Systematic Mapping of Data Quality in the Public Sector: Dimensions, Metrics and Practices
Abstract
Data quality plays a critical role in ensuring the reliability and effectiveness of information systems in public sector organizations. However, fragmented data ecosystems, legacy systems, and a lack of standardized assessment practices challenge the implementation of consistent data quality strategies. This study presents a comprehensive systematic mapping of data quality dimensions, metrics, practices, and challenges across public institutions. A total of 53 peer-reviewed studies were analyzed, revealing the most frequently used data quality dimensions and associated metrics. The research highlights a wide spectrum of techniques, ranging from traditional validation rules to emerging applications of machine learning for anomaly detection and data imputation. In addition, it identifies key barriers — including limited automation, lack of governance, and scarce resources — and proposes actionable recommendations for public sector entities. The findings serve as a conceptual and practical foundation for improving data quality management in complex government environments, with the ultimate goal of supporting the implementation of data quality assessment strategies in a state-level public finance institution in Brazil, the Ceará State Treasury Department (Sefaz-CE).
Keywords:
Data Quality, Public Sector, Systematic Mapping, Government Data
References
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Bethany L Hedt-Gauthier, Lyson Tenthani, Shira Mitchell, Frank M Chimbwandira, Simon Makombe, Zengani Chirwa, Erik J Schouten, Marcello Pagano, and Andreas Jahn. 2012. Improving data quality and supervision of antiretroviral therapy sites in Malawi: An application of Lot Quality Assurance Sampling. BMC Health Services Research (2012). DOI: 10.1186/1472-6963-12-196
Mohamad Taha Ijab, Ely Salwana Mat Surin, and Norshita Mat Nayan. 2019. Conceptualizing big data quality framework from a systematic literature review perspective. Malaysian Journal of Computer Science (2019). DOI: 10.22452/mjcs.sp2019no1.2
DAMA International. 2017. The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) (2nd ed.). Technics Publications.
Sean Kandel, Ravi Parikh, Andreas Paepcke, Joseph M Hellerstein, and Jeffrey Heer. 2012. Profiler: Integrated statistical analysis and visualization for data quality assessment. In Proceedings of the International Working Conference on Advanced Visual Interfaces. 547–554. DOI: 10.1145/2254556.2254659
Sesillia Fajar Kristyanti, Tien Fabrianti Kusumasari, and Ekky Novriza Alam. 2020. Operational Dashboard Development as A Data Quality Monitoring Tools Using Data Deduplication Profiling Result. In Proceedings - 2020 6th International Conference on Science and Technology, ICST 2020. DOI: 10.1109/ICST50505.2020.9732870
Gómez-Omella Meritxell, Basilio Sierra, and Susana Ferreiro. 2022. On the Evaluation, Management and Improvement of Data Quality in Streaming Time Series. IEEE Access (2022). DOI: 10.1109/ACCESS.2022.3195338
P. Missier, G. Lalk, V. Verykios, F. Grillo, T. Lorusso, and P. Angeletti. 2003. Improving data quality in practice: A case study in the italian public administration. Distributed and Parallel Databases (2003). DOI: 10.1023/A:1021548024224
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Leo L. Pipino, Yang W. Lee, and Richard Y. Wang. 2002. Data quality assessment. Commun. ACM (2002). DOI: 10.1145/505248.506010
Maria Priestley, Fionntán O’donnell, and Elena Simperl. 2023. A Survey of Data Quality Requirements That Matter in ML Development Pipelines. J. Data and Information Quality (2023). DOI: 10.1145/3592616
Arie Purwanto, Anneke Zuiderwijk, and Marijn Janssen. 2020. Citizens’ trust in open government data: a quantitative study about the effects of data quality, system quality and service quality. In Proceedings of the 21st Annual International Conference on Digital Government Research. 310–318. DOI: 10.1145/3396956.3396958
Muhammad Badriansyah Putra, Fahmi Alaydrus, Ira Sulistyowati, Teguh Raharjo, and Riko Wijayanto. 2022. Issues and Challenges of the Data Analytics Development Project in The Center of Information System and Financial Technology. In 2022 1st International Conference on Information System & Information Technology. DOI: 10.1109/ICISIT54091.2022.9872715
Thomas C Redman. 1998. The impact of poor data quality on the typical enterprise. Commun. ACM 41, 2 (1998), 79–82.
