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Electron Markets (2011) 21:129139 DOI 10.1007/s12525-011-0060-4
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Quality of data standards: framework and illustration using XBRL taxonomy and instances
Hongwei Zhu & Harris Wu
Received: 26 August 2010 /Accepted: 6 April 2011 /Published online: 27 April 2011 # Institute of Information Management, University of St. Gallen 2011
Abstract The primary purpose of data standards is to improve the interoperability of data in an increasingly networked environment. Given the high cost of developing data standards, it is desirable to assess their quality. We develop a set of metrics and a framework for assessing data standard quality. The metrics include completeness, relevancy, and a combined measure. Standard quality can also be indirectly measured by assessing interoperability of data instances. We evaluate the framework on a data standard for financial reporting in United States, the Generally Accepted Accounting Principles (GAAP) Taxonomy encoded in eXtensible Business Reporting Language (XBRL), and the financial statements created using the standard by public companies. The results show that the data standard quality framework is useful and effective. Our analysis also reveals quality issues of the US GAAP XBRL taxonomy and provides useful feedback to taxonomy users. The Securities and Exchange Commission 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 and the efficiency of the data supply chain in a networked business environment.
Keyword Information quality. Data quality. Data standards . XBRL . US GAAP taxonomy
JEL Classification M40 - General, Accounting and Auditing
Introduction
Data standards are used to reduce schematic and semantic heterogeneity and to ensure interoperability of data from multiple sources. There have been successful large-scale data standardization efforts such as those within the US Department of Defense (Rosenthal et al. 2004) and across the residential mortgage industry (Markus et al. 2006). As a community-based collaborative approach to information management, data standards have the potential to improve the efficiency of the data supply chain in an increasingly networked business environment.
A data standard is expected to improve the quality, especially the interoperability, of data created by different organizations using the standard. To accomplish this objective, the data standard itself must have high quality and there should be...