Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.

1. Notes from the
OBSERVATION DECK
3
A Data
Revolution
IN THIS ISSUE
Big Data
Defined
Our Digital
Footprint
Big Data
In Action
Mobile Data
and Privacy

2. About
NOTES FROM THE OBSERVATION DECK IS A QUARTERLY E-ZINE CREATED
THROUGH THE BRAND STRATEGY GROUP AT TEAM DETROIT.
This publication will provide you with an examined look at the
interesting phenomena and trends taking place around us today. We
present them to you with the hope of sparking broader conversations,
debates and ideas. Please use this e-zine as a resource for innovation,
inspiration and enjoyment.
In this issue, we’re featuring the Big Data landscape and how it has
been influencing consumer insights. If you are interested in
telling us what you think or contributing to future issues, please contact intelligence@teamdetroit.com.
Enjoy!

3. Notes from the
Observation Deck
Vol.3
Editor-in-Chief
Gabrielle George
The Major
Players
Creative
in the
Contributors
Online
Video
Industry
Assistant Editors
Morgan Pomish
Cameron Quattrone
Alex Leonard
Derrick Bollin
Jessica Vaughn
Jenna Thompson
Kelly Schneider
Kevin Conlon
Kristine Taylor
Nicholas Oliver
Russ Hopkinson
To everyone else who
contributed, thank you.
While Youtube is the most widely used
source for accessing online video,
a handful of other services have
successfully inserted themselves into
the conversation as well. Netflix,
Hulu and Vimeo have carved out their
niche in the online video sphere, and
are jockeying for position in the race
to online video supremacy. Below is a
dive into who they are and how they’re
developing in the landscape.
YouTube
YouTube is a video-sharing website that

4. Agenda
A Data Revolution
1
A Force of Change
Letter From the Editor
2
Big Data: Defined
The Data Era
Connecting the Dots Through Data Visualization
Our Digital Footprint
Predictive Personalization
3
Big Data in Action: Observations
Sephora ColorIQ/Turning Big Data Into Beauty
IBM SlamTracker/Home Court Advantage
Echo Nest/Music Intelligence for Smarter Listening
Data in the Driver’s Seat /Fiat & Volt
4
Mobile Data and Privacy: At a Glance
Big Data in the Mobile World
Privacy: Walking a Fine Line
5
Forward Thinking: Actionable Insights
Wrap-Up
Glossary

5. 1
A Force of Change

6. Letter From the Editor
We’re living in a society where rapid
innovation is creating a more informed
world. Since 2001, we’ve entered what is
referred to as the Big Data Age — a logical
progression for a technology-driven world.
Big Data provides new tools that help
us spot trends, unveil insights for mass
and niche targets, and leverage untapped
opportunities in a much more informed and
efficient manner.
of information gathering to fill the entire
ocean. The power of Big Data lies in its
ability to translate streams of information
into predictive insights, allowing companies
to dig deeper into business solutions. With
the help of data scientists and specialized
computer systems, companies can better
manage data and identify emerging patterns
that give movement to consumer behavior.
In this issue of Notes from the Observation
Deck, we take a deep dive into the realm of
Big Data. In short, Big Data is a collection
of information that’s too big to be handled
by common computers. If bytes were
buckets, it would only take about 20 weeks
In this issue, we’ll explore the Big Data
era and prove why it is a driving force of
change. We hope to introduce you to needto-know terms, best industry practices and
how to translate data into killer creative
ideas.
Thanks for reading!
Gabrielle George
Notes from the Observation Deck: A Data Revoltion
6

