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Big Data is a big deal but how much data do we need?
[Big Data gut und schön. Aber wie viel Data brauchen wir?]

Author

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  • Nikolaos Askitas

    () (Institute for the Study of Labor)

Abstract

Abstract Those with a more conservative disposition might believe that Big Data is a short-lived fad and they may in fact be partially right. Others by contrast – especially those who dispassionately note that digitization is only now beginning to deliver its payload – may beg to differ. I argue that all things considered, Big Data will likely cease to exist, not so much because it is a fad but quite likely because all data will eventually be Big Data. In this essay, with the law of diminishing returns in the back of my mind, I use diverse examples, in an effort to shed some light on the question of “how much data do we really need”. My intend is not to exhaustively explore the answers so much as it is to provoke thought among the reader. I argue that depending on the use case both a data deficit and an abundance thereof may be counterproductive and that the various stakeholders, from lay persons and data experts to firms and the society at large, are probably faced with different, and possibly conflicting, optimization problems, whereby nothing will free us from having to continuously ponder on how much data is enough data. Finally the greatest challenges that data-intensive societies are likely to face might include positive reinforcement, feedback mechanisms and data endogeneity.

Suggested Citation

  • Nikolaos Askitas, 2016. "Big Data is a big deal but how much data do we need?
    [Big Data gut und schön. Aber wie viel Data brauchen wir?]
    ," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 113-125, October.
  • Handle: RePEc:spr:astaws:v:10:y:2016:i:2:d:10.1007_s11943-016-0191-3
    DOI: 10.1007/s11943-016-0191-3
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    References listed on IDEAS

    as
    1. Robert J. Shiller, 2015. "Irrational Exuberance," Economics Books, Princeton University Press, edition 3, number 10421, March.
    2. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    4. Natalie Shlomo & Harvey Goldstein, 2015. "Editorial: Big data in social research," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 787-790, October.
    5. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
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    Cited by:

    1. Engels, Barbara, 2016. "Big-Data-Analyse: Ein Einstieg für Ökonomen," IW-Kurzberichte 78.2016, Institut der deutschen Wirtschaft (IW) / German Economic Institute.
    2. Ralf Thomas Münnich & Markus Zwick, 2016. "Big Data und was nun? Neue Datenbestände und ihre Auswirkungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 73-77, October.

    More about this item

    Keywords

    Big Data; Endogeneity; Social science; Causality; Prediction;

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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