Big Data: Potential, Challenges and Statistical Implications

Author/Editor:

Cornelia Hammer ; Diane C Kostroch ; Gabriel Quiros-Romero

Publication Date:

September 13, 2017

Electronic Access:

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Disclaimer: This Staff Discussion Note represents the views of the authors and does not necessarily represent IMF views or IMF policy. The views expressed herein should be attributed to the authors and not to the IMF, its Executive Board, or its management. Staff Discussion Notes are published to elicit comments and to further debate.

Summary:

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.

Series:

Staff Discussion Notes No. 2017/006

Subject:

English

Publication Date:

September 13, 2017

ISBN/ISSN:

9781484310908/2617-6750

Stock No:

SDNEA2017006

Pages:

41

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