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DATA-DRIVEN INNOVATION 
FOR EDUCATION 
5 November 2014 
CERI CONFERENCE ON 
INNOVATION, GOVERNANCE AND REFORM IN EDUCATION...
1. Why should we care about “big data” or 
data-driven innovation (DDI)? 
2. What is really new about it? 
3. What are the...
1. WHAT IS “BIG DATA”? 
AND WHY SHOULD WE CARE?
A lot of “big data” buzz 
• “Data is the new oil.” Andreas Weigend, Stanford (ex Amazon) 
• “The future belongs to compani...
What is “big data”? And why we should 
rather refer to Data-Driven Innovation? 
• Defining “big data” is challenging: 
– D...
6 
Data: unlimited source for growth 
Health and Aging 
Public Administration Retail 
Transportation and 
energy 
Agricult...
2. WHAT IS REALLY NEW?
Data has always been key to social 
and economic activities 
• “Business intelligence” and “data 
warehousing” already eme...
9 
DDI is not only about data, 
it is about the data value cycle
The exponential growth in data 
generated and collected 
Monthly global IP traffic, 2005-16 
In exabytes (billions of giga...
The democratisation of computation 
and analytic capacities 
Open source data 
processing and analytics 
Data requests in ...
A new paradigm in decision making? 
Machine learning is now mainstream 
12
3. WHAT ARE THE KEY 
BENEFITS?
Enabling better insights on 
complex issues incl. predictions 
14 
Daily online price index, United States, 
2008-2012 
Re...
Personal data is increasingly used 
for customization 
15 
Personalised services Collaborative filtering
Data and analytics are empowering 
process automation 
16 
• Automatic adjustment of production (e.g. smart grids) 
• Auto...
4. WHAT ARE THE KEY 
CHALLENGES?
Inappropriate use of data and 
analytics 
18 Source: Nature.com
Privacy violation 
19
Loss of autonomy and freedom 
20 
• Discrimination may result in greater 
efficiencies, but also limits an individual’s 
a...
Lack of data scientists across the 
economy 
21 
United States, 2013 EU, 2013 
Professional 
and business 
services, 43% 
...
• Data ownership? 
• Data interoperability? 
• Data portability? 
 Better data sharing platforms and common 
standards co...
Thank you for your attention! 
23 
• OECD project site: http://oe.cd/bigdata 
• OECD (2013), “Exploring Data-Driven Innova...
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Data driven innovation for education

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This presentation was given by Christian Reimsbach-Kounatze of the OECD at the CERI Conference on Innovation, Governance and Reform in Education on 5 November 2014 during session 6.b: The Role of “Big Data”.

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Data driven innovation for education

  1. 1. DATA-DRIVEN INNOVATION FOR EDUCATION 5 November 2014 CERI CONFERENCE ON INNOVATION, GOVERNANCE AND REFORM IN EDUCATION Christian.Reimsbach-Kounatze@oecd.org Directorate for Science, Technology and Innovation (DSTI)
  2. 2. 1. Why should we care about “big data” or data-driven innovation (DDI)? 2. What is really new about it? 3. What are the key opportunities? 4. What are the key challenges? 2 Structure
  3. 3. 1. WHAT IS “BIG DATA”? AND WHY SHOULD WE CARE?
  4. 4. A lot of “big data” buzz • “Data is the new oil.” Andreas Weigend, Stanford (ex Amazon) • “The future belongs to companies and people that turn data into products”, Mike Loukides, O’Reilly Media “Why big data is a big deal” InfoWorld – 9/1/11 “Keeping Afloat in a Sea of 'Big Data” ITBusinessEdge – 9/6/11 “The challenge– and opportunity– of big data” McKinsey Quarterly—5/11 “Getting a Handle on Big Data with Hadoop” Businessweek-9/7/11 “Ten reasons why Big Data will change the travel industry” Tnooz -8/15/11 “The promise of Big Data” Intelligent Utility-8/28/11 4 Source: http://www.google.com/trends/explore#q=%22big%20data%22
  5. 5. What is “big data”? And why we should rather refer to Data-Driven Innovation? • Defining “big data” is challenging: – Data for which the “size is beyond the ability of typical database software tools to capture, store, manage, and analyse” (McKinsey Global Institute, 2011) – Data that is characterized by the 3Vs: volume, velocity (real-time data) and variety (unstructured data) (Gartner, 2011). • DDI refers to the use of data and analytics to improve or foster new products, processes, organisational methods and markets. 5
  6. 6. 6 Data: unlimited source for growth Health and Aging Public Administration Retail Transportation and energy Agriculture Science and Education
  7. 7. 2. WHAT IS REALLY NEW?
  8. 8. Data has always been key to social and economic activities • “Business intelligence” and “data warehousing” already emerged in the 1960s and became popular in the late 1980s (Luhn, 1958; Keen, 1978). • “Formal education has always been a data-rich activity, with many data collected by teachers and schools about learning outcomes, attendance, enrolments” (see agenda) 8
  9. 9. 9 DDI is not only about data, it is about the data value cycle
  10. 10. The exponential growth in data generated and collected Monthly global IP traffic, 2005-16 In exabytes (billions of gigabytes) Average data storage cost, 1998-2012 In USD per gigabyte (log scale) Source: Source: OECD based on Cisco (2012) OECD based on Pingdom (2011) 10
  11. 11. The democratisation of computation and analytic capacities Open source data processing and analytics Data requests in Netflix, 2010-11 Data centre capacity Sources: Netflix.com In billions 11
  12. 12. A new paradigm in decision making? Machine learning is now mainstream 12
  13. 13. 3. WHAT ARE THE KEY BENEFITS?
  14. 14. Enabling better insights on complex issues incl. predictions 14 Daily online price index, United States, 2008-2012 Real-time traffic flows Twitter flue trends
  15. 15. Personal data is increasingly used for customization 15 Personalised services Collaborative filtering
  16. 16. Data and analytics are empowering process automation 16 • Automatic adjustment of production (e.g. smart grids) • Autonomous machines in retail warehousing or self-driving cars Growth in algorithmic trading as share of total trading Source: The Economist (2012)
  17. 17. 4. WHAT ARE THE KEY CHALLENGES?
  18. 18. Inappropriate use of data and analytics 18 Source: Nature.com
  19. 19. Privacy violation 19
  20. 20. Loss of autonomy and freedom 20 • Discrimination may result in greater efficiencies, but also limits an individual’s ability to escape the impact of prejudices • Filter bubbles: users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles.
  21. 21. Lack of data scientists across the economy 21 United States, 2013 EU, 2013 Professional and business services, 43% Others, 5% Financial Wholesale and retail trade, 5% Information, 6% administration, Manufacturing, 11% Public 7% Educational activities, 12% and health services, 11% Professional, scientific and technical activities, 43% Public administration, defence, and sociale services, 15% Wholesale and retail trade, 6% Information and communication , 9% Manufacturing industry, 12% Financial and insurance activities, 7% Transportation and storage, 2% Others, 7% * 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
  22. 22. • Data ownership? • Data interoperability? • Data portability?  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; 22 Getting data governance frameworks right
  23. 23. Thank you for your attention! 23 • OECD project site: http://oe.cd/bigdata • OECD (2013), “Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by ‘Big Data’”: http://oe.cd/bigdata1 • OECD (2015), Data-Driven Innovation for Growth and Well-being – Preliminary synthesis report on “Data-Driven Innovation for Growth and Well-being”: http://oe.cd/bigdata2 • Contact: Christian.Reimsbach-Kounatze@oecd.org

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