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Big Data as a Source for Official Statistics

Author

Listed:
  • Daas Piet J.H.

    (Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands.)

  • Puts Marco J.

    (Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands)

  • Buelens Bart

    (Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands.)

  • Hurk Paul A.M. van den

    (Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands.)

Abstract

More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. This article discusses the exploration of both opportunities and challenges for official statistics associated with the application of Big Data. Experiences gained with analyses of large amounts of Dutch traffic loop detection records and Dutch social media messages are described to illustrate the topics characteristic of the statistical analysis and use of Big Data.

Suggested Citation

  • Daas Piet J.H. & Puts Marco J. & Buelens Bart & Hurk Paul A.M. van den, 2015. "Big Data as a Source for Official Statistics," Journal of Official Statistics, Sciendo, vol. 31(2), pages 249-262, June.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:2:p:249-262:n:6
    DOI: 10.1515/jos-2015-0016
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    References listed on IDEAS

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    1. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Daas, Piet J.H. & Puts, Marco J.H., 2014. "Social media sentiment and consumer confidence," Statistics Paper Series 5, European Central Bank.
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    Cited by:

    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. John L. Czajka & Mathew Stange, "undated". "Transparency in the Reporting of Quality for Integrated Data: A Review of International Standards and Guidelines," Mathematica Policy Research Reports 984e8919667b48ab9aabcbbcb, Mathematica Policy Research.
    3. Ogerta Elezaj & Dhimiter Tole & Nevila Baci, 2018. "Big Data in e-Government Environments: Albania as a Case Study," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 7, July.

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