IDEAS home Printed from https://ideas.repec.org/p/imf/imfsdn/2017-006.html
   My bibliography  Save this paper

Big Data: Potential, Challenges and Statistical Implications

Author

Listed:
  • Cornelia Hammer
  • Ms. Diane C Kostroch
  • Mr. Gabriel Quiros-Romero

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.

Suggested Citation

  • Cornelia Hammer & Ms. Diane C Kostroch & Mr. Gabriel Quiros-Romero, 2017. "Big Data: Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 2017/006, International Monetary Fund.
    • Handle: RePEc:imf:imfsdn:2017/006
    as

    Download full text from publisher

    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=45106
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    2. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    3. Riccardo De Bonis & Matteo Piazza, 2021. "A silent revolution. How central bank statistics have changed in the last 25 years," PSL Quarterly Review, Economia civile, vol. 74(299), pages 347-371.
    4. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    5. Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019. "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers 2019/275, International Monetary Fund.
    6. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    7. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    8. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    9. Giulia Mugellini & Jean‐Patrick Villeneuve & Marlen Heide, 2021. "Monitoring sustainable development goals and the quest for high‐quality indicators: Learning from a practical evaluation of data on corruption," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1257-1275, November.
    10. Shohei Doi & Takayuki Mizuno & Naoya Fujiwara, 2021. "Estimation of socioeconomic attributes from location information," Journal of Computational Social Science, Springer, vol. 4(1), pages 187-205, May.
    11. Hristo Prodanov, 2019. "Big Data, changes in statistics and the new challenges to politico-economic systems," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 40-58.
    12. Alexandre Gori Maia & Jose Daniel Morales Martinez & Leticia Junqueira Marteleto & Cristina Guimaraes Rodrigues & Luiz Gustavo Sereno, 2023. "Can the Content of Social Networks Explain Epidemic Outbreaks?," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(1), pages 1-34, February.
    13. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:imf:imfsdn:2017/006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Akshay Modi (email available below). General contact details of provider: https://edirc.repec.org/data/imfffus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.