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  4. The System of Data Quality Assessment Criteria in Distributed Information Systems

The System of Data Quality Assessment Criteria in Distributed Information Systems

SISN.
2024;
Volume 16
: pp. 191 - 202
Authors:
  1. Yurii Heriak
  2. Andrii Berko
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University, Information Systems and Networks Department

The authors developed a system of criteria for assessing data quality in the context of distributed information systems. The article describes a set of data quality dimensions formulated based on the challenges of data storage and processing in distributed environments. The main objective of the research is to identify the primary requirements and challenges faced by distributed information resources and to satisfy them with specifically selected data quality criteria. A comprehensive analysis of the literature was conducted to identify key data quality dimensions commonly found in most studies. These dimensions include completeness, accuracy, consistency, and timeliness. The article also outlines the main problems encountered when working with data in distributed information systems. Considering the results of the literature review, an attempt was made to formulate a unified set of data quality assessment criteria, which includes accuracy, consistency, completeness, timeliness, accessibility, and other specific data features. Authors emphasize that data quality criteria depend directly on the purpose of the information system and are based on specific requirements. Therefore, this solution represents only a minimum set of characteristics for evaluating data quality in distributed information systems.

data quality assessment
distributed information systems
data quality dimensions
completeness
accuracy
consistency
timeliness
  1. Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14, 2. https://doi.org/10.5334/dsj-2015-002
  2. General Administration of Quality Supervision (2008) Inspection and Quarantine of the People’s Republic of China. Quality management systems-Fundamentals and vocabulary (GB/T19000—2008/ISO9000:2005), Beijing
  3. Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What  Data  Quality  Means  to  Data Consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996. 11518099.
  4. Crosby, P. B. (1980). Quality is free: The art of making quality certain. New American Library.
  5. Scannapieco M., Missier P., Batini C. (2005) Data Quality at a Glance.
  6. Heriak, Y. M., & Berko, A. Y. (2024). Problems of data quality control in distributed information systems. In Status, achievements and prospects of information systems and technologies (p. 98–100). ONTU Publishing House..
  7. Ballou, D. P., & Pazer, H. L. (1985). Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems. Management Science, 31(2), 150–162. https://doi.org/10.1287/mnsc.31.2.150
  8. Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications  of  the ACM, 45(4), 211–218. https://doi.org/10.1145/505248.506010.
  9. Abdouli, M., & Omri, A. (2021). Exploring the nexus among FDI infows, environmental quality, human capital, and economic growth in the Mediterranean region. Journal of the Knowledge Economy, 12(2), 788–810.
  10. Cho, S., Weng, C., Kahn, M. G., & Natarajan, K. (2021). Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study. JMIR mHealth and uHealth, 9(12), Стаття e31618. https://doi.org/10.2196/31618.
  11. Bailey, J. E., & Pearson, S. W. (1983). Development of a Tool for Measuring and Analyzing Computer User Satisfaction. Management Science, 29(5), 530–545. https://doi.org/10.1287/mnsc.29.5.530.
  12. DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60.
  13. Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785–793.
  14. Laudon, K. C. (1986).  Data  quality  and  due  process  in  large  interorganizational  record systems. Communications of the ACM, 29(1), 4–11. https://doi.org/10.1145/5465.5466.
  15. Morey, R. C. (1982). Estimating and improving the quality of information in a MIS. Communications of the ACM, 25(5), 337–342. https://doi.org/10.1145/358506.358520.
  16. Wang, R. Y., Storey, V. C., & Firth, C. P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, 7(4), 623–640.
  17. Feki, C., & Mnif, S. (2016). Entrepreneurship, Technological Innovation, and Economic Growth: Empirical Analysis of Panel Data. Journal of the Knowledge Economy, 7(4), 984–999. https://doi.org/10.1007/ s13132-016-0413-5.
  18. Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11), 86–95. https://doi.org/10.1145/240455.240479.
  19. Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101–113. https://doi.org/10.1016/ j.jsis.2017.10.001.
  20. Ouechtati, I. (2022). Financial inclusion, institutional quality, and inequality: An empirical analysis. Journal of the Knowledge Economy, 1–25. https://doi.org/10.1007/s13132-022-00909-y.
  21. Prifti, R., & Alimehmeti, G. (2017). Market orientation, innovation, and firm performance—an analysis of Albanian firms. Journal of Innovation and Entrepreneurship, 6(1). https://doi.org/10.1186/s13731-017-0069-9.
  22. Berko, A. , Alieksieiev, V. , Lytvyn, V. (2018). Knowledge-based big data cleanup method. CEUR Workshop Proceedings, 2019, 2386, pp. 96–106