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of information technology, and revamping long-standing practices in acquiring, disseminating and analyzing information ,” adds Marco Marini, who is part of the IMF’s Statistics Department. Such changes will include new legal agreements, adapting cloud storage and related big data platforms, and acquiring an expertise in data science and machine learning techniques. The necessary skills will be acquired through a combination of training existing employees and hiring those with new skills. In short, a big data practice for policy analysis and economic surveillance in

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

proprietary cloud. Increasingly, private companies sell their data for a profit; over time the generation of big data may become a major objective, and not just a byproduct, of their activity. The statistical community and official users, as well as national and international policymakers, will confront many decisions, including complex negotiation processes. 47. Despite a multitude of technical options and platforms to choose from, the selection processes are quite complex . A typical approach that companies follow in setting up a big data practice is establishment of a

IMF Research Perspective (formerly published as IMF Research Bulletin) is a new, redesigned online newsletter covering updates on IMF research. In the inaugural issue of the newsletter, Hites Ahir interviews Valeria Cerra; and they discuss the economic environment 10 years after the global financial crisis. Research Summaries cover the rise of populism; economic reform; labor and technology; big data; and the relationship between happiness and productivity. Sweta C. Saxena was the guest editor for this inaugural issue.
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.