Tendencias en la gestión de calidad de datos y aplicación de marcos y sus dimensiones: Revisión sistemática de literatura
Trends in data quality management and the application of frameworks and their dimensions: Systematic literature review
DOI:
https://doi.org/10.56712/latam.v6i5.4684Palabras clave:
modelo, evaluación de calidad de datos, marco de trabajo, organización conducida por datosResumen
La gestión de la calidad de datos es crucial en la era digital y se ha consolidado como un factor estratégico en la toma de decisiones. Sin embargo, la multiplicidad de técnicas y marcos existentes dificulta su aplicación y plantea el desafío de seleccionar enfoques adecuados para garantizar estándares óptimos. Este artículo estableció una revisión sistemática de literatura entre el 2019 al 2024, para evaluar los marcos de gestión de calidad de datos y el uso de sus dimensiones. El análisis se desarrolló siguiendo el método PRISMA, resultando 38 artículos indexados en Science Direct, Scopus y ProQuest Central. Los hallazgos evidenciaron la utilización de frameworks, modelos y metodologías aplicados a entornos tecnológicos y de salud entre los más relevantes, siendo las dimensiones de completitud (completeness), exactitud (accuracy), consistencia (consistency) y actualidad (timeliness) las más utilizadas como ejes centrales de evaluación. Asimismo, se identificó a Europa, con Alemania como líder, como la región con mayor producción científica en este campo. Esta investigación aporta una síntesis clara de los marcos y dimensiones más utilizados en la gestión de calidad de datos, facilitando su aplicación práctica y ofreciendo una base comparativa que orienta futuras investigaciones y el desarrollo de nuevos enfoques metodológicos. No obstante, se recomienda avanzar hacia nuevos métodos aplicables en entidades públicas, donde la calidad de datos no solo impacta en la eficiencia administrativa, sino también en la confiabilidad de las decisiones estratégicas y optimización de servicios al ciudadano.
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Abbar, A., & Alshammari, A. (2024). The Effect of Data Quality on Decision-Making. A Quasi Experimental Study. Journal of Electrical Systems. Vol. 20, no. 3, pp. 1552-1566. e-ISSN11125209. https://www.proquest.com/scholarly-journals/effect-data-quality-on-decision-making-quasi/docview/3074172802/se-2
Abdolkhani, R., Gray, K., Borda, A. & Desouza, R. (2023). Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study. JMIR mHealth and uHealth. Vol. 11. PMID: 36826986, PMCID: 10007009. https://doi.org/10.2196/35917
Abuhalimeh, A. (2022). Improving Data Quality in Clinical Research Informatics Tools. Frontiers in Big Data. Vol. 5, 871897. https://doi.org/10.3389/fdata.2022.871897
Ali, A., Emran, N., Asmai, S. & Ismail, A. (2019). An Assessment of Open Data Sets Completeness. International Journal of Advanced Computer Science and Applications(IJACSA). Vol. 10, no. 6. ISSN 2158-107X. http://dx.doi.org/10.14569/IJACSA.2019.0100672
Álvarez, R., Beristain, A., Epelde, G. & Carlin, P. (2019). TAQIH, a tool for tabular data quality assessment and improvement in the context of health data. Computer Methods and Programs in Biomedicine. Vol. 181. ISSN 0169-2607. https://doi.org/10.1016/j.cmpb.2018.12.029
Barba, C., Caballero, I., Varela, A., Cruz, J., Gómez, M. & Navas, I. (2024). BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning. Information and Software Technology. Vol. 167, 107378. ISSN 0950-5849. https://doi.org/10.1016/j.infsof.2023.107378
Batini, C. & Scannapieco, M. (2016). Data Quality Dimensions. In: Data and Information Quality. Data-Centric Systems and Applications. SPRINGER, CHAM. pp. 21-51. ISBN 978-3-319-24104-3. https://doi.org/10.1007/978-3-319-24106-7_2
Camera, F., Erkoyuncu, J. & Wilding, S. (2020). Service Data Quality Management Framework to Enable Through-life Engineering Services, Procedia Manufacturing. Vol. 49, pp. 206-210. ISSN 2351-9789. https://doi.org/10.1016/j.promfg.2020.07.020
Carrizo, C., Caro, A., Salgado, C., Sánchez, A. & Peralta, M. (2021). Una Herramienta para la Definición y Especificación Temprana de Requisitos de Calidad de Datos. Revista Ibérica De Sistemas e Tecnologias De Informação. Pp. 660-674. ISSN 16469895. https://www.proquest.com/scholarly-journals/una-herramienta-para-la-definición-y/docview/2647406592/se-2
Cichy, C. & Rass, S. (2019). An Overview of Data Quality Frameworks. IEEE Access. Vol.7, pp. 24634-24648. https://doi.org/10.1109/ACCESS.2019.2899751
Côrte-Real, N., Ruivo, P. Y Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?, Information & Management. Vol. 57, no. 1. ISSN 0378-7206. https://doi.org/10.1016/j.im.2019.01.003.
