Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomyby Hongwei Zhu, Harris Wu

Decision Support Systems

About

Year
2014
DOI
10.1016/j.dss.2014.01.006
Subject
Information Systems and Management / Information Systems / Management Information Systems

Similar

An Account of the State Prison, or Penitentiary House in New-York (Concluded)

Authors:
One of the Inspectors of the Prison
1811

The status of the otter (L. lutra L.) in Britain in 1977

Authors:
THE EARL OF CRANBROOK
1977

Principles, concepts, and practices in prosthodontics

Authors:
The Academy of Denture Prosthetics
1963

Christians and atomic war

Authors:
The British Council of Churches
1959

Text

an of M

Adm d by thei s m -sc ns, alu

AAP onfi ma data. ts to be nged a such a ss the ents a uch d andard quality metrics cwe implement an auluation shows that the easurements.We evalDecision Support Systems 59 (2014) 351–360

Contents lists available at ScienceDirect

Decision Supp .ehave everything I need? Do I need everything in the standard? Does the standard accomplish its primary objective? We evaluate the framework using real-world data standards and the corresponding data inuate the framework using XBRL taxonomies and data instances. The methods are applicable to any data standards specified using a formal language such as XML. (4) The framework and the evaluation also di-gap by developing a framework with metrics and automatic methods to systematically assess the quality of large-scale data standards. The framework offers methods to answer fundamental questions about a data standard, such as: Is the standard complex? Does the standard interoperability. We are not aware of the same st elsewhere in extant literature. (3) For eachmetri tomatedmethod to obtain themeasurement. Eva metrics are effective for large-scale automatedmlarge-scale data standards are needed to aid the development, implementation, and evolvement of data standards.

Despite extensive work in the areas of data and information quality [18,30,54,56,66], little has been done to create automated methods for assessing the quality of data standards. We attempt to fill this research pact and easy to implement, but also informative and relatively comprehensive. (2) The contextual and effectual metrics of the framework are novel. The contextual metrics measure how well a data standard fits users' needs. The effectual metrics objectivelymeasure howwell a standard has accomplished its primary objective of achieving semantic datastances in the financial domain. The standar

Generally Accepted Accounting Principles (GA ⁎ Corresponding author. Tel.: +1 978 934 2585.

E-mail addresses: hongwei_zhu@uml.edu (H. Zhu), hw 0167-9236/$ – see front matter © 2014 Elsevier B.V. All ri http://dx.doi.org/10.1016/j.dss.2014.01.006ata standards are costly t on organizations that easuring the quality of

Our work makes four contributions to both research and practice. (1) The framework consists of a small number of quality metrics for four primary aspects of data standard quality. Thus it is not only com-to develop and can have a significant impac use the standards. Systematic methods for mGAAP Taxonomy 1. Introduction

Data standards specify data elemen zations to create data that can be excha ously. Large-scale data standards,

Department of Defense [52] and acro dustry [32], include many data elem by a large number of organizations. Sused bymultiple organind processed unambigus those within the US real estate mortgage innd are intended for use in 2009 and then revised in 2011. The two versions of the Taxonomy are specified using the eXtensible Business Markup Language (XBRL) [70]. The Securities and Exchange Commission (SEC) has adopted both the 2009 and 2011 versions of the GAAP Taxonomy and mandated the public companies to use either version to create their financial statements. The data instances are official financial statements encoded in

XBRL, submitted to the SEC by publicly traded companies.XBRL © 2014 Elsevier B.V. All rights reserved.

Quality assessment or consume standards-basedAssessing the quality of large-scale data st

GAAP Taxonomy

Hongwei Zhu a,⁎, Harris Wu b a Department of Operations and Information Systems, Manning School of Business, University b Department of Information Technology and Decision Sciences, College of Business and Public a b s t r a c ta r t i c l e i n f o

Article history:

Received 12 May 2013

Received in revised form 8 January 2014

Accepted 17 January 2014

Available online 24 January 2014

Keywords:

Information quality

Data quality

Data standards

Data standards are often use veloping data standards and the quality of data standard assessing the quality of large contextual quality dimensio data interoperability. We ev reporting standard, the US G onomy. Evaluation results c valuable insights to decision j ourna l homepage: wwwds are the United States

AP) Taxonomy released u@odu.edu (H. Wu). ghts reserved.dards: A case of XBRL assachusetts Lowell, Lowell, MA 01854, United States inistration, Old Dominion University, Norfolk, VA 23529, United States multiple organizations to produce and exchange data. Given the high cost of der significant impact on the interoperability of data produced using the standards, ust be systematically measured. We develop a framework for systematically ale data standards using automated tools. It consists of metrics for intrinsic and as well as effectual metrics that assess the extent to which a standard enables ate the quality assessment framework using two versions of a large financial

Taxonomy, and public companies' financial statements created using the Taxrm the effectiveness of the framework. Findings from the evaluation also offer kers who develop and improve data standards, select and adopt data standards, ort Systems l sev ie r .com/ locate /dssrectly answer the call for increased professional relevance in decision support systems research [14]. The SEC has tasked the Financial Accounting Standards Board (FASB) to continuously “improve” the GAAP

Taxonomy. The methods and findings of this research are apparently useful to decisionmakers such as those at FASB and other standards development organizations. 352 H. Zhu, H. Wu / Decision Support Systems 59 (2014) 351–360The rest of the paper is organized as follows. Section 2 reviews related research to justify the need for effective measurement of data standard quality. Section 3 describes the metrics of the framework.

Section 4 presents the evaluation method and briefly describes the data standards and the corresponding data used for the evaluation.

Section 5 presents the evaluation results. Section 6 discusses the characteristics of this research. Section 7 concludes the paper and points out directions of future research. 2. Related work