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Big Data Is a Big Deal But How Much Data Do We Need?


  • Askitas, Nikos

    () (IZA)


The more conservative among us believe that "Big Data is a fad that will soon fade out" and they may in fact be partially right. By contrast, others – especially those who dispassionately note that digitization is only now beginning to deliver its payload – may beg to differ. We argue that all things considered, Big Data will likely cease to exist, although this will happen less because it is a fad and more because all data will eventually be Big Data. In this essay, I pose and discuss the question of "how much data do we really need" since everything in life and hence the returns from data increments ought to obey some kind of law of diminishing returns: the more the better, but at some point the gains are not worth the effort or become negative. Accordingly, I discuss small and large, specific and general examples to shed light on this question. I do not exhaustively explore the answers, rather aiming more towards provoking thought among the reader. The main conclusions, nonetheless, are that depending on the use case both a deficit and an abundance of data may be counterproductive, that individuals, data experts, firms or society have different optimization problems whereby nothing will free us from having to reach decisions concerning how much data is enough data and that the greatest challenges that data-intensive societies will face are positive reinforcement, feedback mechanisms and data endogeneity.

Suggested Citation

  • Askitas, Nikos, 2016. "Big Data Is a Big Deal But How Much Data Do We Need?," IZA Discussion Papers 9988, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp9988

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    References listed on IDEAS

    1. Robert J. Shiller, 2015. "Irrational Exuberance," Economics Books, Princeton University Press, edition 3, number 10421, December.
    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


    Big Data; endogeneity; social science; causality; prediction;

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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