Daniel, C., Serre, P., Orlova, N., Bréant, S., Paris, N. & Griffon, N. (2019). Initializing a hospital-wide data quality program. The AP-HP experience., Computer Methods and Programs in Biomedicine. Vol. 181. ISSN 0169-2607. https://doi.org/10.1016/j.cmpb.2018.10.016
Elouataoui, W., El Mendili, S. & Gahi. Y. (2023). An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis. Data. Vol. 8, no. 12. EISSN 2306-5729. https://doi.org/10.3390/data8120182
Elsner, J., Brings, H., Sohnius, F. & Schmitt, R. (2024). Data quality in environmental assessment methods – Implications for the operational management in manufacturing, Procedia CIRP. Vol. 122, pp. 807-812. ISSN 2212-8271. https://doi.org/10.1016/j.procir.2024.01.113
Fang, Z., Liu, Y., Lu, Q., Pitt, M., Hanna, S. & Tian, S. (2022). BIM-integrated portfolio-based strategic asset data quality management. Automation in Construction. Vol. 134. ISSN 0926-5805. https://doi.org/10.1016/j.autcon.2021.104070
Fuentes, I., Bernal, W. & Zapater, J. (2021). Revisión Sistemática de Literatura sobre Modelos de Gobierno y Gestión de Datos corporativos en el contexto de las Organizaciones. Revista Ibérica De Sistemas e Tecnologias De Informação. Pp. 18-34. ISSN 16469895. https://www.proquest.com/scholarly-journals/revisión-sistemática-de-literatura-sobre-modelos/docview/2647405847/se-2
García, F. (2022). Desarrollo de estados de la cuestión robustos: Revisiones Sistemáticas de Literatura. Education in the Knowledge Society (EKS), Vol. 23, e28600. ISSN 2444-8729. https://doi.org/10.14201/eks.28600
Gu, R., Qi, Y., Wu, T., Wang, Z., Xu, X., Yuan, C. & Huang, Y. (2021). SparkDQ: Efficient generic big data quality management on distributed data-parallel computation. Journal of Parallel and Distributed Computing. Vol. 156, pp. 132-147. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2021.05.012
Gualo, F., Rodriguez, M., Verdugo, J., Caballero, I. & Piattini, M. (2021). Data quality certification using ISO/IEC 25012: Industrial experiences. Journal of Systems and Software. Vol. 176. ISSN 0164-1212. https://doi.org/10.1016/j.jss.2021.110938
Guerra, C., Nikiforova, A., Jiménez, S., Perez, H. Ramírez, M. & Ontañon, L. (2023). ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards Data Quality by Design. Data & Knowledge Engineering. Vol. 145. ISSN 0169-023X. https://doi.org/10.1016/j.datak.2023.102152
Günther, L., Colangelo, E., Wiendahl, H-H. & Bauer, C.(2019). Data quality assessment for improved decision-making: a methodology for small and medium-sized enterprises, Procedia Manufacturing. Vol. 29, pp. 583-591. ISSN 2351-9789. https://doi.org/10.1016/j.promfg.2019.02.114
Gürdür, D., El-Khoury, J. & Nyberg, M. (2019). Methodology for linked enterprise data quality assessment through information visualizations, Journal of Industrial Information Integration. Vol. 15, pp. 191-200. ISSN 2452-414X. https://doi.org/10.1016/j.jii.2018.11.002
Herrmann, J., Tackenberg, S., Padoano, E., Hartlief, J., Rautenstengel, J., Loeser, C. & Böhme, J. (2022). An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks, Procedia Computer Science. Vol. 200, pp. 194-204. ISSN 1877-0509. https://doi.org/10.1016/j.procs.2022.01.218
Imran, M. & Ahmad, A. (2021). Enhancing data quality to mine credible patterns. Journal of Information Science. Vol. 49, no. 2, pp. 544 – 564. https://doi.org/10.1177/01655515211013693
International Organization for Standardization – ISO (2008). ISO/IEC 25012: Software Engineering: Software Product Quality Requirements and Evaluation (SQuaRE): Data Quality Model. ISO/IEC. https://www.iso.org/standard/35736.html
