Data-­‐Driven  Business  Models  (DDBMs):    A  Blueprint  for  Innova9on  

 Pa;erns  from  Established  Organisa9ons  

 

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Josh  Brownlow,  Mohamed  Zaki,  Andy  Neely  and  Florian  Urmetzer  

Agenda  

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 •  Introduc@on  •  Methodology  

•  Results  •  Summary    

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

Research  Objec9ves    

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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    

   

Methodology:  

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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  

Company  Classifica9on  Table  

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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  

Description of specific DDBM's for each established organization

Results:    

DDBM  Framework  in  start-­‐ups    

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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

PrescriptiveData+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 CostAdvantage

TargetCustomer

B2B

B2C

DDBM  Framework  in  Established  Organisa9ons  

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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 Cyclical1Reinvestment

Competitive)Advantage

Shortened)Supply)Chain

Expansion

Consolidation

Processing)Speed

Differentiation

Brand Reputation

Compe99ve  Advantage  

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•   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  

Data  Source  

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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  

Key  Ac9vity  

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•   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.    

Revenue  Model  

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•   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  

Validation A  Total  of  41  survey  responses  

Industry  Specific  DDBM’s  

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Industry  Specific  DDBM’s  

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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.      

Summary:    

DDBM    Innova9on  Blueprint    

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The Six Fundamental Questions for DDBM Construction

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