Taking Big Data Visually

takingbigdata
visually
March 4, 2014

2014 Analytics with Purpose
Behavioral Insight to Advantage
San Diego, California

Charles Sayers
Experience Innovation
SapientNitro
csayers@sapient.com

 challenges in data volume
and velocity

what we’ll look at

 how we’re wired for sight
 creating visual stories

© 2014 SapientNitro

THE REALITY

we’re generating
a LOT of data


%
of the data in the world today was created in
the last 2 years alone.
- IBM -


This is the volume of photos
uploaded to Flikr in a single day


THE PROBLEM

we’re generating
too much, too fast


we’re accumulating data faster
than our ability to understand it

ibm

a new
wave of
data is on
the horizon
innovations in wearables
and biometrics will drive a
surge in physiological data

190,000
people with deep analytic sills as well as

1.5 million
managers and analysts will be needed by
2018 to fill jobs in Big Data


THE OTHER PROBLEM

we don’t think in
numbers


an egg
This is your brain,

a pan
this is your brain,

a shift
this is your brain on drugs,

Any questions?

this famous public service campaign signaled a shift in data story telling
from numbers and statistics to emotional metaphor


our brains process visual information

,

times faster than text


%

%

of our brains are involved in

of all of our

visual processing

sensory receptors
are in our eyes


THE SOLUTION

visualization


FOR PEOPLE

visualization

helps make data clearer and more engaging


FOR BUSINESS

visualization

helps make data actionable and easier to manage


visualization
is about asking

an intriguing question

visualization
is answered with

contextual data

visualization
is expressed in

an engaging way

visualizations
are created from one of two perspectives
rational




literal
quantifiable
pragmatic

creative




metaphorical
relatable
emotional


visual storyscapes
are created when you combine the two
insightful
revealing
engaging


“Stories are just
data with a soul”
Dr. BreneBrown
Research Storyteller
University of Houston


what’s an intriguing
question?


BAD QUESTION

do these
jeans make
me look fat?

TRICK QUESTION

do you know
why I stopped
you?

INTRIGUING QUESTION

how is the
moon like an
Oreo cookie?

where do you find
contextual data?


what you’re
doing

who you are

what you’ve
done

who you
do it with

retail

what ‘s your
lifestyle?

why are
you here?

how much do
you need?

how much
you deposit

how much
you withdraw

banking

what
changed?

what was?

why did it
change?

what did the
change
change?

change

how can data
stories engage?


four types of

visual
engagement

1. collective
2. descriptive
3. adaptive
4. predictive


collective

visualizations summarize data

modern history

payment patents

how search works

deltasight.com

interactive timeline
the rei 1440 project


descriptive

visualizations share data-driven snapshots

consumer intelligence
the consumer barometer

relative time

retail dwell time

here is today

how retailers track you when you shop


adaptive

visualizations adapt to real time data feeds

social mapping

fitness monitoring

spatial awareness

google maps

tictrac

british airways


predictive

visualizations offer the ability to
future-cast and explore conditional scenarios

career advisor

life coach

the existential calculator

Reporter app for iPhone

financial manager
MoneyWiz personal finance


story complexity

complexity of
the data will
point to the
best option for
visualization

type of visualization

following a single
strand of hair
A VISUAL STORYSCAPE


In a world full of mobile savvy shoppers and
aisles bursting with hair care products,

how can one box of
hair color rule them all?

data considerations
84% of guests use their smartphone while in a
store…but what are they actually doing?
Is ‘showrooming’ something to worry about?

 Smartphone use and
behavior tracking
 Location-based, interactive
phone questionnaire

data dive
deploy device activity loggers onto the smartphones of
a community of Shoppers to obtain detailed data on
use both in-store and out of store

learning
environment
 passive infrared sensors and
sound collectors
 heat sensors collect data on
paths, dwell times, entry and
exit points, fixture interactions
 sound data is collected as a
means to understand human
interactions

smartphone





Unobtrusive widget runs in OS background
Logs what people do on their phones 24/7
Data pushed to data warehouse for analysis

questionnaire



Helps us understand context and drivers of a
trip; ultimately match trip types of to phone
behavior and use

snackable moments


THE CAVEAT

believing what we
see isn’t always a
good thing


this visualization overemphasizes the impact of non-communicable diseases

This graphic lumps together pneumonia deaths at
age 1 with car accidents at age 20, and cancer deaths
at 50 with heart disease deaths at 80.

http://www.informationisbeautiful.net/visualizations/20th-century-death/


visualization is a collaborative sport
strategist

designer

developer

what are the primary
and supporting data
points to our story?

how do we
convey that
story visually?

how do we enable
our story?


 ask an intriguing question
 gather the right contextual
data

visualize
this!

 make sure your data is clean,
accurate and complete
 choose the best type of visual
engagement
 visualize truthfully
 collaborate from start to finish

a parting visualization

the life you have in jelly beans

thank you
takingbigdata
visually
March 4, 2014

Charles Sayers
Experience Innovation
SapientNitro
csayers@sapient.com

Taking Big Data Visually

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