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AIMQ: a methodology for information quality assessment

Authors:

Diane M. Strong,

Beverly K. Kahn,

Richard Y. WangAuthors Info & Claims

Information and Management, Volume 40, Issue 2

Pages 133 - 146

https://doi.org/10.1016/S0378-7206(02)00043-5

Published: 01 December 2002 Publication History

Abstract

Information quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIM quality (AIMQ) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.

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Index Terms

AIMQ: a methodology for information quality assessment
1. Information systems
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. System management
        Quality assurance

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cover image Information and Management

Information and Management Volume 40, Issue 2

December 2002

71 pages

ISSN:0378-7206

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Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2002

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Cited By

Wamba SWamba-Taguimdje STwinomurinzi H(2024)Why Should Users Take the Risk of Sustainable Use of Generative Artificial Intelligence ChatbotsJournal of Global Information Management10.4018/JGIM.36560032:1(1-32)Online publication date: 7-Aug-2024
https://dl.acm.org/doi/10.4018/JGIM.365600
Zheng YHe Y(2024)The Audience Perception to AI Voicebot News: An Experimental AnalysisProceedings of the 2024 8th International Conference on Education and Multimedia Technology10.1145/3678726.3678764(302-308)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3678726.3678764
Bodendorf FFranke J(2024)What is the business value of your data? A multi-perspective empirical study on monetary valuation factors and methods for data governanceData & Knowledge Engineering10.1016/j.datak.2023.102242149:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.datak.2023.102242
Zhang SZhang YLi JNi ZLiu Z(2024)Heart or mind? The impact of congruence on the persuasiveness of cognitive versus affective appeals in debunking messages on social media during public health crisesComputers in Human Behavior10.1016/j.chb.2024.108136154:COnline publication date: 25-Jun-2024
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Lin YWang W(2024)Enhancing students’ online collaborative PBL learning performance in the context of coauthoring-based technologies: A case of wiki technologiesEducation and Information Technologies10.1007/s10639-023-11907-129:2(2303-2328)Online publication date: 1-Feb-2024
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Wais BRinderle-Ma S(2024)Towards a Comprehensive Evaluation of Decision Rules and Decision Mining Algorithms Beyond AccuracyAdvanced Information Systems Engineering10.1007/978-3-031-61057-8_24(403-419)Online publication date: 3-Jun-2024
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Liu CGuo YHsu J(2023)Creating and Validating an Information Quality Scale for E-Commerce PlatformsJournal of Electronic Commerce in Organizations10.4018/JECO.32735021:1(1-28)Online publication date: 31-Jul-2023
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Chen GXiao SZhang CZhao H(2023)A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response PredictionInformation Systems Research10.1287/isre.2022.119634:4(1513-1532)Online publication date: 1-Dec-2023
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