Approaches to Identifying Data Quality Issues: The Role of the Data Broker

Arif Wibisono*, David Sammon, Ciara Heavin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Data quality issues are problematic and costly for organizations. Employees (termed “Data Brokers”) must identify data quality issues before data are used for reporting purposes. In five field studies, we investigate how these employees identify these often-hidden data quality issues. Organizations can execute five “checking” approaches: data templates, supervisor validation, data accuracy, data consistency, and data completeness. We discuss each approach, theorize their inter-relationships, and explain their contributions to research and practice.

Original languageEnglish
Pages (from-to)226-237
Number of pages12
JournalInformation Systems Management
Issue number3
Publication statusPublished - 2024


  • Data quality issues
  • data broker
  • data curation
  • field studies
  • manual identification


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