The Perils of Working with Big Data and a SMALL Framework You Can Use to Avoid Them

Scott A. Brave, R. Butters, M. Fogarty
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引用次数: 1

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.
使用大数据的危险和一个可以用来避免它们的小框架
如今,利用“大数据”来解释宏观经济的波动或指导企业的商业决策是如此普遍,以至于在某些情况下,它甚至开始与更传统的政府统计和商业分析相媲美。与这些更传统的数据源相比,大数据源通常可以提供优势,但这些优势也带来了潜在的陷阱。我们提出了一个名为SMALL的框架,这是我们开发的,目的是帮助有兴趣的各方在大数据雷区中导航。基于一组五个问题,SMALL框架应该帮助大数据用户在他们自己的工作中发现问题,以及其他依赖这些数据得出具有可操作的公共政策或商业影响的结论的人。为了证明这一点,我们提供了几个案例研究,这些案例研究表明,在处理和解释这些新的大数据源时,有必要保持一定的怀疑态度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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