7. 02. Big Data: Defined
It seems hard to believe that the
Internet was commercialized
less than 20 years ago; that in
1995, people were still trying
to understand the meaning of
an “@” symbol, and how to
pronounce the strangest combination of punctuation and
letters they’d ever seen, “.com.”
Let’s not forget Facebook
and Twitter, sites that didn’t
even exist 10 years ago, which
are now deeply woven into
the world we live in today.
Aside from allowing people to
connect to the things in which
they are interested, these sites have
given companies the opportunity
to collect a lot of never-before
known information and behavioral
data on consumers. Today, the
depth and width of this data is
bigger than ever before, aptly
called Big Data. It influences
the way we make decisions, how
we run businesses, interact with
one another and even how we
live our daily lives. Big Data is
not a trend or a tool; it’s an era.
An Oracle study of more than 300
executives in the U.S. and Canada,
released in July 2012, indicated
93 percent believe they’re
losing revenue opportunities by
not being able to leverage the
information available to them.
Big Data
Big data is a collection of
information so large and complex
that commonly used software
tools cannot possibly capture,
manage and process it. It is
greater in volume, variety and
velocity than normal data sets.
Volume:
To give context to just how much
volume Big Data really has, consider this statistic: every two days,
we create as much information as
we did from the dawn of civilization up until 2003. Big Data is
currently measured in petabytes
and exabytes, but by the end of
2015, annual global IP traffic is
expected to reach the zettabyte
threshold. If an 11 oz. cup of
coffee represents one gigabyte,
then one zettabyte would have the
same volume as the Great Wall of
China (Cisco Systems Report).
Variety:
Big Data is diverse because it is
being collected in myriad different
formats, ranging from tweets
and ad clicks, to RFID and GPS
signals. The collection of data
from this variety of formats and
types is what makes Big Data
processing multi-structured.
Velocity:
Input: A majority of incoming
data is being collected in real time.
Derived Data: Derived data,
however, only moves as quickly
as the computers that process
it. High velocity, or predictive
data, requires highly specialized
computers that have the ability to
process multiple types of data.
Facebook’s system processes 2.5
billion pieces of content and
500+ terabytes of data each day.
It’s pulling in 2.7 billion “Like”
actions and 300 million photos
per day, and it scans roughly
105 terabytes of data each
half hour (Facebook, 2012).

8. a wide variety of
data is captured at an
extremely high velocity
the data is collected
in extraordinarily
large volumes
Information
Stored
Information
Processed
Usable
Information
Notes from the Observation Deck: A Data Revoltion
8

9. CONNECTING THE
DOTS THROUGH DATA
VISUALIZATION
How Humans Deal with Data
Data visualization is a creative way of presenting relevant data through visual
interpretation. Putting data in a more artistic format helps tell stories about
numbers. Data visualizations can take many different forms, ranging from
computer graphics and animations to sculptures and music. (Please see page
16 for a data visualization.)
Our Digital Footprint
People are using new tools to manage their personal data, present it in different
ways and discover how much of their information is publicly available.
Companies such as Foursquare and Twitter leverage personal information to
identify user locations and behaviors. Personal data curation will be a key feature
that allows consumers to take greater control of their information.
With over 129 million credit cards in circulation, MasterCard® is sitting on
top of some of the best shopper data in the world. This past holiday season,
the company packaged its customer transaction data and sold it to advertisers
through a program called MasterCard Audiences.™ Categories such as holiday
travelers, last-minute shoppers and big spenders provide marketers with a
deeper understanding of consumer values and alignment with marketing
objectives.
Customer service is one area that’s set to be upended for the better, thanks to
the application of Big Data. Neiman Marcus is piloting an iPhone® app dubbed
“NM Service” that enables sales staff to hyper-personalize the treatment
of shoppers who use the app. Sales associates can view these customers’
purchase history, as well as items they have “favorited” via the app, then tailor
recommendations accordingly. They are even able to stock dressing rooms with
relevant items when a customer, who has used the app, enters the store.
The more data that you have,
the better the model that you
will be able to build. Sure,
the algorithm is important,
but whoever has the most
data will win.”
– Gil Elbaz,
CEO of Factual
Increasingly, savvy consumers
will start to make the most
of their lifestyle data by
turning to brands that use
this data to proactively offer
customers help and advice on
how to improve their behavior
and/or save money.”
– Trendwatching

10. HOW MUCH DATA IS IT?
1 Megabyte
A Small Novel
1 Gigabyte
10 Yards of Books
2 Petabytes
All U.S. Research Libraries
(Source: whatsabyte.com)
50 Megabytes
An Encyclopedia
1 Terabyte
50,000 Trees Made Into Paper and Printed
5 Exabytes
All Words Ever Spoken
Notes from the Observation Deck: A Data Revoltion
10

