1) Data-driven innovation refers to using data and analytics to improve or create new products, processes, organizations and markets. It represents the "new R&D" for 21st century innovation systems across many sectors like health, education and transportation.
2) While data is not a traditional commodity, it functions as a non-rivalrous and general purpose input that is an infrastructure for innovation with increasing returns. Its value depends on access and the ability to extract useful insights.
3) Key challenges include encouraging adoption of data-driven technologies especially among small and medium enterprises, addressing shortages of data specialist skills, and balancing open data and reuse with privacy, security and intellectual property concerns.
1. DATA-DRIVEN INNOVATION
(DDI) for Growth and Well-Being
Find out more about our work at:
http://oe.cd/bigdata
High-Level Roundtable on Data-Driven Innovation:
Productivity, Jobs, Growth and Wellbeing in the Age of Big Data
Lisbon Council – 6 October 2015
2. Key drivers of 21st century economies
GVCs
KBC
Digital
Economy
DDI
2
3. DDI refers to the use of data and analytics
to improve or foster new products,
processes, organisational methods and
markets
3
What is data-driven innovation (DDI)?
4. Data is the “new R&D” for 21st century
innovation systems
Health
Public
Administration
Retail
TransportationAgriculture
Science and
Education 4
5. 5
Data is not oil, but an infrastructure
• Data is non-rivalrous (but excludable)
Data re-use and non-discriminatory access can maximize its value
Data enables multi-sided markets
• Data is a capital with increasing returns
Data can be re-used as input for further production
Data linkage is a key source for super-additive insights
• Data is a general purpose input with no intrinsic value
Data are an input for multiple purposes
Its value depends on complementary factors related to
the capacity to extract information (e.g. skills, software)
6. 0
5
10
15
20
25
30
35
%
Internet of things Big data Quantum computing and telecommunication 2005-07
49
42 50 42
Economies’ share of IP5 patent families filed at USPTO and EPO,
selected ICT technologies (2005-07 and 2010-2012)
Source: OECD Science, Technology and Industry Scoreboard 2015 (forthcoming).
Top players in key technologies could
benefit from first mover advantages
6
7. R² = 0.9758
0
50
100
150
200
250
300
350
400
450
0 200 400 600 800 1 000 1 200 1 400
Number of top sites hosted
Thousands
Number of co-location data centres
USA
GBR
DEU
FRA
R² = 0.9758
0
50
100
150
200
250
300
350
400
450
0 200 400 600 800 1 000 1 200 1 400
Number of top sites hosted
Thousands
Number of co-location data centres
USA
GBR
DEU
FRA
DEU
CHN GBRFRA
JPN NLD
CAN
AUS IND
R² = 0.5737
0
10
20
30
40
50
60
70
80
0 50 100 150 200
Magnified
RUS
7
The emerging global data ecosystem
Top locations by number of colocation data centres and top sites hosted
Source: OECD (2015), Data-driven Innovation: Big Data for Growth and Well-Being, OECD Publishing
8. Encouraging adoption of DDI and
related technologies in businesses …
The diffusion of selected ICT tools and activities in enterprises, 2014
Percentage of enterprises with ten or more persons employed
Source: Based on OECD Science, Technology and Industry Scoreboard 2015 (forthcoming),
OECD, ICT Database; Eurostat, Information Society Statistics and national sources, July 2014. 8
MEX
MEX
TUR JPN
ISL
POL MEX HUN
CAN
FIN
FIN
NZL
ISL
BEL
FIN NZL
KOR
KOR
0
20
40
60
80
100
Broadband Website E-purchases Social media ERP Cloud
computing
E-sales Supply chain
mngt. (ADE)
RFID
%
Gap 1st and 3rd quartiles Average Lowest Highest
9. … with a focus on small and medium
enterprises
0
10
20
30
40
50
60
70
% All enterprises 10-49 50-249 250+
Source: OECD, ICT Database; Eurostat, Information Society Statistics and national sources, January 2015.
http://dx.doi.org/10.1787/888933224863
Use of cloud computing as a percentage of enterprises in each
employment size class, 2014
9
10. 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2013 2012 2011
%
10
The lack of data specialists is also a
missed opportunity for job creation
Data specialists as a share of total employment in selected OECD countries
Source: OECD based on data from Eurostat, Statistics Canada, Australian Bureau of Statistics Labour Force
Surveys and US Current Population Survey March Supplement, February 2015.
11. 11
Tackle skills shortages and mismatch
90
100
110
120
130
140
150
160
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Data specialists Mathematicians, actuaries and statisticians
Database and network administrator ICT specialists
95
100
105
110
115
120
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Data specialists Mathematicians, actuaries and statisticians
Database and network administrator ICT specialists
Trends in the share of data specialists in the United States
Trends in relative average wage of data specialists in the United States
Source: Bureau of Labor Statistics, Occupational Employment Statistics (OES), November 2014.
