THE ECONOMICS OF DATA-DRIVEN INNOVATION
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1 New Engines of Growth Driving Innovation and Trade in Data High-Level Transatlantic Summit 24 April 2014 THE ECONOMICS OF DATA-DRIVEN INNOVATION Opportunities and challenges for Europe
2 OECD - Organisation for Economic Co-operation and Development Like-minded Economic and social development Policy standard setter Inter-governmental Multi-stakeholder Inter-governmental organisation with 34 members Australia Austria Belgium Canada Czech Republic Chile Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States + European Union 2
3 Structure* 1. Why do we care about data-driven innovation? 2. What is the current evidence-base? 3. What are the key policy opportunities for europe? *Disclaimer: This presentation reflects my expert views and should not be reported as representing the official views of the OECD or of its member countries. 3
4 DATA DRIVEN INNOVATION: A NEW SOURCE OF GROWTH?
5 Data: inexhaustible source for growth across the economy Public Administration Health Retail Agriculture Transportation Science and Education 5
6 Thousands Internet firms pioneers of data-driven innovation Average revenue per employee of top 250 ICT firms in thousand USD Internet Software Telecommunications Semiconductors Communications equipment IT equipment Electronics & components IT services Source: OECD Internet Economy Outlook
7 More data is being generated through the Internet of Things (M2M) Supply Chain Management Work In Process Tracking Security & Access Control Environmental Applications Asset Management Consumer Applications 7
8 Data-driven innovation in 21 st century manufacturing 8
9 EVIDENCE BASE ON DATA-DRIVEN INNOVATION STILL POOR BUT IMPROVING!
10 Investment (% of adjusted GDP) 18% Improvements in measuring knowledge-based capital Business investment in intangible (knowledgebased) and tangible assets in the United States, as % of GDP 16% Investment in KBC 14% 12% 10% Investment in tangibles 8% 6% 4% Source: Corrado, Hulten, and Sichel (2012) 10
11 including investments in databases and software Investment in physical and knowledge-based capital, 2010 (As a percentage of value added of the business sector) % 35 Non-residential physical assets R&D and other intellectual property products Software and databases Brand equity, firm-specific human capital, organisational capital Source: OECD Science, Technology and Industry Scoreboard
12 30 Effects of complementary investments still need further study Knowledge-based capital related workers, 2012 (as a percentage of total employed persons) Organisational Capital Computerised Information Design Research & Development Overlapping assets TUR SVK ITA PRT HUN ESP GRC DNK POL CZE LUX AUT IRL FIN SVN EST BEL NLD SWE DEU FRA NOR ISL GBR USA Source: OECD Science, Technology and Industry Scoreboard
13 What we know about the fundamental economic properties of data Data is a non-rivalrous good: Data re-use and non-discriminatory access to data can maximize data-driven value creation Data enables multi-sided markets Data has no intrinsic value: Value depends on the capacity to extract information and to gain knowledge out of it Data can create super-additive insights: Data linkage is a key source for the opportunities and challenges of data-driven innovation 13
14 Data-driven innovation is not only about data, but about the data value chain! Analyt ics Map reduc e Storag e 101 Datafication, data collection and linkage Data analytics (software and skills) Data-driven decision Big data Knowledge Base Value added growth and well-being Source: OECD 14
15 A holistic view is needed to leverage data-driven innovation Analyt ics Map reduc e Storag e 101 Datafication, data collection and linkage Increasing the pool of re-useable data Open data Data portability Data analytics (software and skills) Enhancing data analytic capacities Cloud infrastructure Data scientist skills Data-driven decision Promoting responsible decisionmaking for growth and well being Domain-specific competencies Entrepreneurship Cross-cutting policy issues: Privacy, IPR, competition, tax, trade, Source: OECD 15
16 KEY POLICY OPPORTUNITIES
17 1. Promoting trust in data-driven innovation 17
18 2. Getting data governance frameworks right Data silos preventing the re-use of data even within organizational boarders; Potential opportunity costs through unrealised positive externalities (spill-over effects) of data; Better data sharing platforms and common standards could be needed; Privacy as well as IPR concerns may better be addressed in a more differentiated manner; 18
19 3. Addressing skills shortages and mismatch Information Technology Marketing, Sales and Service Health Science Finance Business, Management and Administration Science, Technology, Engineering and Architecture and Construction Hospitality and Tourism Law, Public Safety, Corrections and Security Transportation, Distribution and Logistics Human Service Education and Training Agriculture, Food and Natural Resources Manufacturing Government and Public Administration Arts, Audio/Video Technology and Bright outlook occupations (% total) the sexy job in the next 10 years will be statisticians. (nytimes.com) 0% 10% 20% 30% 40% 50% 60% 70% Source: OECD based on O*NET, US occupations 19
20 Only a small percentage with a sufficient level of basic e-skills Australia Austria Canada Czech Republic Denmark England/N. Ireland (UK) Estonia Finland Flanders (Belgium) Germany Ireland Japan Korea Netherlands Norway Poland Slovak Republic Sweden United States 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Opted out of the computer based assessment Missing Failed ICT core or had no computer experience Below Level 1 Level 1 Level 2 Level 3 Source: OECD PIAAC Top skills ~ 6% 20
21 Distribution of data scientists jobs* across the economy in the US and EU Information, Public 6% administration, 7% Wholesale and retail trade, 5% Manufacturing, 11% Educational and health services, 11% United States, 2013 EU, 2013 Others, 5% Financial activities, 12% Professional and business services, 43% Wholesale and retail trade, 6% Financial and insurance activities, 7% Transportation and storage, 2% Information and communication, 9% Manufacturing industry, 12% Others, 7% Public administration, defence, and sociale services, 15% Professional, scientific and technical activities, 43% * Based on preliminary working definition of data scientists ; ICT services included in Professional *. Source: OECD based on US CPS (March Supplement 2013) and EU LFS 21
22 Thank you for your attention! Report Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by Big Data : OECD project site on data-driven innovation: Christian.Reimsbach-Kounatze@oecd.org 22
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