Abstract
The primary purpose of data standards or metadata schemas is to improve the interoperability of data created by multiple standard users. Given the high cost of developing data standards, it is desirable to assess the quality of data standards. We develop a set of metrics and a framework for assessing data standard quality. The metrics include completeness and relevancy. Standard quality can also be indirectly measured by assessing interoperability of data instances. We evaluate the framework using data from the financial sector: the XBRL (eXtensible Business Reporting Language) GAAP (Generally Accepted Accounting Principles) taxonomy and US Securities and Exchange Commission (SEC) filings produced using the taxonomy by approximately 500 companies. The results show that the framework is useful and effective. Our analysis also reveals quality issues of the GAAP taxonomy and provides useful feedback to taxonomy users. The SEC has mandated that all publicly listed companies must submit their filings using XBRL. Our findings are timely and have practical implications that will ultimately help improve the quality of financial data.
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Zhu, H., Wu, H. (2010). Assessing Quality of Data Standards: Framework and Illustration Using XBRL GAAP Taxonomy. In: Sánchez-Alonso, S., Athanasiadis, I.N. (eds) Metadata and Semantic Research. MTSR 2010. Communications in Computer and Information Science, vol 108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16552-8_26
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DOI: https://doi.org/10.1007/978-3-642-16552-8_26
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