capturing value from big data through data driven business models prensetation

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

“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

How to get value from Big Data?

3

OpKmizaKon of exisKng service

Data Driven Business Models1

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

quesKo

ns

Data-‐driven business model framework

How to idenKfy paVerns?

Research

Que

sKon

Clustering

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?

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




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 Ca

pturing

Value Crea@o

n 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

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)




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

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

Data-‐Driven Business Model

Data Sources

Key AcKvity

Offering

Target Customer

Revenue Model


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

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

Dimension: Activities

11

Key AcKvity

Data GeneraKon

Crowdsourcing

Tracking & Other

Data AcquisiKon

Processing

AggregaKon

AnalyKcs

descripKve

predicKve


DistribuKon

Dimension: Offering

12

Offering

Data

InformaKon/Knowledge

Non-‐Data Product/Service

Dimension: Revenue Model

13

Revenue Model

Asset Sale

Lending/RenKng/Leasing

Licensing

Usage fee

SubscripKon fee

AdverKsing

Dimension: Target Customer

14

Target Customer B2B

B2C


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


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


Data collection and coding

16


Build the framework Literature Review Data collecKon

& coding

Data collecKon Data analysis Sampling

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

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

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

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

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

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.

7 Business Model Cluster were identified

23

Cluster 1 2 3 4 5 6 7

Data Sou

rce

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

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”

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

Custom

er

provided

Tracked &

gene

rated

Key ac@vity

Key Da

ta Sou

rce

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

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)

Finding PaVerns

The cases studies will be validated the framework and the clustering

28


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

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.

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

Further Reading

31

hVp://www.cambridgeservicealliance.org/uploads/downloadfiles/2014_March_Data%20Driven%20Business%20Models.pdf

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

Appendix

33

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






35

ParKKoning

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

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?





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

“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”





“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





Number of cluster k

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





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

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





good no cluster

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