Kang, J., Al Masry, Z., Varnier, C., Mosallam, A. & Zerhouni, N. (2024). A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry, Engineering Applications of Artificial Intelligence. Vol. 136, Part. A. ISSN 0952-1976. https://doi.org/10.1016/j.engappai.2024.108834
Krishna, C., Ruikar, K. & Jha, K. (2023). Determinants of Data Quality Dimensions for Assessing Highway Infrastructure Data Using Semiotic Framework. Buildings. Vol. 13, no. 944. EISSN 2075-5309. https://doi.org/10.3390/buildings13040944
Labeeb, K., Chowdhury, K.., Riha R., Abedin, M., Yesmin, S. & Khan, M. (2020). Pre-Processing Data in Weather Monitoring Application by Using Big Data Quality Framework. Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020. Pp. 284 - 287. ISBN 978-1-6654-3027-2. https://doi.org/10.1109/WIECON-ECE52138.2020.9397990
Lettner, C., Stumptner, R., Fragner, W., Rauchenzauner, F. & Ehrlinger, L. (2021). DaQL 2.0: Measure Data Quality based on Entity Models. Procedia Computer Science. Vol. 180, pp. 772-777. ISSN 1877-0509. https://doi.org/10.1016/j.procs.2021.01.327
Linares, E., Hernández, V., Domínguez, J., Fernández, S., Hevia, V., Mayor, J., Padilla, B. & Ribal, M. (2018). Methodology of a systematic review, Actas Urológicas Españolas (English Edition). Vol. 42, no. 8, pp. 499-506. ISSN 2173-5786. https://doi.org/10.1016/j.acuroe.2018.07.002
Makeleni, N. & Cilliers, L.(2021). Critical success factors to improve data quality of electronic medical records in public healthcare institutions. South African Journal of Information Management. Vol. 23, no. 1. https://doi.org/10.4102/sajim.v23i1.1230
Mao, X., Gong, B., Su, F., Xu, K., Xian, K., Liu, D. & Guo, H. (2019). Data Quality Management and Measurement. Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering. Singapore, Springer. Vol. 494, pp. 229–236. ISBN 978-981-13-1732-3. https://doi.org/10.1007/978-981-13-1733-0_28
Nagle, T., Redman, T. & Sammon, D. (2020). Assessing data quality: A managerial call to action, Business Horizons. Vol. 63, no. 3, pp. 325-337. ISSN 0007-6813. https://doi.org/10.1016/j.bushor.2020.01.006
Naik, S., Voong, S., Bamford, M., Smith, K., Joyce, A., & Grinspun, D. (2020). Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index. Journal of the American Medical Informatics Association : JAMIA. Vol. 27, No. 5, pp. 776–782. EISSN 1527-974X. https://doi.org/10.1093/jamia/ocaa031
Ngueilbaye, A., Huang, J., Khan, M. & Wang, H. (2023). Data quality model for assessing public COVID-19 big datasets. Journal of Supercomputing. Vol. 79, no. 17, pp. 19574 - 19606. https://doi.org/10.1007/s11227-023-05410-0
Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S. & Zerhouni, N. (2021). Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications. Computers in Industry. Vol. 127, 103414. ISSN 0166-3615. https://doi.org/10.1016/j.compind.2021.103414
Pezoulas, V. Kourou, K., Kalatzis, F., Exarchos, T., Venetsanopoulou, A., Zampeli, E., Gandolfo, S., Skopouli, F., De Vita, S., Tzioufas, A. & Fotiadis, D. (2019). Medical data quality assessment: On the development of an automated framework for medical data curation, Computers in Biology and Medicine. Vol. 107, pp. 270-283. ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2019.03.001
Pipino, L., Lee, Y., & Wang, R. (2002). Data quality assessment. Communications of the ACM, 45(4), 211–218. https://doi.org/10.1145/505248.506010
Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.
