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Innovacion abierta: 
endavant i seny! 
David Osimo, Laia Pujol 
Open Evidence 
1-12-2014 - DKV
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
Trend 1: 
sharing 
economy 
Source: the economist
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...
Llegando a 
todos los 
sectores 
Source: http://blog-en.mila.com/2014/09/30/sharing-economy-in-europe/
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( 
http://egov20.wordpress.com/2011/11/03/collaborative-e-government-public-services-that-get-better-the-more-people-use-them/
Generando dinero
Consumo collaborativo
Trend 2: big data 
• More data 
• More granular, specific data 
• Real time data 
• From different datasets 
• “At its core, big data is about predictions”
Growth of the Digital Universe from 2013 to 2020 
4.4 ZB 44 ZB 
Data from embedded systems (IoT) 
© IDC Visit us at IDC.com 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
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 IDC.com 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
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
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
Predecir 
peliculas 
More data beat 
better algorythm
Predecir crimenes
Hasta predecir las hospitalisaciones
Data science as a service
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
Trend 3: social computing
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
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." 
http://www.technologyreview.com/InfoTech/wtr_14664,258,p1.html
Enterprise 2.0: accessing micro-expertise 
22 innocentive.com
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
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
Porque abrirse? 
Source: Open Evidence / UNDP
Thematic knowledge: peer to patent 
Decision rests with gov 
(USPTO)
Geographic coverage
User experience
IT skills
Many eyes and many hands
Networks and contacts
Trends que se refuerzan mutuamente 
Big data 
Social 
computing 
Sharing 
economy
Una nueva 
manera de 
innovar
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: 
www.intrapreneurshipconference.com/ 
cbinsights.com
Pero no es abertura total y 
indiscriminada! 
Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux-people- 
autumn-2010-talks/
Ejemplo: PeerToPatent 
La Decision queda en el gobierno 
(USPTO)
Como abrirse 
Source: Open Evidence / UNDP
No importa cuantos, importa quien 
Ignoran 
Leen 
Comentan 
1000 
100 
10 
1 
Datos abiertos
1 reutilizador puede ser suficiente 
Source: www.bbc.co.uk/news/magazine-22223190
“Why investing on it until 
we don’t have clear ROI?”
“Why investing on it until 
we don’t have clear ROI?” 
Kodak CEO, 2005
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
Gracias 
dosimo@open-evidence.com 
www.open-evidence.com 
@osimod

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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: http://blog-en.mila.com/2014/09/30/sharing-economy-in-europe/
  • 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( http://egov20.wordpress.com/2011/11/03/collaborative-e-government-public-services-that-get-better-the-more-people-use-them/
  • 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 IDC.com 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 IDC.com 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
  • 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." http://www.technologyreview.com/InfoTech/wtr_14664,258,p1.html
  • 22. Enterprise 2.0: accessing micro-expertise 22 innocentive.com
  • 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)
  • 30. Many eyes and many hands
  • 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: www.intrapreneurshipconference.com/ cbinsights.com
  • 35. Pero no es abertura total y indiscriminada! Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux-people- 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: www.bbc.co.uk/news/magazine-22223190
  • 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

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