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Discovering Data Quality Problems

The Case of Repurposed Data

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Abstract

Existing methodologies for identifying data quality problems are typically user-centric, where data quality requirements are first determined in a top-down manner following well-established design guidelines, organizational structures and data governance frameworks. In the current data landscape, however, users are often confronted with new, unexplored datasets that they may not have any ownership of, but that are perceived to have relevance and potential to create value for them. Such repurposed datasets can be found in government open data portals, data markets and several publicly available data repositories. In such scenarios, applying top-down data quality checking approaches is not feasible, as the consumers of the data have no control over its creation and governance. Hence, data consumers – data scientists and analysts – need to be empowered with data exploration capabilities that allow them to investigate and understand the quality of such datasets to facilitate well-informed decisions on their use. This research aims to develop such an approach for discovering data quality problems using generic exploratory methods that can be effectively applied in settings where data creation and use is separated. The approach, named LANG, is developed through a Design Science approach on the basis of semiotics theory and data quality dimensions. LANG is empirically validated in terms of soundness of the approach, its repeatability and generalizability.

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Fig. 1

Source: Sadiq and Indulska (2017), data available from https://catalog.data.gov

Fig. 2

Source: Peffers et al. (2007)

Fig. 3

Source: Authors, informed by Peffers et al. (2007)

Fig. 4

Source: Authors

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Notes

  1. The researchers named the approach as ‘LANG’ – ‘Lang’ conveys the meaning of ‘becoming clear’ in the Chinese language, which fits with the aim of the approach, that is, to make clear the data quality requirements of a dataset.

  2. The mapping is omitted due to length considerations but is available from the authors upon request.

  3. The download period is between June and August 2016. We note that the datasets are frequently updated in the respective open data portals including change of meta-data, such as adding or removing columns as well as providing or removing other documentation related to the dataset. Hence, the current versions of the datasets may not have the same data quality problems as those identified in our study.

  4. In this paper we have demonstrated the application of LANG with the help of relational database (MySQL). We present the overall approach in the body of the paper, and present the SQL instantiation of the method in Appendix A.

  5. Some detail is abstracted in this figure for visual simplicity; in particular sequences between some of the individual checks, which may result in skipping certain checks/stages (as relevant on the basis of analysis results).

  6. “The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and much more.” (jupyter.org).

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, 4072, Australia

    Ruojing Zhang & Shazia Sadiq

  2. UQ Business School, The University of Queensland, St Lucia, QLD, 4072, Australia

    Marta Indulska

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  1. Ruojing Zhang
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  2. Marta Indulska
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  3. Shazia Sadiq
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Correspondence to Marta Indulska.

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Accepted after two revisions by Matthias Jarke.

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Zhang, R., Indulska, M. & Sadiq, S. Discovering Data Quality Problems. Bus Inf Syst Eng 61, 575–593 (2019). https://doi.org/10.1007/s12599-019-00608-0

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