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The perils of working with Big Data and a SMALL framework you can use to avoid them

Author

Listed:
  • Scott A. Brave
  • R. Andrew Butters
  • Michael Fogarty

Abstract

The use of “Big Data” to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL framework should help users of big data spot concerns in their own work and that of others who rely on such data to draw conclusions with actionable public policy or business implications. To demonstrate, we provide several case studies that show a healthy dose of skepticism can be warranted when working with and interpreting these new big data sources.

Suggested Citation

  • Scott A. Brave & R. Andrew Butters & Michael Fogarty, 2020. "The perils of working with Big Data and a SMALL framework you can use to avoid them," Working Paper Series WP-2020-35, Federal Reserve Bank of Chicago, revised 02 Mar 2020.
  • Handle: RePEc:fip:fedhwp:92317
    DOI: 10.21033/wp-2020-35
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    Cited by:

    1. Daniel Aaronson & Scott A. Brave & Michael Fogarty & Ezra Karger & Spencer D. Krane, 2021. "Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade," Working Paper Series WP-2021-05, Federal Reserve Bank of Chicago, revised 18 Jun 2021.

    More about this item

    Keywords

    big data; economic statistics; business analytics; forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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