11. OPINION
Predictive
Personalization
JESSICA VAUGHN
Senior Trends Strategist
JWT

12. “ ait, how did you
W
figure that out?”
Taking the creep
factor out of Big Data
application.
Tapping into Big Data has obvious advantages
for marketers, such as more accurate consumer
insights and better-informed decision-making in
real time. However, it will take time for consumers
to get over the creep factor associated with the idea
that so many of their actions and preferences are
being tracked and analyzed as they go about their
daily lives.
The ability to process unstructured data combined
with the proliferation of new data sources means
marketers will become much more adept at getting
the correct product or message in front of the
right consumer, at the right moment. However,
turning this new treasure trove of information into
actionable efforts must be handled gently. Take
The New York Times report on Target calculating
“pregnancy prediction scores” that determine
not only whether a woman is pregnant but her
due date within a small margin of error. In one
instance, Target mailed coupons for baby goods to
a high school student whose father was unaware
of her pregnancy. He stormed into Target in a
rage, but soon learned his daughter was indeed
expecting. Oops.
Photo credit: Ford Archives.
Consumers will take some time to fully accept that
marketers can predict an unnerving amount of
information about their lives. A 2012 survey among
U.S. and U.K. consumers shows that the majority
of people still feel “creeped out” if a retailer sends
offers tied to an impending life event. Many say it
feels like Big Brother is watching over them, and
receiving customized offers based on personal habits
and preferences makes them feel anxious.
Current perceptions aside, smart use of Big Data
can deeply enrich the brand experience, and there
are consumers looking for a more personalized
approach. Me-centric consumers, armed with
information at their fingertips and an array of
social media platforms where they can broadcast
their thoughts, are accustomed to personalized
recommendations from Netflix, Amazon and the
like. They expect similarly relevant information
across the board, but can’t always find highly
personalized offerings and experiences in today’s
environment. Marketers who fail to address
individual needs will start to fall flat with these
consumers, especially Millennials, who are more
open to personal data collection and expect brands
to use their personal data to tailor offerings.
Proper application of data-based programs and
common sense courtesy for privacy can go a long
way in improving brand experiences and deepening
consumer loyalty. As marketers learn the habits
and sentiments of individual consumers, they’ll
have to be smart about how they implement this
knowledge, or risk coming off as invasive. Marketers
will need to assure consumers that they’re to be
trusted with personal information and explain the
purpose behind collecting it. With open dialogue and
increasing applications that make consumers’ lives
easier, people will be more likely to warm to the idea
of highly personalized targeting.
Notes from the Observation Deck: A Data Revoltion
12

14. 3
Big Data in Action: Observations
Sephora ColorIQ
IBM SlamTracker
Turning Big Data into a Beauty Tool
Home Court Advantage
Beauty mega-giant Sephora is pioneering a
personalized service for its clients. In a partnership
with the global color authority Pantone, Sephora
created the most advanced foundation color-matching
service available in North American beauty retail.
IBM recently took advantage of its Big Data through
a sponsorship of the US Open tennis championship,
using data to create a new experience for tennis fans.
The company created an app called SlamTracker,
which provided live video, competition highlights
and real time data and statistics that enhanced the fan
experience in a way never done before. By leveraging
the company’s own analytic algorithms and real
time statistics of the players, the app predicted what
each player needed to do to improve their chances
of winning against opponents. For example, IBM
demonstrated how a player like Andy Murray’s best
chances of winning the tournament were in his
ability to win more than 43% of three to eight shot
rallies. Andy Murray went on to accomplish this 49%
of the time en route to his first US Open title.
Sephora’s ColorIQ utilizes two devices during
the foundation consultation with the client. First,
the client’s skin tone is captured with the Pantone
CAPSURE,™ a handheld device that evaluates the
client’s tone in less than two seconds. From that
device, an official PANTONE SkinTone number
is assigned to the client. Then, the color consultant
inputs the number into Sephora’s Universal SkinTone
Library (on an iPad®), which holds over 1,000
foundation SKUs across all of Sephora’s brands to
determine which products offer a precise match.
Sephora has collected data from women nationwide
and combined it with the new technology. As a
result, clients are matched with the perfect shade
of foundation based on thousands of choices.
Choosy shoppers can filter options even further by
indicating a brand preference, foundation type and
more. Sephora plans to have ColorIQ technology
in all its stores by the end of 2013 – turning Big
Data into a beauty tool.
Image Credit: Alex Leonard
The IBM SlamTracker brought fans closer to the
matches, their favorite players, and justified its sponsorship presence in the eyes of fans and consumers
at the event. The key to IBM’s success was its ability
to communicate the brand’s capabilities in a meaningful and engaging way. The same technology used
by SlamTracker is representative of IBM’s business
processes, to leverage and effectively use data to make
smarter business decisions. By communicating at an
event like the US Open, IBM demonstrated the
company’s ability to translate its data in a more
human-friendly way.
Notes from the Observation Deck: A Data Revoltion
14

