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Orchestrating Ecosystem Transformation with Data-Driven Network Visualizations
- 1. Orchestra)ng Ecosystem Transforma)ons with Data-‐driven Network Visualiza)ons Martha G Russell h-p://mediax.stanford.edu Neil Rubens, Jukka Huhtamäki, Kaisa SBll, Camilla Yu, Rahul Basole
- 12. Ecosystem
- 13. InnovaBon Ecosystems Innova)on Ecosystems refer to the inter-‐organizaBonal, poliBcal, economic, environmental, and technological systems through which a milieu conducive to business growth is catalyzed, sustained, and supported. Heterogeneous and conBnuously evolving set of firms that are interconnected through a complex, global network of relaBonships. [Basole et al., 2012 A dynamic innovaBon ecosystem is characterized by a conBnual realignment of synergisBc relaBonships that promote growth of the system. In agile responsiveness to changing internal and external forces, knowledge, capital and other vital resources flow through these relaBonships. Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
- 14. InnovaBon Ecosystems Approach • Networked systems perspecBve to examine why, when, and how interfirm networks and alliances form and change (Gula) et al., 2000) • Co-‐crea)on creates value (Ramaswamy & Guillart, 2004) • Value creaBon requires orchestra)on among firms across segments (Basole & Karla, 2012; Dhanaraj & Parkhe, 2006) • Responsiveness to changing internal and external forces (Rubens et al., 2011) • Shared Vision guides and accelerates transformaBon (Russell et al., 2011)
- 15. Distance Old New
- 16. The Way We USED to Think About Organizations New OrganizaBonal Chart Based on RelaBonships Relationship-Focused Co-Creation Infrastructure Stakeholder Infrastructure = RelaBonships (Companies are interlocked through key people – informa8on flow, norms, mental models.(Davis,1996)
- 17. New Data & New Tools Accessing Data Streams about Innovation Building a Dataset on Innovation Crystallisation Through Visualisation The Card-‐Mackinlay-‐Shneiderman visualisaBon reference model:(Card et al., 1999; Miksch, 2005) !"#$%&'()*+% ,"-)*% ./0*1)2*3'1$% 4"-"/*3'1% 51*+6$)$% ,)1)10% 7)$8*+)2*3'1% 5(39"% :1;"++)0"1("% </)9*;"% =*;*% !"##$%&'()*+,'-% Accuracy of Wikipedia (Giles, 2005)
- 18. STEP 1 Boundary SpecificaBon STEP 2 Metrics IdenBficaBon STEP 3 ComputaBon, Analysis & VisualizaBon STEP 4 Sense Making & Storytelling DATA determines feeds Deals & Alliances Execu)ves & Funding Public Opinion & Discourse IEN NL SDC Iden8fy, extract, and curate InnovaBon Ecosystem Network Data-‐driven VisualizaBons Basole, Russell, Huhtamäki, SBll and Rubens, “Understanding Mobile Ecosystem Dynamics: A Data-‐Driven Approach,” Submi-ed to JIT March 2013.
- 19. The new maps may be based on the connecBons through relaBonships -‐ rather than on distance. § Ecosystem PerspecBve § RelaBonship based § Links form networks § Resource flows = knowledge, capital, talent § Network orchestraBon
- 21. Norwegian Tech-‐based Companies Their Branch Offices and Their Financial Orgs Links show relaBonships Example view to IEN dataset in Gephi. Companies are selected with keyword search “Norway + Norwegian;” the funding organizaBons associated with those companies are added Nodes represent companies and their investors; edges indicate resource flows. The network layout is created with Yifan Hu MulBlevel algorithm and nodes are inflated according to their indegree, i.e. the number of the connected investors.
