Ecosystem visualization methodology

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Ecosystem visualization methodology

  1. 1. Visualizing EcosystemsProf. Bala IyerBabson CollegeTwitter: @balaiyer6/18/12
  2. 2. Agenda Background Examples Methodology Lessons Future directions 2
  3. 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. 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] 4 4
  5. 5. Example 1 5
  6. 6. M-payments Wireless Innovation Council (Dupont, LOréal, lexis-nexis, Marriott,..) Industry stack Companies involved Relationships or dependencies Industry analysis Future trends 6
  7. 7. M-payment Stack Platform provider Network Operators Banks Credit Card Issuers Device Manufacturers Merchants Users 7
  8. 8. 8 8
  9. 9. Platforms 9 9
  10. 10. Method used for Ecosystemgeneration 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. 11. Ecosystem-- complementors-- platforms-- partnerships 11 11
  12. 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. 13. M-payment platform ConsumersComplementors• Credit card co,operators, banks, device Transactionsmfc., merchants… Platform Ad servers Bid for ads Support development Developers Advertisers • Fortune 500 firms, individuals 13
  14. 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. 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. 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 16 16
  17. 17. Example #2 17
  18. 18. Cloud computing Society for Information Management- APC sponsored study Vendors involved in cloud computing Companies using cloud technology Key benefits Lessons 18
  19. 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 19
  20. 20. Cloud Ecosystem (partnerships)Applications Collaboration Services 20 Infrastructure Platform
  21. 21. Core players 21
  22. 22. Seven Cloud ComputingCapabilities Controlled interface Location independence Sourcing independence Virtual business environment Ubiquitous access Addressability and traceability Rapid elasticity 22 22
  23. 23. Other Examples 23
  24. 24. January 2007Mashups 24
  25. 25. IT Outsourcing Contracts (IDC data) 2009 25
  26. 26. IT Vendors Alliances 26
  27. 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. 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. 29. StacksVideo Games SmartOSContent providers Network operatorSoftware developers Handset manufacturerSoftware publishers Mobile OS providerPlatform provider Content providers and aggregatorsRetailer Application developersConsumer 29
  30. 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? 30
  31. 31. Get alliance information andattributes  Sources  Company websites  News feeds  Inputs  Alliance type (technical, marketing, strategic or financial)  Single or multiple alliances 31
  32. 32. Enter information intodatabase The database has two tables  Firms  Relationships Visualization semantics can also be stored in the database 32
  33. 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 33
  34. 34. Semantics for links Thickness  Based on repeat links Color  Based on relationship type  Technical  Marketing  Financial  Strategic 34
  35. 35. Create input file forvisualization  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 35
  36. 36. Visualize Pajek Netdraw Many eyes 36
  37. 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 37
  38. 38. What to track?• News feed on core players• Positional measures• Network effects• Exclusive links• Link type (marketing, technical, licensing..)• Experiments in emerging markets 38
  39. 39. What determines success? Capability of the firm Structure of the network Action of partners 39
  40. 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 40
  41. 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 41
  42. 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. 43. Decision EnvironmentAlliance data Discuss visuals API Query OntologiesFirm data Query API processor Visualization engine Business Context Resource Biz Network generator manager Visualization Staging External monitorBoard memberdata System settings API Blogs, (user preference, Social bookmarks etc.) 43 43
  44. 44. 44
  45. 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 45 45

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