Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Capturing Value from Big Data through Data Driven Business models prensetation

1 375 vues

Publié le

This presentation demonstrates a study which provides a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. The Data Driven Business Model (DDBM) framework represents a basis for the analysis and clustering of business models. For practitioners the dimensions and various features may provide guidance on possibilities to form a business model for their specific venture. The framework allows identification and assessment of available potential data sources that can be used in a new DDBM. It also provides comprehensive sets of potential key activities as well as revenue models.The identified business model types can serve as both inspiration and blueprint for companies considering creating new data-driven business models. Although the focus of this paper was on business models in the start-up world, the key findings presumably also apply to established organisations to a large extent. The DDBM can potentially be used and tested by established organisations across different sectors in future research.

Publié dans : Données & analyses
  • Identifiez-vous pour voir les commentaires

Capturing Value from Big Data through Data Driven Business models prensetation

  1. 1. Capturing Value from Big Data through Data-Driven Business Models Patterns from the Start-up world Philipp Hartman, Dr Mohamed Zaki and Prof Andy Neely Cambridge Service Alliance University of Cambridge
  2. 2. “Data is the new oil”1 1  various  authors,  e.g.  Clive  Humby       0   5000   10000   15000   20000   25000   30000   35000   40000   45000   2005   2010   2015   2020   Data  volume  per  year  (Exabytes)2   2  IDC's  Digital  Universe  Study,  December  2012   56%     Top  Priority:   “How  to  get  value   from  big  data”  3   3   Gartner  “Big  Data  Study”  2013  
  3. 3. How to get value from Big Data? 3   OpKmizaKon  of   exisKng  service   Data  Driven   Business  Models1  
  4. 4. Based on this motivation the research question was developed 4   What  types  of  business  models  that  rely  on  data  as  a  key  resource  (i.e.   data-­‐driven  business  models)  can  be  found  in  start  up  companies?   How  to  analyse  data-­‐ driven  business   models?   Sub   quesKons   Data-­‐driven  business   model  framework   How  to  idenKfy   paVerns?   Research   QuesKon   Clustering  
  5. 5. The research was done in five steps 5   Case  studies   Finding   PaVerns   Data  collecKon   &  coding   Build  the   framework   Literature  Review   How  to  analyse  data-­‐ driven  business   models?   How  to  idenKfy   paVerns?  
  6. 6. The first step was a literature review with three different topics 6   Literature  Review   Big  Data   DefiniKon   Value  CreaKon   Business  Model   DefiniKon   Business  Model   Frameworks   Related  Work   Data  driven  business   Models   Cloud  business   models   Case  studies   Finding   PaVerns   Data  collecKon   &  coding   Build  the   framework   Literature  Review  
  7. 7. Business model key components were synthesized from existing frameworks ExisKng  Business  Model  Frameworks   -­‐  Chesbrough  &  Rosenbloom  2002   -­‐  Hedman  &  Kaling  2003   -­‐  Osterwalder  2004   -­‐  Morris  2005   -­‐  Johnson,  Christensen  et.  al.  2008   -­‐  Al-­‐Debei  2010   -­‐  Burkhart  2012   Value  CapturingValue  Crea@on Key  Resources Key  AcKviKes Cost  structure Revenue  Model Customer  Segment Value  ProposiKon Business  Model  DefiniKon   Business  Model  Key  Components   -­‐  No  universally  accepted  definiKon   of  the  concept   (Weill,  Malone  et  al.  2011)   -­‐  Most  definiKons  refer  to     value  crea@on  &  value  capturing      
