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

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.