Capturing Value from Big Data through Data-Driven Business Models

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1 Capturing Value from Big Data through Data-Driven Business Models Patterns from the Start-up world Philipp Hartman, Dr Mohamed Zaki and Prof Duncan McFarlane Cambridge Service Alliance University of Cambridge

2 Data is the new oil Data volume per year (Exabytes) IDC's Digital Universe Study, December various authors, e.g. Clive Humby

3 Two general areas can be identified where big data creates value How to get value from Big Data? Optimization of existing business 1 New Business Models 1 Chesbrough, Rosenbloom (2002): Business model to capture value from an innovation Crisculo (2012): New technologies often first commercialized through start-up companies 1 cf. McKinsey 2011, IBM 2012, Davenport 2006, AT Kearney 2013, EMC

4 Research Question Sub questions Based on this motivation the research question was developed 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 datadriven business models? How to identify patterns? Data-driven business model framework Clustering 4

5 The research was done in five steps Literature Review Build the framework Data collection & coding Finding Patterns Case studies How to analyse datadriven business models? How to identify patterns? 5

6 The first step was a literature review with three different topics Literature Review Literature Review Build the framework Data collection & coding Finding Patterns Case studies Big Data Definition Value Creation Business Model Related Work Definition Business Model Frameworks Data driven business Models Cloud business models 6

7 The first step was a literature review with three different topics Literature Review Literature Review Build the framework Data collection & coding Finding Patterns Case studies Big Data Definition Value Creation Business Model Related Work Definition Business Model Frameworks Data driven business Models Cloud business models 7

8 Literature Review Literature review: Business Model Literature Review Build the framework Data collection & coding Finding Patterns Case studies Definition Big Data Value Creation Business Model Related Work Definition Business Model Frameworks Data driven business Models Cloud business models 8

9 Business model key components were synthesized from existing frameworks Value Capturing Value Creation Business Model Definition Business Model Key Components - No universally accepted definition of the concept (Weill, Malone et al. 2011) Key Resources - Most definitions refer to value creation & value capturing Existing Business Model Frameworks - Chesbrough & Rosenbloom Hedman & Kaling Osterwalder Morris Johnson, Christensen et. al Al-Debei Burkhart 2012 Key Activities Value Proposition Customer Segment Revenue Model Cost structure

10 Literature Review Only a few papers are available in this field Literature Review Build the framework Data collection & coding Finding Patterns Case studies Definition Big Data Value Creation Business Model Related Work Definition Business Model Frameworks Data driven business Models Cloud business models 10

11 The review was extend to cloud business models Literature Review Literature Review Build the framework Data collection & coding Finding Patterns Case studies Big Data Definition Value Creation Business Model Related Work Definition Business Model Frameworks Data driven business Models Cloud business models 11

12 The literature review identified several gaps Literature Review Build the framework Data collection & coding Finding Patterns Case studies Little academic research on big data and value creation mostly whitepapers Gap in literature: data-driven business models Otto, Aier (2013) interesting paper but limited to specific industry > no generalization possible Similar research for cloud business models (cf. Labes, Erek et. Al. 2013) 12

13 The framework was build from literature starting from the key components Literature Review Build the framework Data collection & coding Finding Patterns Case studies Business Model Key Components (Dimensions) Data-Driven Business Model Framework Data Sources Key Activity Features for data sources Data Sources Internal External Data Generation Data Acquisition existing data Selfgenerated Data Acquired Data Customer provided Free available Crowdsourci ng Tracking & Other Open Data Social Media data Web Crawled Data Processing Data-Driven Business Model Offering Target Customer Revenue Model Features for each dimension Data-Driven- Business Model Key Activity Offering Target Customer Aggregation Analytics Visualization Distribution Data Information/ Knowledge Non-Data Product/Serv ice B2B B2C Asset Sale Lending/Ren ting/leasing descriptive predictive prescriptive Specific cost advantage Revenue Model Specific cost advantage Licensing Usage fee Subscription fee Advertising