- 22. Advisors & Angels Expand Access Investors leverage co-‐creaBon opportuniBes with investments in mulBple companies. Intl companies not shown. Companies leverage value co-‐creaBon opportuniBes through relaBonships with mulBple investors. Some investors are internaBonal. Timeline analysis of investment events reveals pa-erns of co-‐investment – an indicaBon of intenBon to co-‐create value and, perhaps, sBmulus programs. IEN Dataset, July 2010
- 23. Sørlandet is world leading in offshore oil drilling technology Torger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011
- 24. Shipping brokers Effective ports and terminals Advanced ship equip- ment Maritime R&D Specialized ship yards Maritime education Ship design Maritime IT Ship management Marine insurance Shipping finance Advanced fisheries Environmental standards MariBme policies Offshore oil and gas industry LogisBcs systems MariBme lawyers Ship classificaBon services SHIPPING Mari)me: From ship tonnage to mari)me technology and finance Torger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011
- 25. SHIPPING Torger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011 Mari)me: From ship tonnage to mari)me technology and finance
- 26. Insights About Norway • Dual offices: regional and Oslo • In sectors we studied – Business locaBons parallel technical university programs – Investor relaBonships have strong local links • Some invesBng organizaBons are governmental programs • Expands to Oslo when offices are in Oslo • InternaBonal relaBonships linked to small set of personal relaBonships at execuBve level – InternaBonal investors drawn through execuBve relaBonships • RelaBonships through execs at Google and AOL provide channels for global network expansion
- 28. CapDigital -‐ Regional Sector Catalyst Vision To catalyze the new digital infrastructure in France with global connecBons To create an ecosystem to facilitate the relaBonship between France and global market Enable Paris to become global region of the market for digital services How do you spend money locally to enhance global parBcipaBon in a way that returns the benefit back home? • CapDigital members: – Small startups – Large companies – Support programs 28
- 29. Parisian InnovaBon Ecosystem CapDigital – France -‐ Global Pale Red: French company Dark Red: CapDigital member Light Green: Foreign Venture/ firm Dark Green: French venture firm Blue: Foreign company From IEN Dataset 2010 Selected Paris companies Linked people & venture/financing enBBes Linked companies, people & v/f enBBes 1 degree 2 degree 29 Preliminary and proprietary for CapDigital Permission required for sharing © 2010 InnovaBon Ecosystems Network InnovaBon Ecosystems Network
- 30. Network Graph Parisian InnovaBon Ecosystem Pale Red: French company Dark Red: CapDigital member Light Green: Foreign Venture/ firm Dark Green: French venture firm Blue: Foreign company From IEN Dataset 2010 Selected Paris companies Linked people & venture/financing enBBes Linked companies, people & v/f enBBes 1 degree 2 degree 30 Preliminary and proprietary for CapDigital Permission required for sharing © 2010 InnovaBon Ecosystems Network InnovaBon Ecosystems Network
- 31. CapDigital Program OpportuniBes Pale Red: French company Dark Red: CapDigital member Light Green: Foreign Venture/ firm Dark Green: French venture firm Blue: Foreign company Zone 2: VC Community Zone 3: New CapDigital Members Zone 4 of Parisian Two-‐Level InnovaBon Ecosystem From IEN Dataset 2010 Selected Paris companies Linked people & venture/financing enBBes Linked companies, people & v/f enBBes 1 degree 2 degree
- 33. Analysis of EIT ICT Labs: Trento included as the sixth node, more ciBes connected to colocaBon centers, updated data and transformaBon in place SBll, Huhtamäki, Russell, Rubens (2012). Transforming Innova8on Ecosystems Through Network Orchestra8on: Case EIT ICT Labs
