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Measuring data quality in information systems research

Yoram Timmerman (UGent) and Antoon Bronselaer (UGent)
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Abstract
Although contemporary research relies to a large extent on data, data quality in Information Systems research is a subject that has not received much attention until now. In this paper, a framework is presented for the measurement of scientific data quality using the principles of rule-based measurement. The proposed framework is capable of handling data quality problems due to both incorrect execution and incorrect description of data collection and validation processes. It is then argued that uncertainty can arise during the measurement, which complicates data quality assessment. The framework is therefore extended to handle uncertainty about the truth value of predicates. Instead of a numerical quality level, data quality is then expressed as either a probability distribution or a possibility distribution over the ordinal quality scale. Finally, it is also shown how quality thresholds can be formulated based on the results of the quality measurement. The usefulness of the proposed framework is illustrated throughout the paper with an example of the construction of a possible survey data quality measurement system and, subsequently, the application of that system on a realistic example.
Keywords
Data quality, Rule-based measurement, Information systems, Uncertainty modelling, MODEL

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Please use this url to cite or link to this publication:

MLA
Timmerman, Yoram, and Antoon Bronselaer. “Measuring Data Quality in Information Systems Research.” DECISION SUPPORT SYSTEMS, vol. 126, 2019, pp. 1–7.
APA
Timmerman, Y., & Bronselaer, A. (2019). Measuring data quality in information systems research. DECISION SUPPORT SYSTEMS, 126, 1–7.
Chicago author-date
Timmerman, Yoram, and Antoon Bronselaer. 2019. “Measuring Data Quality in Information Systems Research.” DECISION SUPPORT SYSTEMS 126: 1–7.
Chicago author-date (all authors)
Timmerman, Yoram, and Antoon Bronselaer. 2019. “Measuring Data Quality in Information Systems Research.” DECISION SUPPORT SYSTEMS 126: 1–7.
Vancouver
1.
Timmerman Y, Bronselaer A. Measuring data quality in information systems research. DECISION SUPPORT SYSTEMS. 2019;126:1–7.
IEEE
[1]
Y. Timmerman and A. Bronselaer, “Measuring data quality in information systems research,” DECISION SUPPORT SYSTEMS, vol. 126, pp. 1–7, 2019.
@article{8634924,
  abstract     = {Although contemporary research relies to a large extent on data, data quality in Information Systems research is a subject that has not received much attention until now. In this paper, a framework is presented for the measurement of scientific data quality using the principles of rule-based measurement. The proposed framework is capable of handling data quality problems due to both incorrect execution and incorrect description of data collection and validation processes. It is then argued that uncertainty can arise during the measurement, which complicates data quality assessment. The framework is therefore extended to handle uncertainty about the truth value of predicates. Instead of a numerical quality level, data quality is then expressed as either a probability distribution or a possibility distribution over the ordinal quality scale. Finally, it is also shown how quality thresholds can be formulated based on the results of the quality measurement. The usefulness of the proposed framework is illustrated throughout the paper with an example of the construction of a possible survey data quality measurement system and, subsequently, the application of that system on a realistic example.},
  articleno    = {113138},
  author       = {Timmerman, Yoram and Bronselaer, Antoon},
  issn         = {0167-9236},
  journal      = {DECISION SUPPORT SYSTEMS},
  keywords     = {Data quality,Rule-based measurement,Information systems,Uncertainty modelling,MODEL},
  language     = {eng},
  pages        = {113138:1--113138:7},
  title        = {Measuring data quality in information systems research},
  url          = {http://dx.doi.org/10.1016/j.dss.2019.113138},
  volume       = {126},
  year         = {2019},
}

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