BE DATA INFormeD
without being a data scientist

Pamela
Pavliscak
(not meant to be spoken)
@paminthelab
Change Sciences

I <3 Data
but I’m still
learning

Today’s JOURNEY
data overload
the analyst
the threshold
poor signals
the abyss
return with elixir
data thinking
supertechnological aid
trophy metrics

3 million emails
2 million Google searches
684,000 Facebook shares
$272,000 spent online shopping
100,000 tweets
3,600 photos to Instagram
2 million blog posts

1 baby born
5 mobile phones activated
@paminthelab

Each person carries an average of
3 devices

375 megabytes consumed
Per household each day

For now, data is focused on
Selling and surveillance

DATA
Being
Nothingness

BIG DATA
is a little scary

Big data is like teenage sex - everybody
talks about it, nobody really knows how
to do it, everyone thinks everyone else
is doing it, so everyone claims they are
doing it.
-Dan Ariely
“
@paminthelab

INSIGHT VACUUM

Think of data science like archeology
traces left behind

They came
<something, something>
They left

It’s the every person story

There are really three ways to use DATA

PROVE
something you know

Proving is about the bottom line

Numbers
KPIs & scores
@paminthelab

Strategic rather than tactical

TOO HIGH LEVEL for experience design

IMPROVE
something you can change

A lot of consistency
and a little experimentation

DISCOVER
something you don’t know

Look for patterns

Discovering is about relationships

How to Think
like a data scientist

curiosity
THINK WITH DATA
1

CONSIDER
measurable moments

Expectations (before)
Moments (during)
Memories (after)

The Experience Continuum
the long
before
just a
memory
weeks
days
hours
minutes
now
now
now
minutes
hours
days
almost
now
now
the
here after

remember
it’s people first

Behaviors
Interactions
Feelings
Attitudes

Start with buckets,
and count the things

hamburger icon test case

Our gut says no

PROBABILiTy
BE A BIT BAYESIAN
2

P (B | A) P (A)
P (A | B) =
P (B)
TRANSLATION
Belief + new evidence
= updated belief

Evaluate
the evidence

Analytics
Piwik, Clicky
Google
Chartbeat, Crazy Egg,
Mixpanel, KISS metrics

Social Media Data
Shoutlet
Reachli
Trackur
Topsy
Social Mention
Open Status
Google Trends

Other data sources
1. A/B tests
2. Surveys
3. Heatmaps
4. Quantified qualitative studies
5. Biometrics & eyetracking
6. Sales data
7. CRM data
8. Call center logs
9. Billing systems
10. Business intelligence systems

It’s really all behaviors & words

USE
degrees over absolutes

Pay attention to variation
@paminthelab

Correlation ≠ causation

It’s never 100% CERTAIN

Source: Exis
Subtle changes
make a difference

TRansparency
DESCRIBE YOUR BIAS
3

There is always a filter
HEISENBERG UNCERTAINTY
PRINCIPLE (small data)
SIGNAL BIAS
(big data)

Even a big data source is not everyone

By studying something, we change it

Nice people skew studies
HUMAN CONTACT
NICE

There is a nice factor
TV Network Site ONLINE Study
0 20 40 60 80 100
TV Network Site LAB Study
0 20 40 60 80 100
Source: Change Sciences

gut feel
<bias>
one site

CoNTEXT
EMBRACE COMPLEXITY
4

COMbine forces
Quantitative & Qualitative

Balance subjective & objective

Not Curious
74% tapped right past
help of any kind

meaning
TELL A STORY
5

SKIP TO MEANING
5
4
3
2
1
0

Select
actionable detail

OVerview
zoom
filter

Audience
put a human face
on it

C-Suite
Team

CREATE
emotional impact

MENU IS
CLEARER
20% more people
clicked Menu than the
the icon alone
Source: Exis

I’ve been seeing that little doodad on
the site, but I just thought it was part of
the design.
-M, Millennial
“
Source: Change Sciences

Hamburger awareness
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Source: Change Sciences
is on the rise

Data
thinking
1. Get curious
2. Think Bayesian
3. Explain bias
4. Embrace context
5. Show & tell

UX + DATA SCIENCE

BE DATA INFormeD
without being a data scientist
Pamela Pavliscak
@paminthelab
ChangeSciences.com