RESEARCH PAPER Discovering Data Quality Problems The Case of Repurposed Data Ruojing Zhang • Marta Indulska • Shazia Sadiq Received: 3 October 2017 / Accepted: 28 June 2019 / Published online: 22 July 2019 Ó Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019 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, orga- nizational 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. Keywords Data quality Á Open data Á Design science 1 Introduction In contemporary societies and organizations, data is both a resource and an asset (Fisher 2009). For individual and organizational processes that depend on data, data quality has become a key determinant of the quality of decisions and actions (Stvilia et al. 2007). Poor data quality affects analytical results from Business Intelligence (BI) tools and Data Warehouses and causes severe losses to organizations (English 2009). As a result, in public and private sectors, several related initiatives have been launched, with data quality playing a leading role. Examples include the Data Quality Act enacted by the United States government (OMB 2002) and the Data Quality Assessment Methods and Tools (DatQAM) promoted by the European Com- mission (Ehling and Ko ¨rner 2007). Data quality has been an area of research for over 2 decades (Sadiq et al. 2011), with contributions from com- puter science, statistics, information systems, and respec- tive domain areas such as health, transport and administrative data. It has been widely acknowledged that one cannot manage data quality without first being able to measure it meaningfully (Stvilia et al. 2007). Therefore, discovering the quality of a dataset is a fundamental task in Accepted after two revisions by Matthias Jarke. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12599-019-00608-0) contains sup- plementary material, which is available to authorized users. R. Zhang Á S. Sadiq School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia e-mail: r.zhang3@uq.edu.au S. Sadiq e-mail: shazia@itee.uq.edu.au M. Indulska (&) UQ Business School, The University of Queensland, St Lucia, QLD 4072, Australia e-mail: m.indulska@business.uq.edu.au 123 Bus Inf Syst Eng 61(5):575–593 (2019) https://doi.org/10.1007/s12599-019-00608-0