In this paper the authors present an integrated framework that could help stimulate an organisation to become data-driven by enabling it to construct its own Data-Driven Business Model (DDBM) in coordination with the six fundamental questions for a data-driven business. There are a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. By utilising the blueprint an existing business is able to follow a step-by-step process to construct its own DDBM centred around the business’ own desired outcomes, organisation dynamics, resources, skills and the business sector within which it sits. Furthermore, an existing business can identify, within its own organisation, the most common inhibitors to constructing and implementing an effective DDBM and plan to mitigate these accordingly. Within the DDBM-Innovation Blueprint inhibitors are colour-coded and ranked from severe (red) to minor (green). This system of inhibitor ranking represents the frequency and severity of inhibitor, as perceived by 41 strategy and data-oriented elite interviewees.

1. Data-­‐Driven
Business
Models
(DDBMs):
A
Blueprint
for
Innova9on
Pa;erns
from
Established
Organisa9ons
1
Josh
Brownlow,
Mohamed
Zaki,
Andy
Neely
and
Florian
Urmetzer

2. Agenda
2
• Introduc@on
• Methodology
• Results
• Summary

3. Introduc9on
• Capitalizing on data explosion is increasingly becoming a necessity
in order for a business to remain competitive
• The challenges are threefold:
– how to extract data
– how to refine it
– how to ensure it
• Organizations that fail to align themselves with data-driven practices risk
losing a critical competitive advantage and market share

4. Research
Objec9ves
4
RQ:
How
Does
Big
Data
Affect
Data
Driven
Business
Models
in
Established
Business
Organiza?ons?
Objec?ves
:
• Further
demonstrate
the
validity
of
the
DDBM
framework
by
applying
it
to
established
organiza@ons.
• Where
applicable,
to
add
dimensions
to
the
DDBM
framework
that
are
present
in
established
organiza@ons
but
were
not
present
in
business
start-­‐ups.
• Provide
a
founda@on
and
structural
guidelines
within
which
an
exis@ng
or
new
business
can
analyze,
construct
and
apply
its
own
DDBM

5. Methodology:
5
Sampling
Data
collec@on
Data
analysis
Sectors
determined
through
literature
reference
frequency
Top
10
dis@nct
companies
for
each
sector
Random
sample
4
companies
selected
for
each
sector
20
Companies
Company
informa@on
• Company
websites
• Annual
Reports
• Press
releases
Public
sources
•
Case
studies
•
Business
schools
info
•
Newspaper
ar@cles
142
Sources
Thema@c
language
analysis
Over
7
sources
per
company
• Coding
of
sources
using
data
driven
business
model
framework
• Nodes
added
• Nvivo
analy@c
soUware
Valida@on
• Qualita9ve
research
u@lizing
a
ques@onnaire
• Primary
data
collected.
• Compared
with
findings
of
thema@c
language
analysis
• Forma@on
of
case
studies.
Valida@on
or
Invalida@on
of
Findings

6. Company
Classifica9on
Table
6
Finance
Insurance
Publishing
Retail
Telecoms
HSBC
Direct
Line
The
Times
Topshop
GiffGaff
Mobile
Merrill
Lynch
Allstate
New
York
Times
Primark
Vodafone
Wells
Fargo
Admiral
Group
The
Financial
Times
Asos
AT&T
Goldman
Sachs
ING
Direct
Hatche`e
Livre
Zara
Orange

7. Description of specific DDBM's for each established
organization

8. Results:

9. DDBM
Framework
in
start-­‐ups
9
9
Data%Driven+Business+
Model
Data+Source
External
Acquired+Data
Cust Provided
Free+Available+
Open+Data
Social+Media+
Data
Web+Crawled+
Data
Internal
Existing+Data
Self+Generated+
Data
Crowd+Sourced
Tracked,+
Generated
Key+Activity
Aggregation
Analytics
Descriptive
Predictive
Prescriptive
Data+
Acquisition
Data+
Generation
Crawling
Tracking+and+
Crowdsoucing
Distribution
Processing
Visualisation
Offering
Data
Information+
and+Knowledge
Non+Data+
Product+and+
Service
Revenue+
Model
Asset+ Sale
Lending+or+
renting
Licensing+
Usage+Fee
Subscription+
Fee
Advertising+
Specific Cost
Advantage
Target
Customer
B2B
B2C

10. DDBM
Framework
in
Established
Organisa9ons
10
Two
new
main
dimensions
were
added
to
the
DDBM
framework:
Compe@@ve
advantage
and
investment,
both
of
which
received
between
77
–
90%
approval
ra@ng
Investment
External
Internal
Cyclical1
Reinvestment
Competitive)Advantage
Shortened)
Supply)Chain
Expansion
Consolidation
Processing)
Speed
Differentiation
Brand Reputation

