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Presentation at board DKV Seguros

  1. Innovacion abierta: endavant i seny! David Osimo, Laia Pujol Open Evidence 1-12-2014 - DKV
  2. Startup y grandes empresas: dos mundos incompatibles? Kodak Instagram Created in 1888 Created in 2010 Top value: 30B $ Top value: 1B $ Top employees: Top employees: 145.000 18 Today bankrupt Today part of Facebook
  3. Trend 1: sharing economy Source: the economist
  4. Los prosumers El usuario final como provededor de: • storage & server capacity (P2P), • connectivity (wifi sharing, mesh networks), content (youtube), taste/emotion (Amazon), contacts (Linkedin), relevance (Google Pagerank), reputation & feedback (Tripadvisor), – goods (eBay), – Funding (kickstarter) – Habitaciones (AIRbnb) – Taxi (Uber) » Anything else...
  5. Llegando a todos los sectores Source:
  6. Servicios que mejoran cuanta mas gente los utilizes 6 “Hands-on care by health professionals can't scale. One-on-one advice from professional intermediaries, like librarians, can't scale. Networked peer support, research, and advice can scale. In other words: Altruism scales.” Susannah Fox ! "#$%&' ( ) *( +, -. %/, ( 0 "1 2, -() *(" +, -+( 5) /%#$( 3%4%&#$( 67#$) 4(
  7. Generando dinero
  8. Consumo collaborativo
  9. Trend 2: big data • More data • More granular, specific data • Real time data • From different datasets • “At its core, big data is about predictions”
  10. Growth of the Digital Universe from 2013 to 2020 4.4 ZB 44 ZB Data from embedded systems (IoT) © IDC Visit us at and follow us on Twitter: @IDC 10 Data on the cloud 20% Data on the cloud 40% 22% 37% Share of useful data on total 2% 10% 2013 2020 Source: IDC for EMC 2014
  11. Examples of data: Big Data Market grows 6 times faster than the traditional IT market Big Data Technologies and Services Market, worldwide © IDC Visit us at and follow us on Twitter: @IDC 11 € Bn 7.2 23.7 2012 2017 Source: IDC 2014 2.3 4.3 2.7 2013 - € Bn hardware software services
  12. Vertical Market Big Data Heatmap Western Europe Volume Variety Velocity Value Intensity of Big Data Drivers Finance Process Manufacturing Discrete Manufacturing Retail/Wholesale Telecom/Media Utilities/Oil & Gas Prof. Services/Transport Government/Education Healthcare Total Hot High Medium Low Based on mean scores assigned by survey respondents
  13. El mercado de datos VC research training incubators other services regulators Data market Data landscape Data holders Gov, Personal, Scientific, Business, Sensor data Marketplaces Knoema Quandl Dandelion Europeana ICT enablers: Radoop Talend Sensaris Analytics Teralytics ; SAS Captain Dash Datasift ; Spaziodati RapidMiner Vertical apps Exelate Kreditech Mendeley Doctoralia Data Users Gov Industry Civil society Enabling players Cross infrastructure Amazon MS-Azure SAP Google IBM
  14. Predecir peliculas More data beat better algorythm
  15. Predecir crimenes
  16. Hasta predecir las hospitalisaciones
  17. Data science as a service
  18. Llegan los “datavores” • “Firms using data-driven decisionmaking have 5- 6% higher productivity” (Brynolfsson et al 2012) • “Datavores are 25 per cent more likely to say they launch products and services before competitors” Nesta 2013 • But “The coolest thing to do with your data will be thought of by someone else” – Rufus Pollock
  19. Trend 3: social computing
  20. A different idea of technology • Traditionally, computing is about automation: technology substitutes humans, humans should adapt • Social computing is about augmentation: technology adapts to and augments human capacity (Engelbart 1962) 20
  21. Social Machines 21 “The brilliance of social-software applications like Flickr, Delicious, and Technorati is that they […] devote computing resources in ways that basically enhance communication, collaboration, and thinking rather than trying to substitute for them.",258,p1.html
  22. Enterprise 2.0: accessing micro-expertise 22
  23. Traditional Enterprise apps Enterprise 2.0 Mission Enable pre-defined groups/teams working closely together and/or relatively formal collaborative relationships. Enable individuals to act in loose, ad-hoc collaborations with a potentially very large number of others. Relationship to organisational hierarchy Tools reflect the organizational hierarch and roles within them. Little link to organizational hierarchy Control of structure Centrally imposed and generally rigid controls Emergent (=emerges and evolves) Content originated by Specialists with authorisation All users - also emergent Control over users Users/participants are fixed and their roles pre-defined. Roles by choice and can evolve over time (emergent) Control mechanisms Formal, rules Norms, examples Change of content timescales Slow Rapid Delivery model Typically on premise commercially licensed software Range of delivery models including on premise, cloud, commercial, open source, stand-alone, suites or add-ins to E1.0 systems Range of participants Colleagues with similar or complementary job roles Anyone in the organization and potentially outside (e.g. customers) Links between participants Peer or hierarchical Links can be strong to non-existent (or 'potential') within the group Typical tools Knowledge management, knowledge repositories, decision automation Blogs, wikis, social networking, prediction markets Communication patterns One-to-one Many-to-many
  24. Effects of enterprise 2.0 • Black and Lynch estimate that changes in organizational capital may have accounted for approximately 30 percent of output growth in the manufacturing sector. • Gant, Ichiniowski and Shaw find robust evidence of positive impact of connective capital – defined as workers’ access to the knowledge and skills of other workers-on productivity (relevance for E2.0). 24
  25. Porque abrirse? Source: Open Evidence / UNDP
  26. Thematic knowledge: peer to patent Decision rests with gov (USPTO)
  27. Geographic coverage
  28. User experience
  29. IT skills
  30. Many eyes and many hands
  31. Networks and contacts
  32. Trends que se refuerzan mutuamente Big data Social computing Sharing economy
  33. Una nueva manera de innovar
  34. Grandes empresas crecen • internal ecosystems for accelerated innovations, • Enterprise 2.0 platforms • incubator/accelerator programs, • seed-funds, • cross-disciplinary networks, • ‘beyond the pill’ business models • Intrapreneurship • coworking • BBVA, Bohringer, Deutsche Telekom, BBC, Johnson & Johnson, Telefonica, Philips... Fuentes:
  35. Pero no es abertura total y indiscriminada! Fuente: autumn-2010-talks/
  36. Ejemplo: PeerToPatent La Decision queda en el gobierno (USPTO)
  37. Como abrirse Source: Open Evidence / UNDP
  38. No importa cuantos, importa quien Ignoran Leen Comentan 1000 100 10 1 Datos abiertos
  39. 1 reutilizador puede ser suficiente Source:
  40. “Why investing on it until we don’t have clear ROI?”
  41. “Why investing on it until we don’t have clear ROI?” Kodak CEO, 2005
  42. Lo que se necesita Experiencia para decidir cuando y como abrirse Instrumentos de implementacion de alta calidad, usabilidad y design Metodos robustos para evaluar input, output y impacto
  43. Gracias @osimod

Editor's Notes

  1. The available storage capacity will decrease from 33% of the digital universe to only 15% Connected things from 20 to 30 billions from 7 to 15% of connectable things