The document discusses visualizing ecosystems through network analysis. It provides examples of visualizing the mobile payments ecosystem and cloud computing ecosystem. It outlines the methodology used, which involves identifying companies and relationships, entering data into a database, defining visualization semantics, generating input files, and visualizing the networks. Key lessons discussed include identifying core players, network effects, and risks. Future directions proposed are developing decision environments for automated data collection and collaboration.

1. Visualizing Ecosystems
Prof. Bala Iyer
Babson College
Twitter: @balaiyer
6/18/12

2. Agenda
 Background
 Examples
 Methodology
 Lessons
 Future directions
2

3. Context
 Babson elective ―Clouds, Platforms and
Networks‖
 Sponsored work on clouds, m-
payments, high-tech and media
 Collaborators: Professors N.
Venkartaman, Chi-Hyon Lee and George
Wyner.
3

4. Ecosystem
 Loose networks – of suppliers, distributors,
outsourcing firms, makers of related products or
services, technology providers, and a host of other
organizations – that affect, and are affected by the
creation and delivery of a company’s own
offerings. [Marco Iansiti]
 An economic community supported by a
foundation of interacting organizations and
individuals—the organisms of the business world.
The economic community produces goods and
services of value to customers, who are themselves
members of the ecosystem [James Moore]
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5. Example 1
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6. M-payments
 Wireless Innovation Council (Dupont,
L'Oréal, lexis-nexis, Marriott,..)
 Industry stack
 Companies involved
 Relationships or dependencies
 Industry analysis
 Future trends
6

7. M-payment Stack
 Platform provider
 Network Operators
 Banks
 Credit Card Issuers
 Device Manufacturers
 Merchants
 Users
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8. 8 8

9. Platforms
9 9

10. Method used for Ecosystem
generation
 List of platform players created from
virtual currency platforms, experts, and
 Created a list of partners from news
feeds and websites
 Visualized using Pajek
10 10

11. Ecosystem
-- complementors
-- platforms
-- partnerships
11 11

12. Analysis
 237 companies, 25 platforms
 Many approaches to M-payment
 Diversity is good
 Many fragmented platforms
 Carriers and card issuers are working
across platforms
 Still evolving ecosystem
 Many experiments in emerging
economies
12 12

13. M-payment platform
Consumers
Complementors
• Credit card co,
operators, banks, device Transactions
mfc., merchants…
Platform Ad servers
Bid for ads Support development
Developers
Advertisers
• Fortune 500 firms, individuals
13

14. M-payment to Virtual Currencies
 FB credits and Bitcoins have made
virtual currencies popular
 Will they support purchase of real
goods?
 Can these companies create money?
 Should these transactions be taxed?
 Will it impact money supply & demand?
 Should these currencies be regulated?
14 14

15. Platform Wars
 A few platforms may emerge, may not
be a ―winner take all‖
 Learn from IBM, Google, Sony vs.
BetaMax
 Direct (developers) and Indirect (end
users) Network Effects
 Business models and subsidies
 Emergence of standards like OpenSocial
 Open vs. Closed APIs 15 15

16. Resources
 MIT Tech Review,
http://www.technologyreview.com/business/?id=27
 Virtual currency platforms
 Michael Cusumano’s work published in CACM
 The Evolution of Platform Thinking: How platform adoption
can be an important determinant of product and
technological success. Communications of The ACM, Vol 53,
No. 1, pp 32--34
 Platform Wars Come to Social Media. Communications of
The ACM, Vol 54, No. 4, pp 31—33
 Michael Cusumano. The Platform Leader’s Dilemma: Study
the lessons learned from past and present platform leaders.
Come to Social Media. Communications of The ACM, Vol 54,
No. 4, pp 31—33
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17. Example #2
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18. Cloud computing
 Society for Information Management-
APC sponsored study
 Vendors involved in cloud computing
 Companies using cloud technology
 Key benefits
 Lessons
18

19. Methodology
 Start with a focal set of firms (55 – 631)
 Determined dependencies
 Identified core
 Capture definitions of cloud from the web
(~70)
 Read descriptions
 Run through a tag cloud analysis
 Identified capabilities
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20. Cloud Ecosystem (partnerships)
Applications Collaboration Services
20
Infrastructure Platform

21. Core players
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22. Seven Cloud Computing
Capabilities
 Controlled interface
 Location independence
 Sourcing independence
 Virtual business environment
 Ubiquitous access
 Addressability and traceability
 Rapid elasticity
22 22