14 Synthesizing the different sources leads to the taxonomy Internal existing data Self-generated Data Data Sources Acquired Data External Customer provided Open Data Free available Social Media data Web Crawled Data 14

15 Dimension: Activities Data Generation Data Acquisition Crowdsourcing Tracking & Other Processing Key Activity Aggregation descriptive Analytics predictive Visualization prescriptive Distribution 15

16 Dimension: Offering Data Offering Information/Knowledge Non-Data Product/Service 16

17 Dimension: Revenue Model Asset Sale Lending/Renting/Leasing Revenue Model Licensing Usage fee Subscription fee Advertising 17

18 Dimension: Target Customer Target Customer B2B B2C 18

19 The final framework Literature Review Build the framework Data collection & coding Finding Patterns Case studies Data Sources Internal existing data Self-generated Data Acquired Data External Data Generation Data Acquisition Customer provided Free available Crowdsourcing Tracking & Other Open Data Social Media data Web Crawled Data Processing Key Activity Aggregation descriptive Analytics predictive Visualization prescriptive Data-Driven- Business Model Distribution Data Offering Target Customer Information/Kno wledge Non-Data Product/Service B2B B2C Revenue Model Specific cost advantage Asset Sale Lending/Renting /Leasing Licensing Usage fee Subscription fee Advertising 19

20 Data collection and coding Literature Review Build the framework Data collection & coding Finding Patterns Case studies Sampling Data collection Data analysis 20

21 The data was generated using public available sources Sampling Data collection Data analysis Tag: big data big data analytics 1329 companies Company information Company websites Press releases Public sources Coding of sources using data driven business model framework Nvivo Random sample cleaning 100 Companies 299 Sources ~3 sources/comp 100 binary feature vectors 21

22 Overall Analysis: Data Source 0% 10% 20% 30% 40% 50% 60% Acquired Data Customer&Partner-provided Data Free available Crowd Sourced >50% of companies rely on free available data >50% of companies use data provided by customers/partners Tracked & Other Note: Sum > 100% as companies might use multiple data sources 22

23 Overall Analysis: Key Activities Aggregation Analytics 0% 10% 20% 30% 40% 50% 60% 70% 80% Descriptive Analytics Predictive Analytics Prescriptive Analytics Data acquistion Data generation >70% of companies use analytics - mostly descriptive Data processing Distribution Visualization Note: Sum > 100% as some companies rely on multiple revenue models 23

24 Overall Analysis: Revenue Model 0% 10% 20% 30% 40% 50% Advertising Asset Sales Brokerage Fees Lending Renting Leasing Licensing Subscription fee Usage Fee Majority of revenue models based on subscription and/or usage fee No information about the revenue model as many companies are in an early stage No information Note: Sum > 100% as some companies rely on multiple revenue models 24

25 Overall Analysis: Target Customer 13% 17% There seems to be a noteworthy predominance of B2B business models 70% But no reference data found B2B B2C both 25

26 BM patterns were identified using a clustering approach Literature Review Build the framework Data collection & coding Finding Patterns Case studies 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Ketchen, David J.; Shook, Christopher L. (1996): The Application of Cluster Analysis in Strategic Managment Reserach: An Analysis and Critique. 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 Miligan, Glenn W. (1996): Clustering Validation: Results and Implications for Applied Analyses. In Phipps Arabie, Lawrence J. Hubert, Geert de Soete (Eds.): Clustering and classification. pp

27 Key Activity Data Source 7 Business Model Cluster were identified Cluster Acquired Data Customer-provided Data Free available CrowdSourced Tracked, Generated & other Aggregation Analytics Data acquistion Data generation Number of companies Type A B - C D E F 27