15. Echo Nest
Music Intelligence for Smarter Listening
“Billy Jean”
Michael Jackson
It has been estimated that 97 million songs exist in
the world today, and new songs are being written
all the time, creating infinite amounts of musical
information. Managing that kind of data is a daunting
task, but music intelligence company Echo Nest has
positioned itself as an industry leader.
Echo Nest uses Big Data analytics to aggregate
and index massive amounts of music data, helping
application developers build smarter music apps. The
company has the world’s largest repository of music
data with over 30 million songs and more than a
trillion data points.
Start infinite
Unlike Pandora® internet radio, which relies on
human input to categorize songs, Echo Nest’s song
analysis is done automatically using the Echo Nest
analyzer. The platform constantly searches the Web
for music and related content, gathering data about
a song’s key, tempo and style, as well as reviews and
even consumers’ tweets. Echo Nest markets its data
to power popular music services’ custom playlists and
radio stations such as iHeartRadio,® Spotify, Nokia
and VEVO.
Infinite Jukebox
Songs are broken down beat-by-beat, looking at pitch,
timbre, loudness, duration and the position of the beat
within the song. An analysis is run on all the beats,
finding each one’s nearest similar-sounding neighbor.
As the song plays, the algorithm determines whether
it will play as normal, or if it will jump to a different
part of the song that sounds similar to the current
beats. The result is an infinitely looping version of
the song. Each track is turned into an organic, circular
visualization.
“Take 5”
Dave Brubeck

16. Notes from the Observation Deck: A Data Revoltion
16

17. OPINION
Data in the Driver’s Seat
FIAT AND VOLT
RUSS HOPKINSON
Cross-Channel Strategist
In-vehicle technology is consistently ranked as one
of the top desires for potential automotive buyers.
Much of the data and sensors that can improve the
future of driving and enable more human-computer
interaction already exist. A car takes inputs in the
form of personal and environmental data, processes
it, presents options and makes changes to the driving
experience based on the driver’s response.
What’s remarkable for the auto industry is that cars
had onboard computers as early as 1968. The car in
your driveway probably knows if there is a passenger
in the passenger seat, the oxygen levels in the air,
the degree of turn in the steering wheel, the location
of the vehicle, the temperature in the engine, in the
passenger compartment and outside the vehicle, if
it is raining, how far you are from the car in front

