Innovation in Future Enterprise, by David Osimo
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Transcript of Innovation in Future Enterprise, by David Osimo
Innovation in Future
Enterprise
David OsimoOpen Evidence
Innovation – as it used to be
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
Trend 1: sharing
economy
Source: the economist
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...
Reaching all sectors
Source: http://blog-en.mila.com/2014/09/30/sharing-economy-in-europe/
Leveraging unexploited assets
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/
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
© 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
Vertical Market Big Data HeatmapWestern Europe
Volume Variety Velocity ValueIntensity 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
The EU data market
Data landscape
Data market
Data holders
Gov, Personal, Scientific, Business,Sensor data
MarketplacesKnoema Quandl
DandelionEuropeana
ICT enablers: Radoop Talend Sensaris
AnalyticsTeralytics ; SAS Captain
DashDatasift ; Spaziodati
RapidMiner
Vertical appsExelate
KreditechMendeleyDoctoralia
Data Users
GovIndustryCivil society
Enabling players
Cross infrastructureAmazon MS-Azure SAP Google IBM
VC research training incubators regulatorsother services
Predicting crimes
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
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
Data science as a service
Trend 3: social computing
Enterprise 2.0: accessing micro-
expertise
18 innocentive.com
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
Mutually reinforcing trends for open innovation
Big data
Social computing
Sharing economy
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
What is needed
Capacity to design inclusive and
effecitve innovation processes
Skills to implementusable platforms
and processes
Smart metrics to monitor and
evaluate processes
Gracias
• www.open-evidence.com
• @osimod
Backup
Predicting hospital admissions
Predicting movies
More data beat better algorythm
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
High profitability
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
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
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
Why opening up?
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
No importa cuantos, importa quien
Ignoran
Leen
Comentan
1
10
100
1000
Datos abiertos
1 reutilizador puede ser suficiente
Source: www.bbc.co.uk/news/magazine-22223190
But its not about total openness
Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux-people-autumn-2010-talks/
How to open up
Source: Open Evidence / UNDP