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Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.
Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

need to find technologies and methods that safeguard their independence and reputation and the public trust. For instance, when governments agree that their compiling agencies cannot be left out of the big data revolution, an important role of the legislative body is to negotiate access to big data. 42. Privacy, confidentiality, and cybersecurity risks are a major concern when using big data . Big data contains large amounts of sensitive and personal information that may be exposed to privacy, confidentiality, and cybersecurity risks. If not sufficiently protected

International Monetary Fund. Strategy, Policy, & Review Department
The scenario planning exercises help to draw out the surveillance priorities and stress- test the robustness of those priorities to uncertainties in the decade ahead. To inform the two priorities on confronting risks and uncertainties and mitigating spillovers, the scenarios illustrate how different shocks and alternative policy approaches carry their own risks and can have both positive and negative spillovers. The scenarios also illustrate some of the complex economic and non-economic factors that feed into the priority on economic sustainability and demonstrate how resource constraints and changing economic structures underpin the need for a unified policy approach.
International Monetary Fund. Strategy, Policy, & Review Department

productivity , and fuels corporations’ expansion into new areas including traditional government roles. More efficient and less risky financial services within ecosystem. SAE investment destinations receive state-directed credit. Low growth, following climate and trade negative aggregate supply shocks, but less of a priority relative to environmental well-being. Diversified countries and climate-technology first-movers doing better. More room for weather-contingent financial diversification. Large productivity gains from 5G access and big data revolution in less