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

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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

• dosimo@open-evidence.com

• 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

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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