The primary purpose of a data standard is to improve the comparability of data created by multiple standard users. Given the high cost of developing and implementing data standards, it is desirable to be able to assess the quality of data standards. We develop metrics for measuring completeness and relevancy of a data standard. These metrics are evaluated empirically using the US GAAP taxonomy in XBRL and SEC filings produced using the taxonomy by approximately 500 companies. The results show that the metrics are useful and effective. Our analysis also reveals quality issues of the GAAP taxonomy and provides useful feedback to the taxonomy users. The SEC has mandated that all publicly listed companies must submit their filings using XBRL beginning mid 2009 to late 2014 according to a phased-in schedule. Thus our findings are timely and have practical implications that will ultimately help improve the quality of financial data.
Zhu, Hongwei and Wu, Harris, "Quality of XBRL US GAAP Taxonomy: Empirical Evaluation using SEC Filings" (2010). AMCIS 2010 Proceedings. 579.