The presentation discusses the relevance of scientific methods to digital data.

1. >
Data
Science
<
Where
does
a
Physics
degree
fit?

2. >
Life
as
a
Data
Scien/st
§ What
is
it?
§ Data
Science
Examples
§ Why
you
should
think
about
it?
§ About
Datalicious
(my
current
company)
August
2011
©
Datalicious
Pty
Ltd
2

3. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
What
is
it?
August
2011
©
Datalicious
Pty
Ltd
3

4. >
Mixing
the
old
to
produce
new
Business
Marke/ng
/
Performance
Adver/sing
IT
/
Coding
Sta/s/cs
Data
Science
August
2011
©
Datalicious
Pty
Ltd
4

5. >
By
Defini/on
Data:
“Facts
and
sta)s)cs
collected
together
for
reference
or
analysis”
Science:
“The
systema)c
study
of
the
structure
and
behaviour
of
the
physical
and
natural
world
through
observa)on
and
experiment”
Hmmm,
so
what?
August
2011
©
Datalicious
Pty
Ltd
5

6. >
Sounds
like
nothing
new
§ Data,
Science,
ObservaPon,
Hypothesis,
Experiment,
Analysis,
PredicPon
à
are
all
nothing
new.
§ BUT,
the
digital
age
has
created
new
opportuniPes
where
scienPfic
methods
can
be
applied
to
massive,
real
world
digital
data
sets.
August
2011
©
Datalicious
Pty
Ltd
6

7. >
And
Data
is
Exploding
“Every
2
Days
We
Create
As
Much
Informa)on
As
We
Did
Up
To
2003”
–
Eric
Schmidt.
CEO,
Google
“AdMob
Seeing
2
Billion
Ad
Requests
Per
Day;
Up
300
Percent
Over
Past
Year”
–
TechCrunch
“The
amount
of
digital
informa)on
created
annually
will
grow
by
a
factor
of
44
from
2009
to
2020,
as
all
major
forms
of
media
-­‐
voice,
TV,
radio,
print
-­‐
complete
the
journey
from
analog
to
digital”
August
2011
©
Datalicious
Pty
Ltd
7

8. >
EMC
-­‐
"The
Digital
Universe
Decade
-­‐
Are
You
Ready?"
“In
2009,
amid
the
"Great
Recession,"
the
amount
of
digital
informa)on
grew
62%
over
2008
to
800
billion
gigabytes
(0.8
Zeabytes).”
But
how
much
is
that
really?
707
trillion
copies
of
the
more
than
2,000-­‐page
U.S.
PaPent
ProtecPon
and
Affordable
Care
Act
signed
into
Law
in
March
2010.
Stacked
end
to
end,
the
documents
would
stretch
from
Earth
to
Pluto
and
back
16
Pmes
or
cover
every
inch
of
the
United
States
in
paper
3
feet
deep
August
2011
©
Datalicious
Pty
Ltd
8

9. [
Mo/va/on
]
Data
=
©
Datalicious
Pty
Ltd
9

10. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Data
Science
Examples
August
2011
©
Datalicious
Pty
Ltd
10

11. >
Search
and
the
product
lifecycle
Nokia
N-­‐Series
Apple
iPhone
October
2010
©
Datalicious
Pty
Ltd
11

12. >
Financial
Predic/on
©
Datalicious
Pty
Ltd
12

13. >
Trigger
based
Sales
2
years
on
the
beach
Iden/fies
themself
User
visits
User
visits
(e.g.
sale
or
Looks
at
lots
of
Site
Site
again
registra/on)
‘widgets’
anonymously
‘anonymously’
Cookie
Web
Cookie
Analy/cs
Database
Hi
John,
long
/me
no
talk,
we
have
a
special
Name:
John
Example
on
widgets!
Interest:
Widgets
History:
Business
-­‐Last
visit
2
years
ago.
Intelligence
-­‐Purchased
10
blue
widgets
Database
-­‐High
value
band
Loca/on:
2000,
Sydney
©
Datalicious
Pty
Ltd
13

