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Data driven innovation for growth and well being

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Data driven innovation for growth and well being

  1. 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. 2. Key drivers of 21st century economies GVCs KBC Digital Economy DDI 2
  3. 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. 4. Data is the “new R&D” for 21st century innovation systems Health Public Administration Retail TransportationAgriculture Science and Education 4
  5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

Editor's Notes

  • 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.

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