28 6 significant Business Model types were identified Type A: Free Data Collector & Aggregator Type B: Analytics-as-a-Service Type C: Data generation & Analytics Type D: Free Data Knowledge Discovery Type E: Data Aggregation-as-a-Service Type F: Multi-Source data mashup and analysis 28

29 The 6 BM types are characterised by the key activities and key data sources Key Data Source Tracked & generated Customer provided Free available Type C Type E Type B Type F Type A Type D Aggregation Analytics Data generation Key activity 29

30 Type D: Free Data Knowledge Discovery Companies 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. Trendspottr Key Data Source - Free available - Social Media - Open Data - Web Crawled Revenue Model Subscription Usage Fee Advertising Brokearge Fees No Information Key Activities - Analytics Descriptive Predictive Prescriptive Target Customer B2B B2C

31 Type D: Examples Using patent-pending technology, Gild evaluates the work of millions of developers so companies using Gild s talent acquisition tools know who s good and can target the right candidates. Key Data: Free available websites (GitHub, Google Codes) Key Activities: Analytics Revenue Model: Monthly subscription 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 conversations, analyze them and visualize them for consumers. Key Data: Twitter Key Activities: Analytics Revenue Model: Advertising Target Customer: B2B (B2C) 31

32 The cases studies will be validated the framework and the clustering Literature Review Build the framework Data collection & coding Finding Patterns Case studies Purpose: 1. Validate framework & clusters 2. Illustrate business model types through examples 4 case studies with companies from the sample such as 3. Identify specific challenges 32

33 Summary - Gap in literature identified - RQ: 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? - 5 (7) different business model patterns identified - Data-driven business model framework created to enable analysis - Pattern identification through clustering - Validation through Case-Studies 33

34 Limitations & Outlook Limitations Only 100 samples Only start up companies Bias of data source (AngelList) Statistical significance of clustering result Only public available sources used 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 organizations 3. Develop methodology to judge (financial) performance of different business models No statement about success of a particular business model 34

35 Appendix 35

36 Literature Al-Debei, Mutaz M.; Avison, David (2010): Developing a unified framework of the business model concept. In Eur J Inf Syst 19 (3), pp Burkhart, Thomas; Wolter, Stephan; Schief, Markus; Krumeich, Julian; Di Valentin, Christina; Werth, Dirk et al. (2012): A comprehensive approach towards the structural description of business models. In : Proceedings of the International Conference on Management of Emergent Digital EcoSystems. New York, NY, USA: ACM (MEDES 12), pp Chesbrough, H.; Rosenbloom, R. (2002): The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies. In Industrial and Corporate Change 11 (3), pp Criscuolo, Paola; Nicolaou, Nicos; Salter, Ammon (2012): The elixir (or burden) of youth? Exploring differences in innovation between start-ups and established firms. In Research Policy 41 (2), pp

37 Literature Davenport, Thomas H.; Harris, Jeanne G. (2007): Competing on analytics. The new science of winning. Boston, Mass: Harvard Business School Press. Everitt, Brian; Landau, Sabine; Leese, Morven (2011): Cluster analysis. 5 th ed. London, New York: Arnold; Oxford University Press. Hagen, Christian; Khan, Khalid; Ciobo, Marco; Miller, Jason; Wall, Dan; Evans, Hugo; Yadava, Ajay (2013): Big Data and the Creative Destruction of Today's Business Models. ATKearney. Han, Jiawei; Kamber, Micheline; Pei, Jian (2011): Data Mining. Concepts and Techniques. 3 rd ed. Burlington: Elsevier Science. Hedman, Jonas; Kalling, Thomas (2003): The business model concept: theoretical underpinnings and empirical illustrations. In European Journal of Information Systems 12 (1), pp