Fedri Ruluwedrata Rinawan, Afina Faza, Ari Indra Susanti, Wanda Gusdya Purnama, Noormarina Indraswari, Didah, Dani Ferdian, Siti Nur Fatimah, Ayi Purbasari, Arief Zulianto, Atriany Nilam Sari, Intan Nurma Yulita, Muhammad Fiqri Abdi Rabbi, and Riki Ridwana. 2022. Posyandu Application for Monitoring Children Under-Five: A 3-Year Data Quality Map in Indonesia. ISPRS International Journal of Geo-Information (2022). DOI: 10.3390/ijgi11070399
Mujiono Sadikin, Purwanto S. Katidjan, Arif Rifai Dwiyanto, Nurfiyah, Ajif Yunizar Pratama Yusuf, and Adi Trisnojuwono. 2025. Improving the MSMEs data quality assurance comprehensive framework with deep learning technique. Indonesian Journal of Electrical Engineering and Computer Science (2025). DOI: 10.11591/ijeecs.v37.i1.pp613-626
Tanapat Samakit, Chutiporn Anutariya, and Marut Buranarach. 2023. QUALYST: Data Quality Assessment System for Thailand Open Government Data. In Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering. DOI: 10.1109/JCSSE58229.2023.10202060
Mariutsi Alexandra Osorio Sanabria, Ferney Orlando Amaya Fernández, and Mayda Patricia González Zabala. 2018. Colombian Case Study for the Analysis of Open Data Government: a Data Quality Approach. In ICEGOV ’18, April 4–6, 2018, Galway, Ireland. Association for Computing Machinery. DOI: 10.1145/3209415.3209474
Flavia Serra, Verónika Peralta, Adriana Marotta, and Patrick Marcel. 2024. Use of Context in Data Quality Management: A Systematic Literature Review. J. Data and Information Quality (2024). DOI: 10.1145/3672082
Christian Sillaber, Clemens Sauerwein, Andrea Mussmann, and Ruth Breu. 2016. Data Quality Challenges and Future Research Directions in Threat Intelligence Sharing Practice. WISCS’16, October 24 2016 (2016). DOI: 10.1145/2994539.2994546
Ahmet Soylu, Óscar Corcho, Brian Elvesæter, Carlos Badenes-Olmedo, Francisco Yedro-Martínez, Matej Kovacic, Matej Posinkovic, Mitja Medvešček, Ian Makgill, Chris Taggart, Elena Simperl, Till C. Lech, and Dumitru Roman. 2022. Data Quality Barriers for Transparency in Public Procurement. Information (Switzerland) (2022). DOI: 10.3390/info13020099
Justin St-Maurice and Catherine Burns. 2017. An Exploratory Case Study to Understand Primary Care Users and Their Data Quality Tradeoffs. In J. Data and Information Quality. Association for Computing Machinery. DOI: 10.1145/3058750
Ikbal Taleb, Mohamed Adel Serhani, Chafik Bouhaddioui, and Rachida Dssouli. 2021. Big data quality framework: a holistic approach to continuous quality management. Journal of Big Data (2021). DOI: 10.1186/s40537-021-00468-0
Jaak Tepandi, Mihkel Lauk, Janar Linros, Priit Raspel, Gunnar Piho, Ingrid Pappel, and Dirk Draheim. 2017. The data quality framework for the Estonian public sector and its evaluation: establishing a systematic process-oriented viewpoint on cross-organizational data quality. In Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXV. Springer, 1–26. DOI: 10.1007/978-3-662-56121-8_1
David Tien. 2010. Project management and data quality control. In Proceedings - 2010 IEEE International Conference on Emergency Management and Management Sciences, ICEMMS 2010. DOI: 10.1109/ICEMMS.2010.5563378
Marco Torchiano, Antonio Vetrò, and Francesca Iuliano. 2017. Preserving the Benefits of Open Government Data by Measuring and Improving Their Quality: An Empirical Study. 2017 IEEE 41st Annual Computer Software and Applications Conference (2017). DOI: 10.1109/COMPSAC.2017.192
Strong Diane M Wang, Richard Y. 1996. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems 12, 4 (1996), 5–33. DOI: 10.1080/07421222.1996.11518099
Xinhao Wang, Lulin Xu, Qin Zhang, Da Zhang, and Xiliang Zhang. 2022. Evaluating the data quality of continuous emissions monitoring systems in China. Journal of Environmental Management (2022). DOI: 10.1016/j.jenvman.2022.115081
Claes Wohlin, Per Runeson, Martin Host, Magnus C Ohlsson, Bjorn Regnell, and Anders Wesslen. 2012. Experimentation in Software Engineering (2012 ed.). Springer, Berlin, Germany.
Hongjiang Xu. 2015. What Are the Most Important Factors for Accounting Information Quality and Their Impact on AIS Data Quality Outcomes? ACM Journal of Data and Information Quality (2015). DOI: 10.1145/2700833
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Published
2025-11-04
How to Cite
SILVA, Maria Inês Vale; LUNA, Ramon Nicolas Gomes; CAVALCANTE, Amanda K. B.; LELLI, Valéria; ANDRADE, Rossana M. de Castro; MONTEIRO, José Maria; SANTOS, Ismayle de Sousa.
Systematic Mapping of Data Quality in the Public Sector: Dimensions, Metrics and Practices. In: BRAZILIAN SOFTWARE QUALITY SYMPOSIUM (SBQS), 24. , 2025, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 153-164.
DOI: https://doi.org/10.5753/sbqs.2025.15065.