- 34. Adding San Francisco Bay Area as “the seventh EIT ICT Labs node” for contrast, interconnecBons, comparison and benchmark SBll, Huhtamäki, Russell, Rubens (2012). Transforming Innova8on Ecosystems Through Network Orchestra8on: Case EIT ICT Labs
- 36. AusBn Ecosystem Enterprises, Deals & Alliances 678 companies and 498 interconnecBons
- 37. Aus)n’s Young Growth Companies Primary Office in Aus)n Investors AusBn Ventures Silverton Partners DFJ Mercury Ba-ery-‐ Ventures Floodgate S3 Ventures New Enterprise Associates Rho Capital Ventures Texas Emerging Technology Fund Interwest Partners Companies = 801 Investors – 286 Leadership Indiv’s = 1355 Key Individuals Joshua-‐ Baer Kevin-‐ Cunningham John Hime Kip Mcclanahan Bill Boebel Larry Warnock Robert Reeves Jason Calacanis Mike Maples Ron Conway Companies DaBcal Homeaway Sailpoint-‐technologies Mass-‐relevance Digby B-‐side Bazaarvoice Wp-‐engine Convio-‐2 Gowalla Actacell IEN Dataset, March, 2013
- 38. Aus)n’s Young Growth Companies Primary Office in Aus)n Zone 3 Zone 1 Zone 2
- 39. Zone 1: Multiple Connections & Investors Datical ATI company. Datical received seed fund of $660K in July last year. It has diversified investors, including Austin Ventures. Funding TOTAL $660K Seed, 7/12 AusBn Ventures Mohr Davidow Ventures Mercury Fund John Hime Robert Reeves Daniel Nelson Leadership Daniel Nelson CEO, Cofounder Pete Pickerill VP Products, Cofounder Robert Reeves CTO, Cofounder
- 40. John Hime is on the board of both Affinegy (ATI alumus company) and Datical (current ATI company). Datical has had several venture investments; Affinegy has had none yet. Relationship may create bridge to investment. ZONE 1: Emerging Network
- 41. Ordoro - ATI Alumus A current ATI company is connected with an ATI alumus company through Central Texas Angel Network. Contribution of ATI, such as introducing the companies XERIS develops patient- friendly injectables based on its XeriJect™ and XeriSol™ formulation and delivery platforms. Ordoro provides a web app for small and medium-sized e-commerce retailers to manage their orders, inventory and suppliers. XERIS - Current ATIZONE 2: Potential Network
- 42. Inxero, a current ATI company ZONE 3: Need Network
- 43. Aus)n’s Young Growth Companies Primary Office in Aus)n Zone 3 Zone 1 Zone 2
- 45. AusBn Start-‐ups, Founders and Angels IEN Dataset, March, 2013 692 companies 3500+ investors $79K average investment
- 46. Colleges/Universi)es = 200+ University of Texas, AusBn Stanford University Massachuse-s InsBtute of Technology University of California, Berkeley Georgia InsBtute of Technology University of Michigan Harvard University Purdue University University of Colorado, Boulder University of Chicago Loca)ons = 200+ San Francisco New York, NY Los Angeles Houston Palo Alto Dallas London Boston Chicago Silicon Valley Sectors/Markets = 374 Social media Mobile SaaS e-‐commerce Digital media Social commerce Consumer internet Marketplaces Small and medium businesses EducaBon Companies = 2000+ Latakoo Bazaarvoice Infochimps SalesVu OwnLocal Predictable Data Mass Relevance ForcasBx SQMOS Vivogig Network of AusBn Start-‐ups, Founders & Angels IEN Dataset, March, 2013
- 47. Actors & Events Impact over time Coalitions Shared Vision TransformaBon Measure & Track Interact & Feedback Co-Create Value Measuring Impact of Transformative Coalitions in Innovation Ecosystems Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
- 50. THE GOAL OF CREATIVE COLLABORATIONS IS TO DO SOMETHING TOGETHER THAT NEITHER COULD DO INDEPENDENTLY PATE IN IC2 IITIATIVE JOIN THE EFFORT AND HELP US MAP IT IC2-‐IEN WORKING SESSION – JUNE 3-‐4, 2013 FUTUR EST EN SEINE, PARIS, JUNE 12-‐13, 2013 INTERNATIONAL SOCIETY OF PROFESSIONAL INNOVATION MANAGERS – JUNE 16-‐19, HELSINKI
- 53. 53 Skill of the Future = Network OrchestraBon
- 55. • Thank you • Martha Russell • InnovaBon-‐ecosystems.org • marthar@stanford.edu