14. >
The
science
of
Banner
Ads
Control
Group
Normal
Display
Adver/sing
(90%)
(10%)
Impressions
=
1
M
Impressions
=
9
M
Visitors
=
1,000
Visitors
=
10,000
Sales
=
50
Sales
=
550
Cost
=
$1,000
Cost
=
$9,000
$
per
sale
=
$20
$
per
sale
=
$16
Without
display
With
display
adverPsing,
1
in
every
20,000
people
22%
adverPsing
1
in
every
16,363
people
will
convert
uplie
will
convert
-­‐
Banner
Ads
do
influence
sales
despite
what
people
think
-­‐
Even
if
you
don’t
click
on
them
©
Datalicious
Pty
Ltd
14

15. [
Affinity
targe/ng
in
ac/on
]
Different
types
of
visitors
respond
to
different
ads.
By
using
category
affinity
targePng,
response
rates
are
lieed
significantly
across
products.
Click-­‐Through
Rate
By
Category
Affinity
Message
Postpay
Prepay
Broadb.
Business
Blackberry
Bold
- - - +
5GB
Mobile
Broadband
- - + -
Blackberry
Storm
+ - + +
12
Month
Caps
- + - +
©
Datalicious
Pty
Ltd
15

16. 14/11/12
©
Datalicious
Pty
Ltd
16

17. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Why
should
you
do
it?
August
2011
©
Datalicious
Pty
Ltd
17

18. >
The
start
of
a
trend…
August
2011
©
Datalicious
Pty
Ltd
18

19. >
Big
Data
also…
August
2011
©
Datalicious
Pty
Ltd
19

20. >
Supply
is
way
behind
demand…
August
2011
©
Datalicious
Pty
Ltd
20

21. IT
needs
more
Scien/sts
August
2011
©
Datalicious
Pty
Ltd
21

22. >
Difficult
to
believe?
§ Marketers
typically
just
don’t
get
it,
but
their
bosses
now
know
you
can
measure
the
ROI
of
digital
Ads,
so
they’re
screwed
without
the
data
scienPsts
§ The
business
guys
mostly
(not
all)
see
the
value,
but
the
Internet
wasn’t
around
when
they
were
back
in
Harvard,
so
they
can’t
do
it
alone
§ Developers
are
predominantly
good
at
following
a
spec.
They
rarely
understand
data,
staPsPcs
and
how
to
go
about
sejng
up
and
analysing
an
experiment
August
2011
©
Datalicious
Pty
Ltd
22

23. >
You
can
work
across
all
industries
August
2011
©
Datalicious
Pty
Ltd
23

24. >
Work
on
wide
ranging
issues…
August
2011
©
Datalicious
Pty
Ltd
24

25. >
Work
from
Anywhere
August
2011
©
Datalicious
Pty
Ltd
25

26. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
About
Datalicious
August
2011
©
Datalicious
Pty
Ltd
26

27. >
Across
major
data
categories
Campaign
data
TV,
print,
call
center,
search,
web
analyPcs,
ad
serving,
etc
Campaigns
Customers
Customer
data
Direct
mail,
call
center,
web
analyPcs,
emails,
surveys,
etc
Consumer
data
Search,
social
media,
trends,
Compe/tors
Consumers
research,
news,
etc
Compe/tor
data
Search,
social
media,
ad
spend,
news,
offers,
etc
August
2011
©
Datalicious
Pty
Ltd
27

28. >
Defining
data
strategies
Con/nuous
tes/ng
and
op/miza/on
AcquisiPon
Up-­‐Sell
RetenPon
Advocacy
Analy/cs
and
metrics
frameworks
August
2011
©
Datalicious
Pty
Ltd
28

29. >
Guiding
the
customer
journey
To
transac/onal
data
To
reten/on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
August
2011
©
Datalicious
Pty
Ltd
29

30. >
Summary
§ Do
you:
1. Know
how
to
idenPfy
trends
in
numbers
and
to
graph
data
2. Know
how
to
write
reports
and
validate
experimental
predicPons
3. Understand
some
business
thinking,
i.e.
cost
of
sales,
maximising
return,
etc
4. Understand
the
principles
of
wriPng
code
§ If
yes,
then
Data
Science
may
be
for
you.
August
2011
©
Datalicious
Pty
Ltd
30

31. Contact
us
hogilvy@datalicious.com
Learn
blog.datalicious.com
Follow
twiner.com/hamishogilvy
twiner.com/datalicious
August
2011
©
Datalicious
Pty
Ltd
31