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

Eelke Heemskerk*, Kevin Young, Frank W. Takes, Bruce Cronin, Javier Garcia-Bernardo, Lasse F. Henriksen, William Kindred Winecoff, Vladimir Popov, Audrey Laurin-Lamothe

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    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.

    Original languageEnglish
    Pages (from-to)3-32
    Number of pages30
    JournalGlobal Networks
    Volume18
    Issue number1
    DOIs
    Publication statusPublished - Jan 2018

    Bibliographical note

    Funding Information:
    This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 638946), and also from the Russell Sage Foundation (grant no. 83-15-13).

    Publisher Copyright:
    Global Networks © 2017 Global Networks Partnership & John Wiley & Sons Ltd

    Funding

    This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 638946), and also from the Russell Sage Foundation (grant no. 83-15-13).

    Keywords

    • BIG CORPORATE NETWORK DATA
    • BIG DATA
    • CORPORATE NETWORKS
    • DIAGNOSTICS
    • NETWORK DATA QUALITY

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