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Quality of data standards: framework and illustration using XBRL taxonomy and instances

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  • Hongwei Zhu

    (Old Dominion University)

  • Harris Wu

    (Old Dominion University)

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.

Suggested Citation

  • Hongwei Zhu & Harris Wu, 2011. "Quality of data standards: framework and illustration using XBRL taxonomy and instances," Electronic Markets, Springer;IIM University of St. Gallen, vol. 21(2), pages 129-139, June.
    • Handle: RePEc:spr:elmark:v:21:y:2011:i:2:d:10.1007_s12525-011-0060-4
    DOI: 10.1007/s12525-011-0060-4
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    References listed on IDEAS

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    1. Ying Zhang & Yuelin Li, 2008. "A user‐centered functional metadata evaluation of moving image collections," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(8), pages 1331-1346, June.
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    Cited by:

    1. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    2. Roman Lukyanenko & Andrea Wiggins & Holly K. Rosser, 0. "Citizen Science: An Information Quality Research Frontier," Information Systems Frontiers, Springer, vol. 0, pages 1-23.
    3. Roman Lukyanenko & Jeffrey Parsons & Yolanda F. Wiersma, 2014. "The IQ of the Crowd: Understanding and Improving Information Quality in Structured User-Generated Content," Information Systems Research, INFORMS, vol. 25(4), pages 669-689, December.
    4. Vysochan Oleh S. & Hyk Vasyl & Mykytyuk Nataliya & Vysochan Olha O., 2023. "Taxonomy of Financial Reporting in the Context of Digitalization of the Economy: Domestic and International Analysis Scientific Research," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(2), pages 49-70, June.
    5. Roman Lukyanenko & Andrea Wiggins & Holly K. Rosser, 2020. "Citizen Science: An Information Quality Research Frontier," Information Systems Frontiers, Springer, vol. 22(4), pages 961-983, August.

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      Keywords

      Information quality; Data quality; Data standards; XBRL; US GAAP taxonomy;
      All these keywords.

      JEL classification:

      • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General

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