Big Data: Potential, Challenges and Statistical Implications

Cornelia Hammer, Diane Kostroch and Gabriel Quiros-Romero

No 2017/006, IMF Staff Discussion Notes from International Monetary Fund

Abstract: 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.

Keywords: SDN; big data data source; strategy action plan; innovation challenge; big data application; big data classification; big data project; IMF big data; private sector; project inventory; Big Data; Macroeconomic and Financial Statistics; Official Statistics; Data Quality; Surveillance; statistics big data strategy action plan; big data data analysis; big data network; big data revolution; big data use; Social networks; Financial statistics; Global (search for similar items in EconPapers)
Pages: 41
Date: 2017-09-13
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Citations: View citations in EconPapers (14)

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