IGF 2020 WS #207 Ensuring Trusted Data Sharing for Monitoring the SDGs

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Nagisa Miyachi

The COVID-19 pandemic highlighted the significance of real-time data access  for policymakers to make informed and urgent decisions. In this session, panellists from the public sector and civil society discussed challenges they face and ways to improve the data ecosystem that champions data as a public good.

National statistics institutions around the world have relied on traditional data production methodologies, such as surveys and administrative data. However, traditional official data may not produce timely and reliable analysis to meet the needs and demands from policymakers and to support monitoring of sustainable development goals (SDGs). In the advent of digital transformation, statisticians started to look at alternative data sources, such as open-source data, web scraping, and satellite imagery, to complement traditional statistics tools. Mr Mark Uhrbach (Chief Program Manager - Digital Economy, Statistics Canada) underscored that the current pandemic has accelerated many activities to use such alternative data sources, since surveys cannot be conducted during the lockdown. He noted that new data sources, combined with traditional sources, are able to portray an accurate picture, particularly when used to research emerging issues, such as e-commerce transactions.

Statistics for policy-making, particularly in areas such as digital transformation, require access to privately-held datasets. Many panellists shared their struggles over gaining access to datasets owned by private corporations. Mr Dominik Rozkrut (President, the Central Statistical Office of Poland) explained the lack of willingness of private actors because handing over the datasets provides little value and incentive to them. He added that there is currently a lack of professionals in the private sector who can bridge the public and private sectors and champion the multistakeholder data ecosystem. While congratulating Google and Apple on the initiative to develop the application programming interface (API) to notify exposure to Coronavirus as a good example of data for the public good, he pressed that innovation to use data for the public good should not stop at the time the pandemic subsides.

Public private partnership (PPP) in data sharing is key for evidence-based policy design in the digital age, given the vast majority of data is in the hands of private companies. Ms Helani Galpaya (Chief Executive Officer, LIRNEasia) pointed to the problem with the current data sharing model as it is a matter of power and relationship whether public institutions can access datasets held by tech operators. Uhrbach highlighted that the public sector needs to present value propositions for data providers. He also noted that the government can take a legislative approach to force private companies to hand over their datasets, however, the legislative power is limited when it comes to multinational corporations. Mr Daniel Ker (Economist, the Organisation for Economic and Co-operation and Development (OECD)) proposed another approach, framing the message in corporate social responsibility. He also noted that it is important to foster dialogue between the public and the private to build confidence of private actors in data sharing. The government may do so by showcasing the independence of the national statistics agency.

International organisations and civil society play an integral role in collecting, analysing, and disseminating statistics of resource-limited countries. Mr Jaco Du Toit (Chief of Section Universal Access to Information, the United Nations Educational, Scientific, Cultural Organization (UNESCO)) shared UNESCO’s four guiding principles of statistics: findability, accessibility, interoperability, and reusability. These four principles help ensure that statistics help national and international policymakers have access to information they need to make sound decisions to accelerate progress towards the attainment of sustainable development goals (SDGs), as well as combining two or more datasets to produce insights on intersectional issues. In developing nations, securing funding for statistical surveys is a common challenge for field researchers. While the World Bank and the International Telecommunication Union (ITU) rely on national governments or civil society on the field to collect data related to development, Ms Alison Gillwald (Executive Director, Research ICT Africa (RIA)) recalled that the lack of funding had inhibited her from doing so. Funding traditionally allocated to surveys is now used for artificial intelligence (AI) and big data, illustrating the difficulties of developing countries in generating sufficient funding to combine traditional and emerging methods of statistics.