Subject. Methods and models for assessing the quality of economic data generated, processed, and used in corporate information systems, including approaches to the formalization and quantitative interpretation of data quality characteristics applied in analytical calculations and management decision?making processes. Objectives. To develop a universal integral indicator of economic data quality that provides a comparable and interpretable assessment of the quality of data used for analytical calculations, forecasting, and support of management decision?making in corporate information systems. Methods. Systems and metric approaches were used; methods of analytical generalization, comparative analysis, as well as formalization and normalization of metric data quality characteristics were applied. Results. Key metric characteristics of economic data quality, including completeness, accuracy, timeliness, and consistency, were identified and formalized. An integral index was developed to aggregate these characteristics into a single quantitative assessment, taking into account their relative significance. The choice of geometric mean aggregation was substantiated; it ensures the sensitivity of the final indicator to a decline in any of the characteristics and reflects the systemic nature of the impact of quality errors. Conclusions and Relevance. The use of an integral data quality index makes it possible to enhance the validity of analytical calculations and management decisions through a formalized, comparable, and interpretable assessment, and also provides a basis for further development of methods for assessing and managing data quality in corporate systems. The results can be applied in corporate information systems, business analytics and decision support systems, financial and economic information systems, as well as data quality monitoring systems.
Liu B., Fang Y., Zhao W., Xu S. Corporate digital transformation and analyst forecast accuracy: Evidence from China. Technology Analysis & Strategic Management, 2025, pp. 1–17. DOI: 10.1080/09537325.2025.2578805
Alabduljabbar A., Alshammari A. The Effect of Data Quality on Decision-Making. A Quasi Experimental Study. Journal of Electrical Systems, 2024, vol. 20, no. 3. DOI: 10.52783/jes.3652 EDN: BTWEFJ
Hassenstein M.J., Vanella P. Data Quality – Concepts and Problems. Encyclopedia, 2022, vol. 2, iss. 1, pp. 498–510. DOI: 10.3390/encyclopedia2010032 EDN: VSEFSR
Carvalho A.M., Soares S., Montenegro J., Conceição L. Data Quality: Revisiting dimensions towards new framework development. Procedia Computer Science, 2025, vol. 253, pp. 247–256. DOI: 10.1016/j.procs.2025.01.088
Priestley M., O'donnell F., Simperl E. A Survey of Data Quality Requirements That Matter in ML Development Pipelines. ACM Journal of Data and Information Quality, 2023, vol. 15, no. 2. DOI: 10.1145/3592616
Serra F., Peralta V., Marotta A., Marcel P. Use of Context in Data Quality Management: A Systematic Literature Review. Journal of Data and Information Quality, 2024, vol. 16, iss. 3. DOI: 10.1145/3672082
Aramburu M.B., Berlanga R., Lanza-Cruz I. A Data Quality Multidimensional Model for Social Media Analysis. Business & Information Systems Engineering, 2023, vol. 66, pp. 667–689. DOI: 10.1007/s12599-023-00840-9 EDN: BROBEX
Tambare P., Meshram Ch., Lee C.-C. et al. Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review. Sensors, 2022, vol. 22, iss. 1. DOI: 10.3390/s22010224 EDN: GFQUXP
Ridzuan F., Zainon W.M.N.W. A Review on Data Quality Dimensions for Big Data. Procedia Computer Science, 2024, vol. 234, pp. 341–348. DOI: 10.1016/j.procs.2024.03.008 EDN: CQMKIB
Saeed M., Adiguzel Z., Shafique I. et al. Big data analytics-enabled dynamic capabilities and firm performance: examining the roles of marketing ambidexterity and environmental dynamism. Business Process Management Journal, 2023, vol. 29, iss. 4, pp. 1463–7154. DOI: 10.1108/BPMJ-01-2023-0015 EDN: LNGXES
Mhlanga D. The role of big data in financial technology toward financial inclusion. Frontiers in Big Data, 2024. DOI: 10.3389/fdata.2024.1184444
Kumar A., Chaudhry M. Review and Analysis of Stock Market Data Prediction Using Data mining Techniques. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021. DOI: 10.1109/ISCON52037.2021.9702498
Vidal-Tomás D. Which cryptocurrency data sources should scholars use? International Review of Financial Analysis, 2022, vol. 81, 102061.
Fu Q., Nicholson G.L., Easton J.M. Understanding data quality in a data-driven industry context: Insights from the fundamentals. Journal of Industrial Information Integration, 2024, vol. 42, 100729. DOI: 10.1016/j.jii.2024.100729
Saisana M., Saltrelli A. Rankings and Ratings: Instructions for Use. Hague Journal on the Rule of Law, 2011, vol. 3, pp. 247–268. DOI: 10.1017/S1876404511200058
Grabisch M., Labreuche C. A Decade of Application of the Choquet and Sugeno Integrals in Multi-criteria Decision Aid. 4OR, 2008, vol. 6, pp. 1–44. DOI: 10.1007/s10288-007-0064-2
Grabisch M., Marichal J.-L., Radko M., Pap E. Aggregation Functions. Cambridge University Press, 2013. DOI: 10.1017/CBO9781139644150
