Ray Poynter delivers the 4th lesson in the #NewMR webinar series on Finding and Communicating the Story in the Data. This lesson looks at Complex and Large Data Streams.

1. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Finding
and
Communica-ng
the
Story
Lesson
4
of
6
Working
with
Complex
Data
Streams
Ray
Poynter
July
2016

2. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Series
Schedule
• An
Introduc5on
and
Overview
-­‐
Feb
23
• Working
with
Qualita5ve
Informa5on
–
Apr
5
• Working
with
Quan5ta5ve
Informa5on
-­‐
May
26
• Working
with
mul-ple
streams
&
big
data
-­‐
July
5
• U5lizing
visualiza5on
–
Sep
13
• Presen5ng
the
story
-­‐
Nov
8

3. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Agenda
• Brief
recap
• Complex
data
and
its
implica5ons
• Example
from
measuring
social
media
• Working
with
big
and
complex
data
• Strategies
for
finding
the
story
in
the
data

4. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
The
Frameworks
Approach
1. Define
and
frame
the
problem
– A
problem
fully
defined
is
a
problem
half
solved
2. Establish
what
is
already
known
– Find
out
what
is
believed
and
what
the
expecta5ons
are
3. Organise
the
data
to
be
analysed
– Systema5c
checking
and
structural
procedures
4. Apply
systema5c
analysis
processes
5. Extract
and
create
the
story

5. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Tradi-onal
MR
Data
ID
Q1
Q2
Q3
Q4
R1
1
2.5
01101
Fast
R2
1
3.5
11000
Green
R3
2
2.4
01110
Thursday
nights
R4
2
1.8
11011
Some5mes
R5
1
4.1
00001
In
the
net
Qualita-ve
Bricolage

6. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016

7. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Assembling
the
Evidence
• Granularity?
• Addi5ve,
complementary,
duplica5on?
• What
is
being
missed?
• Lags
in
availability?
• Normalising?
• Comparators?
• Create
a
model
of
the
interac5ons

8. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Examples
of
Data
Streams
• Tracking
data
from
tradi5onal
surveys
• Passive
behavioural
tracking
• Google
Consumer
Surveys
• Social
Media
analy5cs
• Google
analy5cs
• Web
analy5cs
• Biometrics
• News
• Professional
reviews
• Mystery
shopping
• Leers,
calls,
emails
from
customers
• Transac5onal
data
• 3rd
party
sources
•
Enterprise
feedback
systems

9. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Characteris-cs
of
Data
Streams
• Timelines
–
e.g.
monthly,
weekly,
daily,
con5nuous
• Coverage
–
who
is
represented,
who
is
missed?
• Richness
–
single
number,
range
of
measures,
quotes?
• Veracity
–
e.g.
honesty,
accuracy,
persistence
• Depth
–
one
measure
per
person
or
many
measures?

10. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Nate
Silver
&
FiveThirtyEight

11. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Nate
Silver
and
Elec-on
Predic-ons
• Polling
data
– Inclusive
approach
• Weigh5ng
– Recency
– Sample
size
– Pollster
ra5ng
– House
effects
– Likely
voter
adjustment
• Trend
line
adjustment
• Congressional
approval
• Fundraising
totals
• Highest
elected
office
held
• Margin
of
win
in
most
recent
race
• Ideology
and
State
leaning

12. Screenshot,
25
Feb,
2016

13. NBA
Basketball
Screenshot
25
Feb,
2016

14. Oscars
–
Best
Actor
Screenshot,
25
Feb,
2016

15. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Evalua-ng
SM
Campaigns

16. The
POEM
Framework
Owned
Media
From
#IPASocialWorks

17. The
Interac-ons
in
POEM
From
#IPASocialWorks

18. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Key
Challenges
• The
counter-­‐factual
–
what
would
have
happened
anyway
• Influence,
how
to
measure
it,
does
it
exist?
• Homophily
–
birds
of
a
feather
flock
together
• Short
and
Long-­‐term
effects
• Causa5on
and
Correla5on

19. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Influence
and
Homophily
Type
of
Market
Influence
Target
influencers
Homphily
Target
people
like
buyers

20. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Short
and
Long-­‐term
Effects
• Social
is
very
good
at
measuring
short-­‐term
effects
• The
micro-­‐objec5ves
are
oeen
ac5va5on
events:
– Downloads,
registra5ons,
plays,
trial,
purchase
etc.
• But,
long-­‐term
effects
are
oeen
more
important
to
brand
value
and
price
elas5city
• Without
short-­‐term
effects
there
is
usually
no
long-­‐
term
– But
long-­‐term
effects
are
not
just
the
sum
of
the
short-­‐
term
effects

21. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Evalua-on
Methods
&
Approaches
From
#IPASocialWorks

22. From
#IPASocialWorks

23. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
What
is
the
impact
of
social?
Region
A
– T1
sales
=
100
– T2,
TV,
sales
=
110
– T3,
TV
&
Twier,
sales
=
130
Region
B
– T1,
sales
100
– T2,
Twier,
sales
=
110
– T3,
TV
&
Twiers,
sales
=
130

24. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Lessons
from
Measuring
Social
1. Plan
in
advance,
define
objec5ves,
bake
measurement
into
the
campaign
2. Focus
on
a
core
set
of
relevant
metrics
3. Try
to
include
experiments
/
experimental
design
4. Have
access
to
advanced
analy5cs
–
but
be
pragma5c

25. IBM’s
four
Vs

26. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
What
is
?
Instruc5on
Results

27. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Target

28. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Big
Data
Success
• Nejlix,
what
sort
of
new
produc5ons
should
work
–
House
of
Cards
• UPS
–
how
can
we
op5mize
routes
• eBay
–
how
to
iden5fy
fraudulent
behaviour
• WeatherSignal
–
use
data
from
smartphones
to
create
localised
weather
maps
• Stockholmståg
Trains
–
what
events
predict
delays
in
the
next
2
hours
Check
out
Annie
Pelt’s
NewMR
webinar

29. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Working
with
Big
Data
Most
successes
come
from
having
a
precise
and
narrow
ques5on:
• What
paerns
indicate
fraudulent
ac5vity?
• What
events
predict
churn?
• Which
customers
are
pregnant?
• How
many
types
of
customers
do
we
have?
– What
best
predicts
membership
of
a
segment?

30. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Correla-on
and
Causa-on
1. Correla5on
predicts
the
past
– Which
is
some5mes
enough
– Especially
when
the
past
repeats
itself
2. Causa5on
is
needed
to
predict
new
futures
– But
causa5on
is
hard
to
establish
in
the
real
world
3. Experiments
are
key
to
establishing
causa5on
– Market
research
can
help

31. Correla-on
Annual
Chocolate
Consump-on
&
Nobel
Prizes
per
10
Million
of
Popula-on
New
England
Journal
of
Medicine.

32. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Iden-fy
the
Counterfactual
• What
would
have
happened
without
the
campaign/ac5vity?
• Projec5ons/forecasts
• Year-­‐on-­‐year
figures
• A/B
tests

33. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Make
Predic-ons
Post
hoc
reasoning
when
supported
by
masses
of
data
can
support
the
crea5on
of
almost
any
point
of
view
Genera5ng
predic5ons
before
the
campaign
– As
well
as
targets
– Provides
a
framework
for
finding
out
why
the
predic5ons
were
wrong
(and
they
usually
are).

34. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Using
Triangula-on
Triangula5on
means
using
mul5ple
sources
to
see
if
they
point
the
same
way
– Helps
validate
findings
– Helps
avoid
embarrassing
mistakes
Predic5on
can
be
used
with
triangula5on
to
avoid
simply
describing
paerns
– For
example,
“If
this
finding
about
a
decline
in
sa3sfac3on
is
true
we
expect
churn
to
increase
over
the
next
three
months.”

35. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Use
Benchmarks
Few
metrics
have
absolute
meaning
– And
the
relevance
of
1
million
views
or
shares
changes
over
5me
So,
benchmarks
are
essen5al
– Within
brand
benchmark
– Within
plajorm
benchmark
– Within
ver5cal
benchmark
– Within
target
group
benchmark
Benchmarks
highlight
the
need
to
make
comparisons.

36. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Organising
Complex
Data
• Define
the
problem
– What
success
looks
like,
a
5ghtly
defined
ques5on,
ac5ons
you
wish
to
take
• Assess
the
characteris5cs
of
the
data
streams
– Veracity,
Granularity,
What’s
missing,
Overlaps
etc
• Filter,
clean
and
transform
the
data
• Find
the
answer
– Find
the
main
story
first
and
then
the
relevant
excep5ons
and
details
– Simplify
models
as
much
as
possible,
but
no
further
(borrowing
from
Einstein)
– Use
comparators
to
help
communicate
the
answers
– Create
a
compelling
story
–
without
focusing
on
the
process
or
numbers

37. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016

38. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016

39. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016

40. Normalizing
by
Growth
Pa`erns
Forbes:
hp://bit.ly/NewMR_208

41. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Normalizing
by
‘Share
of’
• Google
Trends
–
internet
use
is
growing,
Google
use
is
growing,
measures
must
be
normalized
to
be
compared.
• Process
– Collect
the
search
terms
and
count
men5ons
per
day
for
each
term
– Express
them
as
percentages
of
all
searches
on
the
same
day
– Find
the
biggest
number
for
the
search
terms
and
set
this
to
100
(or
100%)
– Scale
all
of
the
other
items
by
the
same
factor
• Note
the
only
meaning
the
numbers
have
is
in
the
context
of
the
set
of
items
being
measured
and
the
5me
frame
chosen.

42. Google
Trends
Normalising
by
Share
of
…
Zika,
Worldwide,
last
90
days

43. Comparators

44. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Normalizing
by
Coding
• Sen5ment
analysis,
open-­‐ended
comments
converted
to
Posi5ve,
Nega5ve
and
Neutral
• Digi5zing
from
analogue
to
binary
• Alloca5ng
to
segments
• Scoring
different
elements
– (think
America
Football,
different
points
for
different
events,
leading
to
points
in
a
league)

45. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Ben
Wellington,
TEDx,
How
we
found
the
worst
place
to
park
in
New
York
City
—
using
big
data

46. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016

47. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Use
the
Business
Ques-on
as
a
Lens
The
same
data
will
deliver
different
stories,
based
on
different
business
ques5ons
This
is
one
of
the
reasons
that
industry
reports
have
a
less
focused
story
– They
have
many
readers,
with
different
needs
and
ques5ons
The
business
ques5on
defines
what
is
in,
what
is
out,
and
where
the
magnifica5on
should
be

48. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Find
the
Relevant
Detail
Once
you
have
the
total
story:
– Are
there
people
who
have
a
different
story
(different
from
the
main
story)?
• Who
are
these
people?
• What
is
their
story?
• Where
are
the
differences?
• Why
are
they
different?
• When
do
these
differences
maer,
come
into
play?

49. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Different
Perspec-ves
ASK:
The
alterna3ve
explana3ons
for
this
data
are?

50. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Findings
Need
a
Comparator
RFID

51. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Bad
news
for
men
in
Eastern
Europe
Eurostat
-­‐
hp://goo.gl/r2q526
Amenable
Deaths
Per
100000
of
popula5on
-­‐
2012

52. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
The
Big
Picture
• Start
with
a
well
defined
ques5on
• Assess
the
data
streams
– Who
/
what
is
covered,
lags,
duplica5on,
veracity
etc
• Bake
measurement
in
from
the
start
–
when
possible
– Make
specific
predic5ons
• Transform,
filter,
clean
the
data
• Find
the
main
story
– Considering
correla5on,
causa5on,
comparators
and
alterna5ve
models
(e.g.
influence
and
homophily)
• Find
the
relevant
excep5ons
to
the
main
story
– Who,
what,
why,
when
&
where

53. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Thank
You!
Follow
me
on
Twi`er
@RayPoynter
Or
sign-­‐up
to
receive
our
weekly
mailing
at
h`p://NewMR.org

54. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Schedule
• An
Introduc5on
and
Overview
-­‐
Feb
23
• Working
with
Qualita5ve
Informa5on
–
Apr
5
• Working
with
Quan5ta5ve
Informa5on
-­‐
May
26
• Working
with
mul5ple
streams
&
big
data
-­‐
July
5
• U-lizing
visualiza-on
–
Sep
13
• Presen5ng
the
story
-­‐
Nov
8

55. Finding
and
Communica-ng
the
Story
–
Lesson
4
of
6
–
Complex
Data
Ray
Poynter,
2016
Q
&
A
Ray
Poynter
The
Future
Place