Rajan, N., Gouripeddi, R., Mo, P., Madsen, R. & Facelli, J. (2019). Towards a content agnostic computable knowledge repository for data quality assessment. Computer Methods and Programs in Biomedicine. Vol. 177, pp. 193-201. ISSN 0169-2607. https://doi.org/10.1016/j.cmpb.2019.05.017
Rau, C., Lüdecke, D., Dumolard, L., Grevendonk, J., Wiernik B.M., Kobbe, R., Gacic-Dobo, M. & Danovaro, M. (2022). Data quality of reported child immunization coverage in 194 countries between 2000 and 2019. PLOS Global Public Health. Vol. 2, no. 2. https://doi.org/10.1371/journal.pgph.0000140
Ridzuan, F. & Zainon, W. (2024). A Review on Data Quality Dimensions for Big Data. Procedia Computer Science. Vol. 234, pp. 341-348, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2024.03.008
Salazar, M. & Sossa, E. (2015). Estudio bibliométrico sobre la producción bibliográfica y científica en archivística, desde la perspectiva de las bases de datos Library and Information Science y Science Direct. [Tesis de maestría, Universidad de La Salle, Bogotá]. https://ciencia.lasalle.edu.co/cgi/viewcontent.cgi?article=1011&context=maest_gestion_documental
Salem, S. & Benchikha, F. (2022). LODQuMa: A Free-ontology process for Linked (Open) Data quality management. Journal of King Saud University - Computer and Information Sciences. Vol. 34, no.8, Part. A, pp. 5552-5563. ISSN 1319-1578. https://doi.org/10.1016/j.jksuci.2021.06.001
Schuh, G., Rebentisch, G., Riesener, M., Ipers, T., Tönnes, C. & Jank, M-H.(2019). Data quality program management for digital shadows of products, Procedia CIRP. Vol. 86, pp. 43-48. ISSN 2212-8271. https://doi.org/10.1016/j.procir.2020.01.027
Sdiri, B., Rigaud, L., Jemmali, R. & Abdelhedi, F.(2023). The Difficult Path to Become Data-Driven. SN Computer Science. SCI. Vol. 4, 385. https://doi.org/10.1007/s42979-023-01789-y
Terry , A., Stewart, M., Cejic, S., Marshall, J., De Lusignan S., Chesworth, B., Chevendra, V., Maddocks, H., Shadd, J., Burge, F. & Thind, A. (2019). A basic model for assessing primary health care electronic medical record data quality. BMC Med Inform Decision Making. Vol. 19, no. 30, pp. 19-30. ISSN 1472-6947. https://doi.org/10.1186/s12911-019-0740-0
Timmerman, Y., Nasfi, R., De Tré, G., Pattyn, F. & Bronselaer, A. (2023). Cost-based analysis of the impact of data completeness and representational consistency, Decision Support Systems. Vol. 175. ISSN 0167-9236. https://doi.org/10.1016/j.dss.2023.114044
Tobalina, L. & González, C. (2020). Valoración de la calidad de los datos arqueológicos a través de la gestión de su vaguedad. Aplicación al estudio del poblamiento tardorromano. Complutum. Vol. 31 no. 2, pp. 343-360. ISSN 1131-6993. https://doi.org/10.5209/cmpl.72488
Valencia, A., Parody, A., Varela, A., Caballero, I. & Gómez, M. (2021). DMN4DQ: When data quality meets DMN. Decision Support Systems. Vol. 141. ISSN 0167-9236. https://doi.org/10.1016/j.dss.2020.113450
Valverde, C., Marotta, A., Panach, J. & Vallespir, D. (2022).Towards a model and methodology for evaluating data quality in software engineering experiments. Information and Software Technology. Vol. 151. ISSN 0950-5849. https://doi.org/10.1016/j.infsof.2022.107029
Wahyudi, T. & Isa, S. (2023). Data Quality Assessment using TDQM framework: A case study of PT AID FRAMEWORK: A CASE STUDY OF PT AID. Journal of Theoretical and Applied Information Technology. Vol. 101, no. 9, pp. 3576 - 3589. ISSN 1992-8645. https://www.jatit.org/volumes/Vol101No9/31Vol101No9.pdf
Wang, R., & Strong, D. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems. Vol. 12, no. 4, pp. 5–33. https://doi.org/10.1080/07421222.1996.11518099
Weiskopf, N. & Weng, Ch. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research, Journal of the American Medical Informatics Association, Volume 20, Issue 1, Pages 144–151. https://doi.org/10.1136/amiajnl-2011-000681
Williams, P., Lovelock, B., Cabarrus, T. & Harvey, M. (2019). Improving Digital Hospital Transformation: Development of an Outcomes-Based Infrastructure Maturity Assessment Framework, JMIR Med Inform. Vol. 7, no. 1: e12465. PMID: 30632973, PMCID: PMC6329893. https://pubmed.ncbi.nlm.nih.gov/30632973/
Wook, M., Hasbullah, N., Zainudin, N., Jabar, Z., Ramli, S., Razali N. & Yusop, N. (2021). Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling. Journal of Big Data. Vol. 8, no. 1. ISSN 2196-1115. https://doi.org/10.1186/s40537-021-00439-5
Zhang, L., Jeong, D. & Lee, S. (2021). Data quality management in the internet of things. Sensors. Vol. 21, 17,5834. https://doi.org/10.3390/s21175834
