Data, Big Data and Sustainable Development

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1 Data, Big Data and Sustainable Development Dr. Ugo Caruso Lead, Digital Learning UNSSC Knowledge Centre for Sustainable Development

2 Are we in the midst of a data revolution?

3 The data revolution is The integration of new data with traditional data to produce high quality information that is more detailed, timely and relevant for many purposes and users, especially to foster and monitor sustainable development United Nations IAEG on a Data Revolution, A World that Counts, (2014)

4 More and better data covering the broader scope of SDGs Data disaggregation Data transparency and open access to data Harnessing new sources of data Strengthening national statistical capacity for better accountability and decision-making Key ideas of data revolution

5 Taxonomy of Big Data EXHAUST DATA: passively collected. Transactional data from people s use of digital services MDG data: Mostly collected and owned by Governments. SDG data: Partly produced passively by people, collected by machines and owned by corporations. (Robert Kirkpatrick, Director of UN Global Pulse) Examples Mobile phone data Financial transactions Online search and access logs Administrative data / Citizen card Postal data

6 Taxonomy of Big Data SENSING DATA: detected through a device that detects and responds to input from a physical environment Examples Satellite Sensors in cities, transport and homes Sensors in nature, agriculture and water Wearable technology Biometric data

7 Taxonomy of Big Data DIGITAL CONTENT: actively produced by people as well as institutions Examples Social media data Web scraping Health records Radio content

8 Concepts: from big data to Big Data

9 Concepts: from big data to Big Data 1. Big data (as data): Digital translations of human actions, interactions and transactions picked up by digital devices and services. 2. Big Data (as a field of research and practice): an ecosystem of the 3 Cs of Big Data as data crumbs, capacities (human and technical), and communities ] producing and leveraging information to shape decisions.

13 Few Examples

14 Poverty

15 Mobility

16 Using Financial Transaction Data To Measure Economic Resilience To Natural Disasters

17 Health records

18 Few examples

19 How can we use Big Data for Sustainable Development?

The (Big) Data revolution, SDGs & development March 2016 1.

disaggregated data and what does monitoring something do to that something? 1.

20 The (Big) Data revolution, SDGs & development March How can (Big) Data help monitor the SDGs by filling data gaps with more granular & disaggregated data and what does monitoring something do to that something? 1. How can (Big) Data help promote (or impede?) the SDGS and their underlying human development vision and objectives?

21 The (Big) Data revolution, SDGs & development

22 SDGs - Tier classification of indicators Tier 1: Indicator is conceptually clear and has an internationally established methodology and standards are available. In addition, data are regularly produced by countries. Tier 2: Indicator is conceptually clear, has an internationally established methodology and standards are available, but data are not regularly produced by countries. Tier 3: No internationally established methodology or standards are yet available for the indicator, but methodology/standards are being (or will be) developed or tested.

23 Inter-agency Expert Group on SDG Indicators Tier As of 15 Dec 2017 Indicator Percentage 41% 29% 30% In addition to these, there are 5 indicators that have multiple tiers (different components of the indicator are classified into different tiers). Not evenly distributed, e.g. SDG 13 got its first tier 1 in Nov Scientific review: 91 targets (54 %) could be strengthened by being more specific, and 29 (17 %) require significant work.

24 National Roadmaps Roadmaps from: Colombia Kenya Senegal Tanzania Sierra Leone Philippines

25 Highlights Alignment with National and Regional Development Priorities Institutional Cooperation Engagement with the Private Sector Data sharing remains problematic

26 Opportunities and Challenges?

Key requirements for the future We have proposed three requirements that we consider to be of paramount importance in order to enable positive disruption of data-driven policy-making: user-centric

27 Key requirements for the future We have proposed three requirements that we consider to be of paramount importance in order to enable positive disruption of data-driven policy-making: user-centric data ownership and management; algorithmic transparency and accountability; and living labs to experiment with datadriven policies in the wild. It will be only when we honor these requirements that we will be able to move from the feared tyranny of data and algorithms to a data-enabled model of democratic governance running against tyrants and autocrats.

28 Building blocks of data literacy Concepts: from big data to Big Data

To know more UNSSC/Data-Pop Alliance free

Leveraging Big Data for Sustainable Development

2018 UNSSC/Data-Pop Alliance free open Learning

29 To know more UNSSC/Data-Pop Alliance free webinar series on Big Data for Sustainable Development UNSSC/Data-Pop Alliance training on Leveraging Big Data for Sustainable Development at MIT Media Lab (Cambridge, US) October 2018 UNSSC/Data-Pop Alliance free open Learning Hub on Big Data for Sustainable Development (on Claned).

30 Thank you!

Big Data: what is it and how can (could?) it contribute to sustainable development?

Big Data: what is it and how can (could?) it contribute to sustainable development? Emmanuel Letouzé, PhD Director and co-founder Data-Pop Alliance Visiting Scholar MIT Media Lab Research Associate ODI