Innovation in Future Enterprise, by David Osimo

1. Innovation in Future
Enterprise
David Osimo
Open Evidence

2. Innovation – as it
used to be

3. Kodak Instagram
Created in 1888 Created in 2010
Top value: 30B $ Top value: 1B $
Top employees:
145.000
Top employees:
18
Today bankrupt Today part of
Facebook
New ways to innovate

4. Trend 1:
sharing
economy
Source: the economist

5. Prosumers
User as a provider of
• 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)
– Rooms(AIRbnb)
– Taxi (Uber)
» Anything else...

6. Reaching all
sectors
Source: http://blog-en.mila.com/2014/09/30/sharing-economy-in-europe/

7. Leveraging unexploited assets

8. Services that get better the more
people use them
8
“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
! "#$%&' (
) *(
+, -. %/, (
0"1 2, -() *("+, -+(
3%4%&#$(
5) /%#$(
67#$) 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
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 10
4.4 ZB 44 ZB
Data on the
cloud 20%
Data on the
cloud 40%
22%
37%
Share of useful data
on total
2%
10%
2013 2020
Data from embedded systems (IoT)
Source: IDC for EMC 2014

11. 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

12. The EU data market
Data landscape
Data market
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
VC research training incubators regulatorsother services

13. Predicting crimes

14. Here come the “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

15. Data driven business models
Source: Seven Ways to Profit from Big Data as a Business”, by James Platt, Robert Souza, Enrique Checa and Ravi
Chabaldas; The Boston Consulting Group, March 2014

16. Data science as a service

17. Trend 3: social computing

18. Enterprise 2.0: accessing micro-
expertise
18
innocentive.com

19. 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. This is a very large
number.
• 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).
19

20. Mutually reinforcing trends for open
innovation
Big data
Social computing
Sharing economy

21. Large companies too
• 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

22. What is needed
Capacity to design
inclusive and
effecitve innovation
processes
Skills to implement
usable platforms
and processes
Smart metrics to
monitor and
evaluate processes

23. Gracias
• dosimo@open-evidence.com
• www.open-evidence.com
• @osimod

24. Backup

25. Predicting hospital admissions

26. Predicting
movies
More data beat
better algorythm

27. 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

28. High profitability

29. Examples of data: Big Data Market grows 6
times faster than the traditional IT market
© IDC Visit us at IDC.com and follow us on
Twitter: @IDC
29
7,2
23,7
2012 2017
€ Bn
Big Data Technologies and Services Market, worldwide
Source: IDC 2014
2,3
4,3
2,7
2013 - € Bn
hardware
software
services

30. Social Machines
30
“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

31. 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)
31

32. Why opening up?
Source: Open Evidence / UNDP

33. Thematic knowledge: peer to patent
Decision rests with gov
(USPTO)

34. Geographic coverage

35. User experience

36. IT skills

37. Many eyes and many hands

38. Networks and contacts

39. No importa cuantos, importa quien
Ignoran
Leen
Comentan
1
10
100
1000
Datos abiertos

40. 1 reutilizador puede ser suficiente
Source: www.bbc.co.uk/news/magazine-22223190

41. But its not about total openness
Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux-
people-autumn-2010-talks/

42. How to open up
Source: Open Evidence / UNDP