The promise and perils of using big data in the study of corporate networks: problems, diagnostics and fixes

A new paper by the CORPNET group has been published in the Global Networks journal. The article, “The promise and perils of using big data in the study of corporate networks: problems, diagnostics and fixes”, is written by Eelke Heemskerk, Kevin Young, Frank takes, Bruce Cronin, Javier Garcia-Bernardo, Lasse Hendriksen, William Winekoff, Vladimir Popov and Audrey Lautin-Lamothe, and can be found here.

Abstract
Network data on connections between corporate actors and entities – for instance through co-ownership ties or elite social networks – are increasingly available to researchers interested in probing the many important questions related to the study of modern capitalism. Given the analytical challenges associated with the nature of the subject matter, variable data quality and other problems associated with currently available data on this scale, we discuss the promise and perils of using big corporate network data (BCND). We propose a standard procedure for helping researchers deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these through a specific work-flow procedure. For each step in this procedure, we provide a set of best practices for how to identify, resolve and minimize the BCND problems that arise.

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