DATA-DRIVEN INNOVATION FOR EDUCATION5 November 2014

CERI CONFERENCE ON INNOVATION, GOVERNANCE AND REFORM IN EDUCATION

Christian.Reimsbach-Kounatze@oecd.orgDirectorate for Science, Technology and Innovation (DSTI)

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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?

Structure

1. WHAT IS “BIG DATA”? AND WHY SHOULD WE CARE?

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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

“Keeping Afloat in a Sea of 'Big Data”ITBusinessEdge – 9/6/11

“Why big data is a big deal”InfoWorld – 9/1/11

“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

Source: http://www.google.com/trends/explore#q=%22big%20data%22

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• 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.

What is “big data”? And why we should rather refer to Data-Driven Innovation?

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Data: unlimited source for growth

Health and Aging

Public Administration Retail

Transportation and energy

Agriculture

Data

Science and Education

2. WHAT IS REALLY NEW?

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• “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)

Data has always been key to social and economic activities

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DDI is not only about data, it is about the data value cycle

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The exponential growth in data generated and collected

Monthly global IP traffic, 2005-16 In exabytes (billions of gigabytes)

Average data storage cost, 1998-2012In USD per gigabyte (log scale)

Source: OECD based on Pingdom (2011)Source: OECD based on Cisco (2012)

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The democratisation of computation and analytic capacities

Open source data processing and analytics

Data centre capacity

Sources: Netflix.com

Data requests in Netflix, 2010-11In billions

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Machine learning is now mainstream

A new paradigm in decision making?

3. WHAT ARE THE KEY BENEFITS?

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Enabling better insights on complex issues incl. predictions

Daily online price index, United States, 2008-2012

Real-time traffic flows

Twitter flue trends

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Personal data is increasingly used for customization

Collaborative filteringPersonalised services

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Data and analytics are empowering process automation

Growth in algorithmic trading as share of total trading

• Automatic adjustment of production (e.g. smart grids)• Autonomous machines in retail warehousing or

self-driving cars

Source: The Economist (2012)

4. WHAT ARE THE KEY CHALLENGES?

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Inappropriate use of data and analytics

Source: Nature.com

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Privacy violation

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Loss of autonomy and freedom

• 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.

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Lack of data scientists across the economy

Professional and business services, 43%

Financial activities, 12%Educational

and health services, 11%

Manufacturing, 11%

Public administration,

7%

Information, 6%

Wholesale and retail trade, 5%

Others, 5%

United States, 2013 EU, 2013

Professional, scientific and

technical activities, 43%

Public administration, defence, and

sociale services, 15%

Manufacturing industry, 12%

Information and

communication, 9%

Financial and insurance

activities, 7%

Wholesale and retail trade, 6%

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

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• 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;

Getting data governance frameworks right

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Thank you for your attention!

• 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