  8. 8. The literature review identified several gaps 8   •  LiVle  academic  research  on  big  data  and  value  creaKon  –  mostly   whitepapers   •  Gap  in  literature:  data-­‐driven  business  models   •  OVo,  Aier  (2013)  interesKng  paper  but  limited  to  specific   industry  >  no  generalizaKon  possible   •  Similar  research  for  cloud  business  models  (cf.  Labes,  Erek  et.  Al.   2013)   Case  studies   Finding   PaVerns   Data  collecKon   &  coding   Build  the   framework   Literature  Review  
  9. 9. The framework was build from literature starting from the key components Data-­‐Driven-­‐ Business   Model   Data  Sources   Internal   exisKng  data   Self-­‐ generated   Data   External   Acquired   Data   Customer   provided     Free   available   Open  Data   Social  Media   data   Web  Crawled   Data   Key  AcKvity   Data   GeneraKon   Crowdsourci ng   Tracking  &   Other  Data   AcquisiKon   Processing   AggregaKon   AnalyKcs   descripKve   predicKve   prescripKve  VisualizaKon   DistribuKon   Offering   Data   InformaKon/ Knowledge   Non-­‐Data   Product/ Service   Target   Customer   B2B   B2C   Revenue   Model   Asset  Sale   Lending/ RenKng/ Leasing   Licensing   Usage  fee   SubscripKon   fee   AdverKsing   Specific  cost   advantage   Data-­‐Driven   Business  Model   Data  Sources   Key  AcKvity   Offering   Target  Customer   Revenue  Model   Specific  cost   advantage   Data  collecKon   &  coding   Case  studies   Finding   PaVerns   Literature  Review   Build  the   framework   Features  for   each  dimension   Data-­‐Driven  Business  Model   Framework   Business  Model  Key   Components  (Dimensions)   Data  Sources   Features  for   data  sources  
  10. 10. Synthesizing the different sources leads to the taxonomy 10   Data  Sources   Internal   exisKng  data   Self-­‐generated   Data   External   Acquired  Data   Customer   provided     Free  available   Open  Data   Social  Media  data   Web  Crawled   Data  
  11. 11. Dimension: Activities 11   Key  AcKvity   Data  GeneraKon   Crowdsourcing   Tracking  &  Other   Data  AcquisiKon   Processing   AggregaKon   AnalyKcs   descripKve   predicKve   prescripKve  VisualizaKon   DistribuKon  
  12. 12. Dimension: Offering 12   Offering   Data   InformaKon/ Knowledge   Non-­‐Data   Product/Service  
  13. 13. Dimension: Revenue Model 13   Revenue  Model   Asset  Sale   Lending/RenKng/ Leasing   Licensing   Usage  fee   SubscripKon  fee   AdverKsing  
  14. 14. Dimension: Target Customer 14   Target  Customer   B2B   B2C  
  15. 15. Data  collecKon   &  coding   The final framework 15   Case  studies   Finding   PaVerns   Literature  Review   Build  the   framework   Data-­‐Driven-­‐ Business  Model   Data  Sources   Internal   exisKng  data   Self-­‐generated   Data   External   Acquired  Data   Customer   provided     Free  available   Open  Data   Social  Media   data   Web  Crawled   Data   Key  AcKvity   Data  GeneraKon   Crowdsourcing   Tracking  &  Other   Data  AcquisiKon   Processing   AggregaKon   AnalyKcs   descripKve   predicKve   prescripKve  VisualizaKon   DistribuKon   Offering   Data   InformaKon/ Knowledge   Non-­‐Data   Product/Service   Target  Customer   B2B   B2C   Revenue  Model   Asset  Sale   Lending/RenKng/ Leasing   Licensing   Usage  fee   SubscripKon  fee   AdverKsing   Specific  cost   advantage  
