Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomy

摘要

Data standards are often used by multiple organizations to produce and exchange data. Given the high cost of developing data standards and their significant impact on the interoperability of data produced using the standards, the quality of data standards must be systematically measured. We develop a framework for systematically assessing the quality of large-scale data standards using automated tools. It consists of metrics for intrinsic and contextual quality dimensions, as well as effectual metrics that assess the extent to which a standard enables data interoperability. We evaluate the quality assessment framework using two versions of a large financial reporting standard, the US GAAP Taxonomy, and public companies' financial statements created using the Taxonomy. Evaluation results confirm the effectiveness of the framework. Findings from the evaluation also offer valuable insights to decision makers who develop and improve data standards, select and adopt data standards, or consume standards-based data.