Idea Transcript
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
“Data is the new oil”1 Data volume per year (Exabytes)2 45000
40000 35000 30000 25000 20000 15000 10000 5000 0 2005 2 IDC's
1 various
2010
Digital Universe Study, December 2012
authors, e.g. Clive Humby
2015
2020
Two general areas can be identified where big data creates value How to get value from Big Data?
Optimization of existing business1
New Business Models1 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 2012
3
Sub questions
Research Question
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
The research was done in five steps
Literature Review
Build the framework
How to analyse datadriven business models?
Data collection & coding
Finding Patterns
Case studies
How to identify patterns?
5
The first step was a literature review with three different topics Build the framework
Literature Review
Data collection & coding
Finding Patterns
Case studies
Definition Big Data
Literature Review
Value Creation Definition Business Model Business Model Frameworks Data driven business Models Related Work Cloud business models
6
The first step was a literature review with three different topics Build the framework
Literature Review
Data collection & coding
Finding Patterns
Case studies
Definition Big Data
Literature Review
Value Creation Definition Business Model Business Model Frameworks Data driven business Models Related Work Cloud business models
7
Literature review: Business Model Build the framework
Literature Review
Data collection & coding
Finding Patterns
Case studies
Definition Big Data
Literature Review
Value Creation Definition Business Model Business Model Frameworks Data driven business Models Related Work Cloud business models
8
Business model key components were synthesized from existing frameworks - No universally accepted definition of the concept (Weill, Malone et al. 2011) - Most definitions refer to value creation & value capturing
Business Model Key Components
Value Creation
Business Model Definition
Key Resources Key Activities Value Proposition
-
Chesbrough & Rosenbloom 2002 Hedman & Kaling 2003 Osterwalder 2004 Morris 2005 Johnson, Christensen et. al. 2008 Al-Debei 2010 Burkhart 2012
Value Capturing
Existing Business Model Frameworks Customer Segment Revenue Model Cost structure
Only a few papers are available in this field Build the framework
Literature Review
Data collection & coding
Finding Patterns
Case studies
Definition Big Data
Literature Review
Value Creation Definition Business Model Business Model Frameworks Data driven business Models Related Work Cloud business models
10
The review was extend to cloud business models Build the framework
Literature Review
Data collection & coding
Finding Patterns
Case studies
Definition Big Data
Literature Review
Value Creation Definition Business Model Business Model Frameworks Data driven business Models Related Work Cloud business models
11
The literature review identified several gaps Build the framework
Literature Review
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
The framework was build from literature starting from the key components Literature Review
Build the framework
Data collection & coding
Finding Patterns
Case studies
Data-Driven Business Model Framework
Business Model Key Components (Dimensions)
Internal
Data Sources
Features for data sources
Data Sources External
Data Generation Data Acquisition
Key Activity
existing data Selfgenerated Data Acquired Data Customer provided Free available Crowdsourci ng Tracking & Other
Processing Key Activity
Offering Data-Driven Business Model Target Customer
Features for each dimension
Data-DrivenBusiness Model
Aggregation
predictive
Visualization
prescriptive
Distribution Data Offering
Target Customer
Revenue Model
Information/ Knowledge Non-Data Product/Serv ice B2B B2C Asset Sale Lending/Ren ting/Leasing
Specific cost advantage
Revenue Model Specific cost advantage
descriptive
Analytics
Licensing Usage fee Subscription fee Advertising
Open Data Social Media data Web Crawled Data
Synthesizing the different sources leads to the taxonomy
existing data Internal Self-generated Data Data Sources
Acquired Data
External
Customer provided
Open Data
Free available
Social Media data Web Crawled Data
14
Dimension: Activities Crowdsourcing Data Generation
Tracking & Other Data Acquisition Processing Key Activity
Aggregation
descriptive
Analytics
predictive
Visualization
prescriptive
Distribution 15
Dimension: Offering
Data
Offering
Information/Knowledge
Non-Data Product/Service
16
Dimension: Revenue Model Asset Sale
Lending/Renting/Leasing
Licensing Revenue Model Usage fee
Subscription fee
Advertising
17
Dimension: Target Customer
B2B Target Customer B2C
18
The final framework Literature Review
Build the framework
Data collection & coding
Finding Patterns
Case studies
existing data Internal
Data Sources
Self-generated Data Acquired Data
External
Customer provided
Open Data
Free available
Social Media data
Crowdsourcing
Web Crawled Data
Data Generation Tracking & Other Data Acquisition Processing Key Activity
Data-DrivenBusiness Model
Aggregation
descriptive
Analytics
predictive
Visualization
prescriptive
Distribution Data Offering
Information/Kno wledge Non-Data Product/Service B2B
Target Customer B2C Asset Sale Lending/Renting /Leasing Licensing Revenue Model Specific cost advantage