  16. 16. Data collection and coding 16   Case  studies   Finding   PaVerns   Build  the   framework   Literature  Review   Data  collecKon   &  coding   Data  collecKon   Data  analysis  Sampling  
  17. 17. The data was generated using public available sources 17   Tag:  “big  data”   “big  data  analyKcs”   1329  companies   Data  collecKon   Company  informaKon   •  Company  websites   •  Press  releases   Public  sources   •  Coding  of  sources   using  data  driven   business  model   framework   •  Nvivo   Data  analysis   299  Sources   ~3  sources/comp   Sampling   100  Companies   cleaning   Random  sample   100  binary  feature   vectors  
  18. 18. Overall Analysis: Data Source 18   0%   10%   20%   30%   40%   50%   60%    Acquired  Data    Customer&Partner-­‐provided  Data    Free  available   Crowd  Sourced   Tracked  &  Other   Note:  Sum  >  100%  as  companies  might  use  mulKple  data  sources   •  >50%  of  companies   rely  on  free  available   data   •  >50%  of  companies   use  data  provided  by   customers/partners  
  19. 19. Overall Analysis: Key Activities 19   0%   10%   20%   30%   40%   50%   60%   70%   80%    AggregaKon    AnalyKcs    DescripKve  AnalyKcs    PredicKve  AnalyKcs    PrescripKve  AnalyKcs    Data  acquisKon    Data  generaKon    Data  processing    DistribuKon    VisualizaKon   •  >70%  of   companies  use   analyKcs     -­‐  mostly   descripKve     Note:  Sum  >  100%  as  some  companies  rely  on  mulKple  revenue  models  
  20. 20. Overall Analysis: Revenue Model 20   0%   5%   10%   15%   20%   25%   30%   35%   40%   45%   50%    AdverKsing    Asset  Sales    Brokerage  Fees    Lending  RenKng  Leasing    Licensing    SubscripKon  fee    Usage  Fee    No  informaKon   •  Majority  of   revenue  models   based  on   subscripKon  and/ or  usage  fee   •  No  informaKon   about  the   revenue  model   as  many   companies  are  in   an  early  stage   Note:  Sum  >  100%  as  some  companies  rely  on  mulKple  revenue  models  
  21. 21. Overall Analysis: Target Customer 21   70%   17%   13%   B2B   B2C   both   •  There  seems  to  be  a   noteworthy   predominance  of   B2B  business   models   •  But  no  reference   data  found  
  22. 22. BM patterns were identified using a clustering approach 22   Ketchen,  David  J.;  Shook,  Christopher  L.  (1996):  The  ApplicaKon  of  Cluster  Analysis  in  Strategic  Managment  Reserach:  An  Analysis  and   CriKque.  In:  Strat.  Mgmt.  J.  17  (6).     Han,  Jiawei;  Kamber,  Micheline  (2011):  Data  mining.  Concepts  and  techniques.     Mooi,  Erik;  Sarstedt,  Marko  (2011):  Cluster  Analysis.  In:  A  Concise  Guide  to  Market  Research.  S.  237-­‐284.       Miligan,  Glenn  W.  (1996):  Clustering  ValidaKon:  Results  and  ImplicaKons  for  Applied  Analyses.  In  Phipps  Arabie,  Lawrence  J.  Hubert,   Geert  de  Soete  (Eds.):  Clustering  and  classificaKon.  pp.  341–376.   Case  studies   Data  collecKon   &  coding   Build  the   framework   Literature  Review   Finding   PaVerns   2.  Clustering   method   1.  Clustering   Variables   3.  Number  of   Clusters   4.  Validate  &   Interpret  C.  
  23. 23. 7 Business Model Cluster were identified 23       Cluster   1   2   3   4   5   6   7   Data  Source   Acquired  Data   0   0   1   0   0   0   0   Customer-­‐provided  Data   0   1   1   0   0   1   1   Free  available   1   0   1   0   1   0   1   CrowdSourced   0   0   0   0   0   0   0   Tracked,  Generated  &  other   0   0   0   1   0   0   0   Key  AcKvity   AggregaKon   1   0   0   0   0   1   1   AnalyKcs   0   1   1   1   1   0   1   Data  acquisKon   0   0   1   0   0   0   0   Data  generaKon   0   0   0   1   0   0   1   Number  of  companies   17   28   5   16   14   6   14   Type   A   B   -­‐   C   D   E   F  