12. Prevent loss
of profit
data portability
Striking the right balance between
“openness” and “closeness”
12
Close(ness) Openness
individuals
organisations
Prevent
social and
economic
harm
Enable
spill-over
effects
multi-purpose reuse
data sharing
open standards
open APIs
privacy
confidentiality
user lock-in
walled garden
open data
free flow of data
digital security
IPRs (e.g. trade secrets) Algorithmic transparency
13. 1. Recognise that infrastructure in the digital economy includes
not only broadband networks, but also data
2. Encourage investments in data, data sharing and reuse, and
reduce barriers to data flows that could disrupt GVCs
3. Balance between the benefits of openness and legitimate
concerns over privacy and intellectual property rights
4. Focus on SMEs which face severe barriers to the adoption of
DDI-related technologies
5. Address shortages of data specialist skills, which point to
missed opportunities for job creation
6. Anticipate and address the disruptive force of DDI that could
lead to a new digital data divide
7. Take a whole-of-government strategic approach that
leverages data as the “new R&D” in innovation systems 13
Main policy considerations
14. 14
OECD Horizontal Project on DDI
Find out more about our work at http://oe.cd/bigdata
Contact: Christian.Reimsbach-Kounatze [aatt] oecd.org
• Ch.1 The Phenomenon of data-driven innovation
• Ch.2 Mapping the global data ecosystem and its
points of control
• Ch.3 How data now drive innovation
• Ch.4 Drawing value from data as an infrastructure
• Ch.5 Building trust for data-driven innovation
• Ch.6 Skills and employment for a data-driven
economy
• Ch.7 Promoting data-driven scientific research
• Ch.8 The evolution of health care in a data-rich
environment
• Ch.9 Cities as hubs of data-driven innovation
• Ch.10 Governments leading by example with
public sector data
Hinweis der Redaktion
In addition to the updated IS -- we have studied these 3 forces separately over the last few years, we have not looked systematically at their combination and interaction and the impact on production.
This interaction will be the focus of a new project that we are now embarking on a project on the next production revolution.
We aim to explore the policy issues I’ve highlighted, to help countries enable the next production revolution.
Project launched in 2015 and being accelerated due to growing demand;
The review of firm-level studies suggests that firms’ using data and analytics have raised their productivity growth by +5% to +10% compared to those which have not. NOTE: these productivity gains depend not only on the use of data and analytics, but also on the presence of other factors such as data-related skills, innovative business processes and the sector in which the firm operates.
Use of public sector data in the OECD has generated value across the economy up to USD 509 billion in 2008
Highlight first mover advantages
This graph shows that the US are far advanced, compared to other countries, both in terms of:
Number of top sites hosted (420.000)
Number of data centers (1250)
This slide shows that although most enterprises (in the selected countries) have a BB connection, few are in fact taking advantage of BB to use IT resources (such as Enterprise Resource Planning or the cloud) to conduct their activities. It is important because IT such as ERP, coud, supply chain management and RFID are enablers of DDI.
Except for Switzerland and the Slovak Republic, large enterprises are more likely to use the cloud than the very small ones.
This is an important issue because those which can most benefit from using could computing are the smaller firms.
Note: potential shortage of mathematicians, statisticians and actuaries in the USA starting in 2013.
If confirmed, this could become a barrier to DDI diffusion and a missed opportunity for job creation.
This graph shows the continuum between openness and “closeness” between which policy makers need to strike the right balance.
The infrastructural nature of data, as highlighted by Christian before, suggests that the social and economic value of data is maximised with openness.
In other words, because data is a kind of non-rivalrous, multi-purpose capital, its spill-overs are the highest when we apply what is on the right side of the figure (multi-purpose reuse, open data, and data portability to name a few)
However, from an economic perspective moving towards complete openness would disrupt individuals’ and businesses’ incentives to provide their data in the first place, including for businesses to invest in data, and individuals to provide their personal data.
There is thus a natural tension between openness and closesness that comes with data. For example:
Society’s and in particular businesses’ interest for multi-purpose reuse of personal data <> individuals’’ privacy protection through the purpose specification principle in most privacy regimes.
Consumers’ interest for data portability <> Businesses’ interest in user lock-ins
Society’s interest for algorithmic transparency <> Businesses’ interest for IPRs (trade secrets)
Governments therefore need to balance the spill-over benefits for society with individuals and business concerns for their privacy and intellectual property rights respectively.
Finally I would like to thank your attention and highlighting that this work is based on a collaborative work across divisions and directorates.
And I may have missed to highlight some of the important elements done by my colleagues during the presentation.
Explain the following chart
So here is another/complementary dichonomy to consider next to Michael’s suggest to look at data as a third categories of asset.
Distinguish between investments in KBCs and in physical capital. Why because the economic properties are so fundamentally different. Raising different issues such as for example the issue of TAX where KBC enables BEPS (Base Erosion and Profit shifting) based on the fact that you can easily shift KBC outside of the juridiction of OECD countriy governments. >> OECD is working on it.
We are current assessing Data as part of KBCs
Highlight the countries in which KBC account for a larger share of business value added.
Programme for the International Assessment of Adult Competencies (PIAAC) data across economies reveal that between 7% and 27% of adults have no experience in using computers or lack the most elementary computer skills, such as the ability to use a mouse.