Usage fee Subscription fee Advertising
19
Data collection and coding Literature Review
Sampling
Build the framework
Data collection & coding
Data collection
Finding Patterns
Case studies
Data analysis
20
The data was generated using public available sources Sampling
Tag: “big data” “big data analytics” 1329 companies
Data collection
Company information • Company websites • Press releases
Data analysis
• Coding of sources using data driven business model framework
Public sources • Nvivo
Random sample
cleaning
100 Companies
299 Sources ~3 sources/comp
100 binary feature vectors
21
Overall Analysis: Data Source 0%
10%
20%
30%
40%
50%
60%
Acquired Data
Customer&Partner-provided Data
Free available
• >50% of companies rely on free available data • >50% of companies use data provided by customers/partners
Crowd Sourced
Tracked & Other
Note: Sum > 100% as companies might use multiple data sources 22
Overall Analysis: Key Activities 0%
10%
20%
30%
40%
50%
60%
70%
80%
Aggregation Analytics Descriptive Analytics Predictive Analytics
Prescriptive Analytics Data acquistion
• >70% of companies use analytics - mostly descriptive
Data generation Data processing Distribution Visualization
Note: Sum > 100% as some companies rely on multiple revenue models 23
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
Overall Analysis: Target Customer
13% • There seems to be a noteworthy predominance of B2B business models
17%
70%
B2B
B2C
• But no reference data found
both 25
BM patterns were identified using a clustering approach Literature Review
1. Clustering Variables
Build the framework
Data collection & coding
2. Clustering method
Finding Patterns
3. Number of Clusters
Case studies
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. 237-284. 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. 341–376.
26
7 Business Model Cluster were identified 1
2
3
4
5
6
7
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
Aggregation
1
0
0
0
0
1
1
Analytics
0
1
1
1
1
0
1
Data acquistion
0
0
1
0
0
0
0
Data generation
0
0
0
1
0
0
1
Number of companies
17
28
5
16
14
6
14
Type
A
B
-
C
D
E
F
Key Activity
Data Source
Cluster
27
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
Customer provided
Type C
Type E
Type B
Type F Free available
Key Data Source
Tracked & generated
The 6 BM types are characterised by the key activities and key data sources
Type A
Type D
Aggregation
Analytics Key activity
Data generation
29
Type D: “Free Data Knowledge Discovery” Companies
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
DealAngel Gild Insightpool Juristat Market Prophit MixRank Numberfire Olery PeerIndex PolyGraph Review Signal Tellagence traackr Trendspottr
Key Data Source
Key Activities
- Free available
- Analytics 0
- Social Media - Open Data - Web Crawled
5
10
15
Descriptive Predictive Prescriptive
Revenue Model 0
2
4
Target Customer 6
8
Subscription Usage Fee Advertising Brokearge Fees No Information B2B
B2C
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.”
“ 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: Free available websites (GitHub, Google Codes)
• Key Data: Twitter • Key Activities: Analytics
• Key Activities: Analytics • Revenue Model: Advertising • Revenue Model: Monthly subscription • Target Customer: B2B (B2C) • Target Customer: B2B 31
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
4 case studies with companies from the sample such as
2. Illustrate business model types through examples 3. Identify specific challenges
32
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
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
Appendix
35
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. 359–376. 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. 88–102. 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. 529–555. 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. 319–333.
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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. 5th 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. 3rd 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. 49–59.
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Literature Johnson, Mark W.; Christensen, Clayton M.; Kagermann, Henning (2008): Reinventing your business model. In Harvard Business Review 86 (12), pp. 57–68. 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. 726–735. Negash, Solomon (2004): Business Intelligence. In Communications of the Association for Information Systems 13, pp. 177–195.
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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. 475–489.
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. 103–119. 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. 1445–1459. 39
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. 53–65. 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. 41–50. Weill, Peter; Malone, Thomas W.; Apel, Thomas G. (2011): The business models investors prefer. In MIT Sloan Management Review 52 (4), p. 17.
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The Clustering Process 1. Clustering Variables
Variables relevant to determine clustering (Miligan 1996)
2. Clustering method
3. Number of Clusters
4. Validate & Interpret C.
2 Dimensions: “Data source” & “Key Activity” 9 variables
#Variables has to match #samples (Mooi 2011) Avoid high correlation between variables (