  24. 24. 6 significant Business Model types were identified 24   Type  B:  “AnalyKcs-­‐as-­‐a-­‐Service”   Type  C:  “Data  generaKon  &  AnalyKcs”   Type  D:  “Free  Data  Knowledge  Discovery”   Type  A:  “Free  Data  Collector  &  Aggregator”   Type  E:    “Data  AggregaKon-­‐as-­‐a-­‐Service”   Type  F:  “MulK-­‐Source  data  mashup  and  analysis”  
  25. 25. The 6 BM types are characterised by the key activities and key data sources 25   Type  F   Type  A   Type  D   Type  E   Type  B   Type  C   AggregaKon   AnalyKcs   Data  generaKon   Free    available   Customer   provided   Tracked  &   generated   Key  ac@vity   Key  Data  Source  
  26. 26. Type D: “Free Data Knowledge Discovery” 1.   DealAngel   2.   Gild   3.   Insightpool   4.   Juristat   5.   Market  Prophit   6.   MixRank   7.   Numberfire   8.   Olery   9.   PeerIndex   10.   PolyGraph   11.   Review  Signal   12.   Tellagence   13.   traackr   14.   TrendspoVr   -­‐  Free  available   -­‐  Social  Media   -­‐  Open  Data   -­‐  Web  Crawled   B2B   B2C   Key  AcKviKes   Revenue  Model   Key  Data  Source   -­‐  AnalyKcs   Target  Customer   0   5   10   15   DescripKve   PredicKve   PrescripKve   0   2   4   6   8   SubscripKon   Usage  Fee   AdverKsing   Brokearge  Fees   No  InformaKon   Companies  
  27. 27. Type D: Examples 27   “Using  patent-­‐pending  technology,  Gild   evaluates  the  work  of  millions  of   developers  so  companies  using  Gild’s   talent  acquisiKon  tools  know  who’s  good   and  can  target  the  right  candidates.”     •  Key  Data:  Free  available  websites   (GitHub,  Google  Codes)   •  Key  AcKviKes:  AnalyKcs   •  Revenue  Model:  Monthly  subscripKon   •  Target  Customer:  B2B     “  Our  goal  is  to  provide  the  most   accurate  and  honest  reviews  possible  by   using  the  data  consumers  create.  We   listen  to  the  conversaKons,  analyze  them   and  visualize  them  for  consumers.”     •  Key  Data:  TwiVer   •  Key  AcKviKes:  AnalyKcs   •  Revenue  Model:  AdverKsing   •  Target  Customer:  B2B  (B2C)    
  28. 28. Finding   PaVerns   The cases studies will be validated the framework and the clustering 28   Data  collecKon   &  coding   Build  the   framework   Literature  Review   Case  studies   4  case  studies  with   companies  from  the   sample  such  as     Purpose:   1.  Validate  framework  &   clusters   2.  Illustrate  business   model  types  through   examples   3.  IdenKfy  specific   challenges    
  29. 29. Summary 29   -­‐  Findings:   -­‐  This  study  explores  how  start-­‐up  business  models  capture  value  from   big  data.     -­‐  The  study  also  introduces  the  DDBM  framework  with  which  the   business  models  can  be  studied  and  analysed   -­‐  A  proposed  taxonomy  consisKng  of  six  types  of  start-­‐up  business   model  is  developed.     -­‐  These  types  are  characterised  by  a  subset  of  six  of  nine  clustering   variables  from  the  DDBM  framework.         -­‐  Prac@cal  implica@ons:     -­‐  The  study  helps  not  only  future  researchers  to  structure  their  work   around  data-­‐driven  business  models  but  also  companies  to  build  new   DDBMs.     -­‐  The  proposed  taxonomy  will  help  companies  to  posiKon  their  acKviKes   in  the  current  landscape.      
