Capturing Value from Big Data through Data-Driven Business Models · 2013. 12. 5. · Tag: “big data” “big data analytics” 1329 companies Data collection Company information

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

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

Two general areas can be identified where

big data creates value

3

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

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

q

ues

tio

ns

Data-driven business model framework

How to identify patterns?

Res

earc

h

Qu

est

ion

Clustering

The research was done in five steps

5

Case studies Finding Patterns

Data collection & coding

Build the framework

Literature Review

How to analyse data-driven business

models?

How to identify patterns?

The first step was a literature review with

three different topics

6

Lite

ratu

re R

evie

w

Big Data

Definition

Value Creation

Business Model

Definition

Business Model Frameworks

Related Work

Data driven business Models

Cloud business models



Build the framework

Literature Review

The first step was a literature review with

three different topics

7

Lite

ratu

re R

evie

w

Big Data

Definition

Value Creation

Business Model

Definition


Related Work





Build the framework

Literature Review

Literature review: Business Model

8

Lite

ratu

re R

evie

w

Big Data

Definition

Value Creation

Business Model

Definition


Related Work





Build the framework

Literature Review

Business model key components were

synthesized from existing frameworks

Existing Business Model Frameworks

- Chesbrough & Rosenbloom 2002 - Hedman & Kaling 2003 - Osterwalder 2004 - Morris 2005 - Johnson, Christensen et. al. 2008 - Al-Debei 2010 - Burkhart 2012

Val

ue

Cap

turi

ng

Val

ue

Cre

atio

n

Key Resources

Key Activities

Cost structure

Revenue Model

Customer Segment

Value Proposition

Business Model Definition Business Model Key Components

- No universally accepted definition of the concept (Weill, Malone et al. 2011)

- Most definitions refer to value creation & value capturing

Only a few papers are available in this field

10

Lite

ratu

re R

evie

w

Big Data

Definition

Value Creation

Business Model

Definition


Related Work





Build the framework

Literature Review

The review was extend to cloud business

models

11

Lite

ratu

re R

evie

w

Big Data

Definition

Value Creation

Business Model

Definition


Related Work





Build the framework

Literature Review

The literature review identified several gaps

12

• 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)



Build the framework

Literature Review

The framework was build from literature

starting from the key components

Data-Driven-Business Model

Data Sources

Internal existing data

Self-generated

Data

External

Acquired Data

Customer provided

Free available

Open Data

Social Media data Web

Crawled Data

Key Activity

Data Generation

Crowdsourcing

Tracking & Other Data

Acquisition

Processing

Aggregation

Analytics

descriptive

predictive

prescriptive Visualization

Distribution

Offering

Data

Information/Knowledge Non-Data

Product/Service

Target Customer

B2B

B2C

Revenue Model

Asset Sale

Lending/Renting/Leasing

Licensing

Usage fee

Subscription fee

Advertising

Specific cost advantage

Data-Driven Business Model

Data Sources

Key Activity

Offering

Target Customer

Revenue Model




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

Synthesizing the different sources leads to

the taxonomy

14

Data Sources

Internal

existing data

Self-generated Data

External

Acquired Data

Customer provided

Free available

Open Data

Social Media data

Web Crawled Data

Dimension: Activities

15

Key Activity

Data Generation

Crowdsourcing

Tracking & Other

Data Acquisition

Processing

Aggregation

Analytics

descriptive

predictive


Distribution

Dimension: Offering

16

Offering

Data

Information/Knowledge

Non-Data Product/Service

Dimension: Revenue Model

17

Revenue Model

Asset Sale


Licensing

Usage fee

Subscription fee

Advertising

Dimension: Target Customer

18

Target Customer

B2B

B2C


The final framework

19


Literature Review Build the

framework

Data-Driven-Business Model

Data Sources

Internal

existing data

Self-generated Data

External

Acquired Data

Customer provided

Free available

Open Data

Social Media data

Web Crawled Data

Key Activity

Data Generation

Crowdsourcing

Tracking & Other

Data Acquisition

Processing

Aggregation

Analytics

descriptive

predictive


Distribution

Offering

Data

Information/Knowledge

Non-Data Product/Service

Target Customer

B2B

B2C

Revenue Model

Asset Sale


Licensing

Usage fee

Subscription fee

Advertising


Data collection and coding

20


Build the framework

Literature Review Data collection

& coding

Data collection Data analysis Sampling

The data was generated using public

available sources

21

Tag: “big data” “big data analytics”