11. Compe99ve
Advantage
11
•
Brand
and
differen@a@on
had
the
highest
percentage
of
companies
who
considered
this
important.
•
Brand
considered
most
important
compe@@ve
advantage
throughout
all
the
sectors
analysed.
•
Differen@a@on
seen
as
important
in
retail,
publishing
and
insurance.
•
Processing
speed
considered
a
strong
advantage
by
the
finance
sector.
0
20
40
60
80
Finance
Insurance
Publishing
Retail
Telecommunica@ons
Compe99ve
Advantage
:
Percentage
Reference
Brand
Shortened
Supply
Chain
Processing
Speed
Expansion
Differen@a@on
Consolida@on
0
5
10
15
20
Consolida@on
Differen@a@on
Expansion
Processing
Speed
Shortened
Supply
Chain
Brand
Compe99ve
Advantage
:
%
of
Companies
Note:
Sum
>
100%
as
companies
might
use
mul@ple
sources

12. Data
Source
12
0
20
40
60
80
100
Acquired
data
Customer
Provided
Free
Available
Exis@ng
Data
Self
Generated
Data
Source:
%
of
Companies
•
Highly
varied
use
of
data
sources,
with
all
sectors
using
mul@ple
sources.
• Telecommunica@ons
and
retail
sector
emphasis
on
self
generated
data.
• Consistent
use
of
customer
provided
data
throughout
sectors.
0
10
20
30
40
50
60
Financial
Services
Insurance
Publishing
Retail
Telecommunica@ons
Data
Source:
Percentage
Reference
Self
Generated
Exis@ng
Data
Free
Available
Customer
Provided
Acquired
data

13. Key
Ac9vity
13
•
Publishing
sector
predominantly
u@lizing
descrip@ve
analy@cs.
• Finance
sector
focused
upon
predic@ve
analy@cs.
•
Retail
consistent
use
of
all
analy@c
types.
0
5
10
15
20
25
30
35
Finance
Insurance
Publishing
Retail
Telecommunica@ons
Key
Ac9vity
:
Reference
Percentage
Visualisa@on
Processing
Mutualism
Distribu@on
Data
Genera@on
Data
Acquisi@on
Prescrip@ve
Analy@cs
Predic@ve
Analy@cs
Descrip@ve
Analy@cs
Unspecified
Analy@cs
Aggrega@on
0
20
40
60
80
100
120
Aggrega@on
Unspecified
Descrip@ve
Predic@ve
Prescrip@ve
Data
Acquisi@on
Data
Genera@on
Distribu@on
Mutualism
Processing
Visualisa@on
Key
Ac9vity
:
%
of
Comps
•
100%
of
companies
analyzed
expressed
their
use
of
data
analy@cs.
• Data
acquisi@on
viewed
as
a
key
ac@vity
by
>80%
of
companies.

14. Revenue
Model
14
•
Adver@sing
(~75%)
u@lized
across
all
analyzed
business
sectors.
•
Telecoms,
retail,
publishing
and
insurance
sector’s
concerned
primarily
with
adver@sing.
•
Finance
sector
emphasis
on
lending,
ren@ng
or
leasing.
0
10
20
30
40
50
60
70
80
Adver@sing
Asset
Sale
Lending,
Ren@ng
or
Leasing
Licensing
MGM
Subscrip@on
Fee
Usage
Fee
Revenue
Model
:
%
of
Comps
0
20
40
60
80
100
Finance
Insurance
Publishing
Retail
Telecommunica@ons
Revenue
Model
:
Reference
Percentage
Usage
Fee
Subscrip@on
Fee
MGM
Licensing
Lending,
Ren@ng
or
Leasing
Asset
Sale
Adver@sing

15. Validation
A
Total
of
41
survey
responses

16. Industry
Specific
DDBM’s
16

17. Industry
Specific
DDBM’s
17
Dominant
Challenges
Achieving
a
DDBM
in
Start-­‐ups
and
Established
Organiza9ons
Start-­‐up
Organiza9ons
Hard
SoU
Established
Organiza9ons
Hard
SoU
Issues
acquiring
a
data
source
X
Data
quality
and
integrity
X
Data
analysis
skills
X
Cultural
challenges
X
Resource
Issues
X
Personnel
issues
X
Barriers
due
to
prac@cality
X
Value
percep@on
of
a
DDBM
X
Rela9onship
between
Personnel
Issues
and
other
Internal
and
External
Issues
•
Personnel
issues
may
be
the
root
cause
of
the
other
main
hindrances
to
achieving
a
DDBM.

18. Summary:

19. DDBM
Innova9on
Blueprint
19

20. The Six Fundamental Questions for DDBM
Construction
20