  30. 30. Limitations & Outlook 30   LimitaKons   •  Only  100  samples   •  Only  start  up  companies     •  Bias  of  data  source  (AngelList)   •  StaKsKcal  significance  of   clustering  result   •  Only  public  available  sources   used   •  No  statement  about  success  of   a  parKcular  business  model   Outlook/Next  Steps   1.  Improve  validity  of  findings   1.  Increase  sample  size  to  test   clusters   2.  More  Case-­‐studies  to   illustrate/validate  clusters   2.  Include  established  organiza@ons   3.  Develop  methodology  to  judge   (financial)  performance  of   different  business  models    
  31. 31. Further Reading 31   hVp://www.cambridgeservicealliance.org/uploads/downloadfiles/ 2014_March_Data%20Driven%20Business%20Models.pdf  
  32. 32. Forthcoming Webinars 32   0ct.  13th  2014        Industry  transformaKon  towards  a  service  logic:  a  business  model   approach.  Speaker:  Anna  Vijakainen       Nov.10th  2014      The  B2C  lock-­‐in  effect.  Speaker:  Marcus  Eurich  
  33. 33. Appendix 33  
  34. 34. The Clustering Process 34   Variables  relevant  to   determine  clustering   (Miligan  1996)   #Variables  has  to   match  #samples   (Mooi  2011)     ~  2m  samples  for  m   variables:     6-­‐7  variables   Avoid  high  correlaKon   between  variables   (<0.9)  (Mooi  2011)   2  Dimensions:    “Data  source”  &     “Key  AcKvity”       9  variables   max.  correlaKon:   0,5   2.  Clustering   method   3.  Number  of   Clusters   4.  Validate  &   Interpret  C.   1.  Clustering   Variables  
  35. 35. The Clustering Process 35   ParKKoning   Hierarchical   Density-­‐based   Grid-­‐based   Clustering   Method   (Han  2011)   Proximity   Measure   4.  Validate  &   Interpret  C.   1.  Clustering   Variables   3.  Number  of   Clusters   2.  Clustering   method   K-­‐Medoids   Include  neg.  match   Exclude  neg.  match   Euclidean   Distance  
  36. 36. There is no “one right solution” for the number of clusters 36   large  to  reflect  specific   differences   k  <<  n   1.  Use  a-­‐priori  knowledge  to  determine  number  of  clusters   2.  Visual  approaches   3.  Rule  of  thumb  (Han  2011):     4.  “Elbow”  method   5.  StaKsKcal  methods   𝑘  ~√⁠​ 𝑛/2    → 𝑘  ~  7   k?   2.  Clustering   method   4.  Validate  &   Interpret  C.   1.  Clustering   Variables   3.  Number  of   Clusters   Several  different  approaches  (Pham  2005,  Mooi  2011,  Han  2011,  EveriV  et.  al.  2011):  
  37. 37. “Elbow” method 37   “Elbow  Method”  (cf.  Ketchen  1993,  Mooi  2011):     1.  Hierarchical  clustering  first   2.  Plot  agglomeraKon  coefficient  against  number  of  clusters   3.  Search  for  “elbows”   2.  Clustering   method   4.  Validate  &   Interpret  C.   1.  Clustering   Variables   3.  Number  of   Clusters  
  38. 38. “Elbow” method 38   0.000   0.500   1.000   1.500   2.000   2.500   2   4   6   8   10  12  14  16  18  20  22  24  26  28  30  32  34  36  38  40  42  44  46  48  50  52  54  56  58  60  62  64  66  68  70  72  74  76  78  80  82  84  86  88  90  92  94  96  98   Clustering  Coefficient  (distance)   <29  7   16   2.  Clustering   method   4.  Validate  &   Interpret  C.   1.  Clustering   Variables   3.  Number  of   Clusters   Number  of  cluster  k  
  39. 39. Statistical Measure: Silhouette 39   0   0.05   0.1   0.15   0.2   0.25   0.3   0.35   0.4   0.45   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18   19   20   SilhoueVe  Coefficient   2.  Clustering   method   4.  Validate  &   Interpret  C.   1.  Clustering   Variables   3.  Number  of   Clusters   For  datum  i:     Compares  distance   within  its  cluster  to   distance  to  nearest   neigbouring  cluster     −1≤ 𝑠( 𝑖)≤1   SilhoueVe  Coefficient  s(i)   Number  of  cluster  k   Rousseeuw,  Peter  J.  (1987):  SilhoueVes:  A  graphical  aid  to  the  interpretaKon  and  validaKon  of   cluster  analysis.  In  Journal  of  Computa2onal  and  Applied  Mathema2cs  20  (0).  
  40. 40. The Clustering Process 40   0.335   -­‐1   -­‐0.5   0   0.5   1   SilhoueVe  Value   -­‐0.40     -­‐0.20      -­‐          0.20      0.40      0.60      0.80      1.00     1   6   11   16   21   26   31   36   41   46   51   56   61   66   71   76   81   86   91   96   SilhoueVe   2.  Clustering   method   1.  Clustering   Variables   3.  Number  of   Clusters   4.  Validate  &   Interpret  C.   good  no  cluster  

×