18. On the one hand, information wants to be expensive, because
it’s so valuable. The right information in the right place just
changes your life. On the other hand, information wants to
be free, because the cost of getting it out is getting lower and
lower all the time. So you have these two fighting against
each other.”
– Stewart Brand, Author, “Whole Earth Discipline”
of you or behind you, and if the road is bumpy. Up
until recently, all of these sensor outputs were mostly
invisible to users, and were for vehicle performance
and diagnostics. But marketers have started to
grasp the importance of adding value to customer
experiences and have been playing with data as a
way to do that.
Case in point: Fiat Eco Drive
Using a little bit of vehicle data, Fiat analyzed
personal driving habits and offered tips on how to
change driving behavior to optimize fuel efficiency.
The results – 30,000 registrants reduced CO2
emissions by 2.6 million kg, and AKQA won a Grand
Prix Cyber Lion at Cannes.
Customers have started to realize that any data they
produce is their own and has value. If companies do
not meet this customer need, someone else will.
Case in point: VoltStats.net
Voltstats.net is a site built by a Volt fan, allowing
owners to be included on a fuel efficiency
leaderboard. This is enabled by tapping into the
OnStar®-powered Chevy Volt API. The real time
data shows miles driven, miles driven in electric-
only mode, miles per tank, an anonymous map of
owners and other data. The site is so popular that
approximately 27% of all Volt drivers have signed up
to share their vehicle data.
Although most OEMs are not making it easy for
developers to get vehicle data, it has not stopped
developers from building vehicle-related apps. A few
examples include:
● aze – a navigation app that leverages data from its
W
users to identify traffic and reroute you around it
● PS Tracks – allows you to track, record and map
G
your location
● ev – taps directly into your car’s engine control
R
unit through the onboard diagnostics port to
provide vehicle diagnostics, data visualizations
of performance and other tools for performance
enthusiasts
The bottom line is that your car is a sensor-laden
computer from which data streams. As it becomes
easier to access this data through application
programming interfaces (APIs) provided by
automotive OEMs, developers, marketers and data
scientists will harness it to add value to customers.
The opportunity is for an enhanced customer
experience, a deeper relationship and competitive
differentiation to first movers.
Notes from the Observation Deck: A Data Revoltion
18

19. 4
MOBILE
DATA AND
PRIVACY:
AT A
GLANCE

20. BIG DATA IN
THE MOBILE
WORLD
Data Street: How the
Communication Channel
Between Phone and User
Has Evolved
1
The Early Cell Phone
● communication device
a
between two parties
Your phone starts your car’s
engine for you.
Your phone reminds you to
wear a hat. “It’s cold!”
A detour to the hardware store
is automatically mapped.
Your “to-do” list includes
buying a hammer.
Your phone says, “You have
time to stop for coffee.”
“And the cheapest gas in the
city is on the way too.”
As we venture toward the
mobile 3.0 era, the userto-phone communication
channel is evolving.
Embedded sensors in
the user’s smartphone
are constantly collecting
data, creating streams of
information about users’
behaviors, habits and
routines.
You order a pepperoni pizza
with your phone.
Automatic check-in at all stops
including the pizzeria.
“Passed by 2 friends en-route,
maybe they’re hungry.”
2
Greater Options
● oice-recognition and
v
touch-screen interfaces
● hone offers location and
p
filtered preferences
● ata begins to flow back to
d
the user through software
3
A Multichannel Device
● constant data exchange
a
between user and device
● llows developers to tap into
a
the consumer’s lifestyle
● hone can “predict” what
p
user will do
● ill be able to offer content
w
and suggestions in real time
● ata will be pushed out as fast
d
as it comes in
Notes From The Observation Deck: A Data Revoltion
20