1329 companies

Data collection

Company information • 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

Overall Analysis: Data Source

22

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 multiple data sources

• >50% of companies rely on free available data

• >50% of companies use data provided by customers/partners

Overall Analysis: Key Activities

23

0% 10% 20% 30% 40% 50% 60% 70% 80%

Aggregation

Analytics

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Data acquistion

Data generation

Data processing

Distribution

Visualization

• >70% of companies use analytics - mostly descriptive

Note: Sum > 100% as some companies rely on multiple revenue models

Overall Analysis: Revenue Model

24

0% 10% 20% 30% 40% 50%

Advertising

Asset Sales

Brokerage Fees

Lending Renting Leasing

Licensing

Subscription fee

Usage Fee

No information

• 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

Note: Sum > 100% as some companies rely on multiple revenue models

Overall Analysis: Target Customer

25

70%

17%

13%

B2B B2C both

• There seems to be a noteworthy predominance of B2B business models

• But no reference data found

BM patterns were identified using a

clustering approach

26

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.

Case studies Data collection

& coding Build the

framework Literature Review

Finding Patterns

2. Clustering method

1. Clustering Variables

3. Number of Clusters

4. Validate & Interpret C.

7 Business Model Cluster were identified

27

Cluster 1 2 3 4 5 6 7

Dat

a So

urc

e

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

Act

ivit

y 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

6 significant Business Model types were

identified

28

Type B: “Analytics-as-a-Service”

Type C: “Data generation & Analytics”

Type D: “Free Data Knowledge Discovery”

Type A: “Free Data Collector & Aggregator”

Type E: “Data Aggregation-as-a-Service”

Type F: “Multi-Source data mashup and analysis”

The 6 BM types are characterised by the key

activities and key data sources

29

Type F

Type A Type D

Type E Type B

Type C

Aggregation Analytics Data generation

Free

a

vaila

ble

C

ust

om

er

pro

vid

ed

Trac

ked

&

gen

erat

ed

Key activity

Key

Dat

a So

urc

e

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. Trendspottr

- Free available

- Social Media - Open Data - Web Crawled

B2B B2C

Key Activities

Revenue Model

Key Data Source

- Analytics

Target Customer

0 5 10 15

Descriptive

Predictive

Prescriptive

0 2 4 6 8

Subscription

Usage Fee

Advertising

Brokearge Fees

No Information

Companies

Type D: Examples

31

“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)

Finding Patterns

The cases studies will be validated the

framework and the clustering

32


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. Identify specific challenges

Summary

33

- 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

Limitations & Outlook

34

Limitations

• Only 100 samples

• Only start up companies • Bias of data source (AngelList)

• Statistical significance of

clustering result

• Only public available sources used

• No statement about success of a particular 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 organizations

3. Develop methodology to judge (financial) performance of different business models

Appendix

35

Literature

36

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.

Literature

37

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.

Literature

38

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.

Literature

39

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.

Literature

40

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.

The Clustering Process

41

Variables relevant to determine clustering

(Miligan 1996)

#Variables has to match #samples

(Mooi 2011)

~ 2m samples for m variables:

6-7 variables

Avoid high correlation between variables (<0.9) (Mooi 2011)

2 Dimensions: “Data source” &

“Key Activity”

9 variables

max. correlation: 0,5






42

Partitioning

Hierarchical

Density-based

Grid-based

Clustering Method

(Han 2011)

Proximity Measure





K-Medoids

Include neg. match

Exclude neg. match

Euclidean Distance

There is no “one right solution” for the

number of clusters

43

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. Statistical methods

𝑘 ~𝑛

2 → 𝑘 ~ 7

k?





Several different approaches (Pham 2005, Mooi 2011, Han 2011, Everitt et. al. 2011):

“Elbow” method

44

“Elbow Method” (cf. Ketchen 1993, Mooi 2011): 1. Hierarchical clustering first 2. Plot agglomeration coefficient against number of clusters 3. Search for “elbows”





“Elbow” method

45

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





Number of cluster k

Statistical Measure: Silhouette

46

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

Silhouette Coefficient





For datum i: Compares distance within its cluster to distance to nearest neigbouring cluster

−1 ≤ 𝑠(𝑖) ≤ 1

Silhouette Coefficient s(i)

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).


47

0.335

-1 -0.5 0 0.5 1

Silhouette Value

(0.40) (0.20) - 0.20 0.40 0.60 0.80 1.00

16

111621263136414651566166717681869196

Silhouette





good no cluster

Documents

Capturing Value from Big Data through Data-Driven Business Models · 2013. 12. 5. · Tag: “big data” “big data analytics” 1329 companies Data collection Company information