23. Other Examples
23

24. January 2007
Mashups
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25. IT Outsourcing Contracts (IDC data)
2009
25

26. IT Vendors Alliances
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27. 2002 Clustering coefficient
Central firms:
Between 1990 and 2002,
these firms account for
35 to 69% of the industry sale
Highest
Higher
High
Medium
Low
27

28. Methodology: EcoSysNetworks™
 Determine industry structure or stack
 Identify companies and attributes
 Get dependency information and attributes
 Enter information into database
 Determine semantics for firms (size, shape,
color)
 Determine semantics for links (thickness,
color)
 Create input file for visualization
 Visualize and interpret 28

29. Stacks
Video Games SmartOS
Content providers Network operator
Software developers Handset manufacturer
Software publishers Mobile OS provider
Platform provider Content providers and aggregators
Retailer Application developers
Consumer
29

30. Identify companies and attributes
 Sources
 Competitive list from industry publications
 News feeds
 User generated input (bookmarks or
brainstorming)
 Inputs
 Name
 Revenue
 Business type (stacks)
 Platform provider?
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31. Get alliance information and
attributes
 Sources
 Company websites
 News feeds
 Inputs
 Alliance type (technical, marketing,
strategic or financial)
 Single or multiple alliances
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32. Enter information into
database
 The database has two tables
 Firms
 Relationships
Visualization semantics can also be stored in
the database
32

33. Semantics for firms
 Size
 Currently we use Access Light Magenta
revenue Advertiser Green
 Shape Content Lavender
 Platform players are Hardware Tan
denoted as diamonds,
Default Red
rest as circles
 Color
Operator Melon
Services Blue
 Based on stack layer
Investors Yellow
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34. Semantics for links
 Thickness
 Based on repeat links
 Color
 Based on relationship type
 Technical
 Marketing
 Financial
 Strategic
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35. Create input file for
visualization
 We use Pajek for the visualizations
 A sample Pajek input file has two parts
 Vertices
 A vertex record contains vertex number, name,
color, shape, size and the time it appears on
the visualization.
 Edges
 An edge record contains from, to, thickness,
color and the time it appears in the
visualization
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36. Visualize
 Pajek
 Netdraw
 Many eyes
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37. Pajek measures
 Net/Partitions/Degree, while other two
centralities can be found in
Net/Vector/Centrality
 Network reduction
 Net/Transform/Reduction/Degree
 Core players
 Net/Partitions/Core/Degree
 Operations/Extract Network/Partitions
 Draw-partition
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38. What to track?
• News feed on core players
• Positional measures
• Network effects
• Exclusive links
• Link type (marketing, technical,
licensing..)
• Experiments in emerging markets
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39. What determines success?
 Capability of the firm
 Structure of the network
 Action of partners
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40. Ecosystem risks [Ron Adner]
 Co-innovation risk: Seeing the Real
Odds When You Don’t Innovate Alone
 Adoption Chain Risk: Seeing All th e
Customers Before Your End Customer
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41. Data sources
 Website
 Lexis/Nexis
 Sort search results by subject (alliances and
partnerships)
 SDC Platinum
 Programmableweb.com
 http://www.programmableweb.com/neoapi.xml
 http://www.programmableweb.com/neomatrix.xml
 Appdata
 www.compete.com ; www.socialmention.com;
www.google.com/trends
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42. Future directions
 Decision Environments
 automate data collection
 Visualize
 collaborate
 Shared repositories
 Strong theory and cases
 Better understanding of ecosystem risks
 API-based networks
 Communities to share findings 42

43. Decision Environment
Alliance data Discuss visuals
API
Query Ontologies
Firm data
Query
API processor
Visualization engine
Business
Context
Resource Biz Network
generator
manager Visualization
Staging
External monitor
Board member
data
System settings
API Blogs, (user preference,
Social bookmarks etc.)
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44. 44

45. Platform
 A business platform is a set of capabilities used by multiple
parties that
 A product or service should perform at least one essential
function within what can be described as a ―system of use‖
or solve an essential technological problem within an
industry, and
 It should be easy to connect to or build upon to expand the
system of use as well as to allow new and even unintended
end-uses [Platform Leaders by Gawer and Cusumano, MIT
Sloan Management Review, Winter 2008]
 Has ―options‖ value
 Creates Network Effects
 Has explicit Architectural Control Points
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