21. OPINION
Privacy:
Walking a Fine Line
KEVIN CONLON
Emerging Technologies
Campaign Manager
There is a growing awareness of the importance
of balancing personally identifiable information
(PII) and the desire to improve user experience and
relevancy through data usage. The collection of user
history is valuable because it allows marketers to
identify consumer patterns as opposed to recording
a single site visit, which doesn’t identify the entire
consumer story.
T
he collection of Big Data on individual users
within mobile advertising is limited today, and is
likely to continue to be the case until privacy issues
are solidified. Online privacy is a complex issue and
there is no one-size-fits-all answer, but consumers
are willing to share data when there is an equitable
value exchange (Robert Cell, CEO of MyBuys).
As the industry develops overall standards and
methods, many of the inconsistencies surrounding
data collection will begin to level off and be
standardized.
A
s advertisers develop more sophisticated online
consumer tracking tools, federal agencies and
consumer interest groups are pushing for stricter
privacy standards. Do Not Track, the latest Webtracking opt-out movement, is gaining ground with
browsers and companies alike being pressured into
providing simple opt-out measures.
Do Not Track continues to be an ongoing part of
the discussion around overall privacy and tracking
cookies, and mobile is no exception.
Mobile tracking is accomplished in three ways:
1.
Session Tracking: The time spent by a single user
on a website. The problem with session tracking
is the moment the session is over, the data used
to identify the customer is immediately wiped
out. Without tracking a consumer for a historical
period of time, advertisers are prohibited from
understanding true consumer behavior.
2.
Cookie Tracking: The collection of consumer
behavior throughout a whole mobile experience
is never erased. Many marketers prefer cookie
tracking through ad servers and conversion tracking
companies, but scale is still a factor. Publishers
are moving to this format in lieu of session-bysession tracking, because it allows consumers to be
measured from first impression through to the end
of their mobile session. However, cookie tracking
today is limited to certain mobile phone operating
systems and is not considered reliable in mobile and
tablet marketing, like it is on desktop.
3.
Registration Data: Many content companies – such
as Google, Amazon and Microsoft® – are now relying
on consumer registration data and common log-in
data across multiple devices, to provide relevant and
timely advertising messages and content.

22. Internet users who said they knew
how to limit information about
themselves that is collected by an
online company.
Internet users who said they would
not allow an online search engine
to keep track of their queries, even
if the data provided personalized
results in the future.
Internet users who said they have
noticed online advertising that was
directly targeted to them.
Internet users who said they have
an unfavorable view of targeted
advertising.
Internet users who said they allow
targeted advertising because they
see advertisments pertaining to their
interests.
Social networking site users who
have deleted comments made by
others on their profiles.
(Source: Pew, Search Engine Use, Feb. 2012)
Notes from the Observation Deck: A Data Revoltion
22

23. 5
Forward
Thinking:
Actionable
Insights
W R
A P
U P
Big Data is here to stay; it’s interesting, it’s complex, and
it can be controversial. Considering there is no longer a
one-size-fits-all approach, what opportunities do we need
to embrace?

24. Glossary
Algorithm: A progressive procedure
for calculations used for data
processing and automated reasoning.
Big Data: A collection of information
that’s too big to be handled by
common computers.
new paths. Finally, action is taken
based on the data triggering another
data pull.
Automatic Programming
Interface [API]: Interface used by
software components to communicate with each other. An API allows
the software element to talk to the
hardware device, and vice versa.
Feedback Loop: In the first stage,
a behavior is measured, captured
and stored. Then, the raw data is
relayed in an emotionally resonant
context. The third stage is that the
relayed information shines light on
Hadoop: Free, Java-based
programming framework that supports
the processing of large data sets in a
computing environment.
[The term] Big Data...will fade,
over time, into the pantheon of
other tech buzzwords. But the
data it represents won’t – it exists
now, and continues to grow. So
it’s OK to allow the term for now,
learn the concepts it presents, and
bake it into what you do today.
Big Data will only get bigger. And
that’s not just hype.”
Big Data, like the PC revolution
of the ’80s, the emergence of the
Internet in the ’90s, and Web 2.0
in the 2000s, represents a severalhundred-billion-dollar wealth
creation opportunity.”
Data is power. Once you have that,
you can understand where you have
problems, how to reroute and how
to redirect people to modes of
transport they weren’t even thinking
about when they started their day.”
– Dow Corporate Venture Capital
– Barb Samardzich, vicepresident of product
development at Ford of Europe
– Buck Woody, Simple Talk
Notes from the Observation Deck: A Data Revoltion
24

25. YOUTUBE LOGO SPECS
PRINT
on light backgrounds
on dark backgrounds
standard
no gradients
no gradients
watermark
stacked logo (for sharing only)
stacked logo (for sharing only)
gradient bottom
C0 M96 Y90 K2
standard
watermark
main red
C13 M96 Y81 K54
PMS 1795C
PMS 1815C
white
black
C0 M0 Y0 K0
C100 M100 Y100 K100
WHITE
BLACK
550 Town Center Dr,
Dearborn, MI
48126
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