38 Literature Johnson, Mark W.; Christensen, Clayton M.; Kagermann, Henning (2008): Reinventing your business model. In Harvard Business Review 86 (12), pp Labes, Stine; Erek, Koray; Zarnekow, Ruediger (2013): Common Patterns of Cloud Business Models. In : AMCIS 2013 Proceedings. Manyika, James; Chui, Michael; Brown, Brad; Bughin, Jacques; Dobbs, Richard; Roxburgh, Charles; Hung Byres, Angela (2011): Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Morris, Michael; Schindehutte, Minet; Allen, Jeffrey (2005): The entrepreneur's business model: toward a unified perspective. In Special Section: The Nonprofit Marketing Landscape 58 (6), pp Negash, Solomon (2004): Business Intelligence. In Communications of the Association for Information Systems 13, pp

39 Literature Osterwalder, Alexander (2004): The Business Model Ontology. A Proposition in Design Science Research. These. Ecole des Hautes Etudes Commerciales de l Université de Lausanne, Lausanne. Otto, Boris; Aier, Stephan (2013): Business Models in the Data Economy: A Case Study from the Business Partner Data Domain. With assistance of Rainer Alt, Bogdan Franczyk. In : Proceedings of the 11th International Conference on Wirtschaftsinformatik (WI 2013) : The 11th International Conference on Wirtschaftsinformatik (WI 2013), vol. 1. Leipzig, pp Pham, D. T.; Dimov, S. S.; Nguyen, C. D. (2005): Selection of K in K-means clustering. In Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 219 (1), pp Pham, D. T.; Afify, A. A. (2007): Clustering techniques and their applications in engineering. In Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221 (11), pp

40 Literature Rousseeuw, Peter J. (1987): Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. In Journal of Computational and Applied Mathematics 20 (0), pp Schroeck, Michael; Shockley, Rebecca; Smart, Janet; Romero-Morales, Dolores; Tufano, Peter (2012): Analytics: The real-world use of big data. How innovative enterprises extract value from uncertain data. IBM Institute for Business Value; Saïd Business School at the University of Oxford. Singh, Ranjit; Singh, Kawaljeet (2010): A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing. In IJCSI International Journal of Computer Science I 7 (3), pp Weill, Peter; Malone, Thomas W.; Apel, Thomas G. (2011): The business models investors prefer. In MIT Sloan Management Review 52 (4), p

41 The Clustering Process 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Variables relevant to determine clustering (Miligan 1996) 2 Dimensions: Data source & Key Activity 9 variables #Variables has to match #samples (Mooi 2011) ~ 2 m samples for m variables: 6-7 variables Avoid high correlation between variables (<0.9) (Mooi 2011) max. correlation: 0,5 41

42 The Clustering Process 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Partitioning K-Medoids Clustering Method (Han 2011) Hierarchical Density-based Grid-based Proximity Measure Include neg. match Exclude neg. match Euclidean Distance 42

43 There is no one right solution for the number of clusters 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. large to reflect specific differences k? k << n Several different approaches (Pham 2005, Mooi 2011, Han 2011, Everitt et. al. 2011): 1. Use a-priori knowledge to determine number of clusters 2. Visual approaches 3. Rule of thumb (Han 2011): 4. Elbow method 5. Statistical methods k ~ n 2 k ~ 7 43

44 Elbow method 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Elbow Method (cf. Ketchen 1993, Mooi 2011): 1. Hierarchical clustering first 2. Plot agglomeration coefficient against number of clusters 3. Search for elbows 44

45 Elbow method 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Clustering Coefficient (distance) <29 Number of cluster k 45

46 Statistical Measure: Silhouette 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Silhouette Coefficient s(i) For datum i: Compares distance within its cluster to distance to nearest neigbouring cluster 1 s(i) Silhouette Coefficient Number of cluster k Rousseeuw, Peter J. (1987): Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. In Journal of Computational and Applied Mathematics 20 (0). 46

47 The Clustering Process 1. Clustering Variables 2. Clustering method 3. Number of Clusters 4. Validate & Interpret C. Silhouette (0.40) (0.20) Silhouette Value no cluster good

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