensures that app users give express
consent before their geolocation data
can be collected and shared.”
Legislating Progressiveness

17. 17
Sustainability in Action

18. 18
Respect in Action

19. 19
Fairness in Action

20. 20
A Framework for Ethical Data Use

21. 21
The Sunshine Test
What would happen if all
the details of what you are
doing were out in the open,
in the light of day?
Photo: Madalena Pestana, CC 2.0

22. 22
“
“By knowing where the borders are, you
can innovate more around them.”
− Stefaan Verhulst
Co-Founder and Chief Research
and Development Officer
The Governance Lab (NYU)

23. How Social Networks are adopting privacy-first approaches
Tim Barker
CEO
DATASIFT.COM
#DSWebinar

24. April ’15
Topic data provides anonymized
and aggregate insights into content
and audiences on Facebook.
Nov ’15
Instagram introduces platform
policy change to restrict data
access to approved applications.
Trust is the currency of social networks.
May ’15
Linkedin limits API access to
select, approved partners.
API Changes in last 12 months to protect consumer data from misuse.
#DSWebinar

25. 25
It’s messy
Separate Signal from Noise
It’s text-based
Unlock meaning from text
Challenges in Extracting Insights
It contains personal data
Extracting insights while
protecting consumer privacy
Insights drive marketing investments in Social Networks.
But it’s a Big Data challenge.
Insights drive more marketing
spend on social networks.
Insights drive marketing
spend on Networks
#DSWebinar

26. 26
Enables an ecosystem of product-builders.
See datasift.com/partners for complete list.
Application
Builders
Agencies
We help networks build an
insights-driven ecosystem
Filter
Signal:Noise
Understand
Meaning
Explore
Insights
DataSift partners with Social Networks
to help them build an insights-driven ecosystem
Insights drive marketing
spend on Networks
Transform raw feeds of activity data into
insights into content, engagement and
audiences.
DataSift technology builds
insights, protects identity
DataSift helps Social Networks build an insights-driven ecosystem
Helps developers build compliant, compelling insights.
#DSWebinar

27. 27
DataSift platform connects to the real-time feed of Posts,
Comments, Likes.
Facebook Topic Data: Privacy-First Approach to Insights
Surface Insights from activity
across Facebook
Built from posts, comments, likes
Aggregate and anonymized results
#DSWebinar

28. 28
Anonymized and Aggregate approach.Analysis that spans all of the
available data.
Multi-Dimensional
data analysis
Net-Positive for Consumers +
Businesses
“Bigger Data” for
Bigger Insights
Why a Privacy-First Approach Wins
“Better Data” for Audience
Insights
#DSWebinar

29. “Bigger Data” for Bigger Insights
Comparison of volumes of engagement relating to
an automotive brand across 7-day period.
FACEBOOK PAGES
~1,000
Posts and Engagement
on your own Facebook
Pages
TOPIC DATA
~70,000
Brand-related
Posts and
Engagement across
all of Facebook
#DSWebinar

30. “Better Data” for Consumer Insights
Create Insights from Multi-Dimensional.
30
Gender: Male
Age Range: 35-44
Region: California, USA
CONTEN
T
Negative
Positive
DEMOGRAPHICS
SENTIMENT
Automatic classification
of related topics
e.g. Star Wars VII (Film)
TOPIC ANALYSIS
CONTEN
T
LINKS
Analyze
URLs shared
across Facebook
Engagement and Demographics around
Likes, Comments and Shares
ENGAGEMENT
Can’t wait to take the kids to watch Star Wars VII
CONTEN
T
Privacy-safe
aggregate analysis of
text
TEXT ANALYSIS
#DSWebinar

31. Advertising Agency for a Drink Brand
31
Advertising Agency wanted to understand…
How women engaged with their client’s beverage
brand and with hot drinks.
Deeper understand of the media consumption /
magazine stories which most engaged their target
consumers
#DSWebinar

32. Identify the publications, stories & celebrities
which drove most engagement amongst the
target audience segment
Recommended Actions
๏ Look at featuring different drinks when advertising in different markets and to different demographic groups
๏ Use story insight for media placement as well as for identifying potential influencers
1st
2nd
3rd
LATTE
HOT CHOCOLATE
ESPRESSO
HOT CHOCOLATE
LATTE
CAPPUCINO
CAPPUCINO
LATTE
ESPRESSO
USA UK GERMANY
Under 35s, as a % of brand’s total
engagement
47%
Client Brand Competitor A Competitor B
31%
58%
Celebrity stories driving most engagement
There are big variations in preferences for
hot drinks across nations and demographic
groups
The brand found that they suffered with the
lowest relative engagement amongst
millennials
Insights into Content and Audiences
JENNIFER LAWRENCE
SHARON STONE
TAYLOR SWIFT
KYLIE JENNER
JUSTIN TIMBERLAKE
#DSWebinar

33. 33
+
WinLose
Zero-Sum Game Positive-Sum Game
WinWin
+
- Data is anonymized to protect identity.
- Deeper audience-level insights possible by using demographics /
interest-graph data added by social networks.
- Insights built on a foundation of data privacy and trust.
- To evolve from audience-level analysis to individuals, use a social-
network opt-in to allow customers to control data they want to share.
Privacy does not have to be a zero-sum game
- For business to win, consumers have to lose.
#DSWebinar

34. Q&A

35. THANK YOU
http://bit.ly/ds-reasons
Resources
BALANCING INSIGHT AND TRUST
Altimeter White Paper
http://bit.ly/insightvstrust
10 REASONS FACEBOOK TOPIC DATA WILL
CHANGE YOUR WORLD
DataSift eBook