Integrated data quality management within the organization
https://doi.org/10.55648/1998-6920-2025-19-4-28-47
Abstract
The relevance of integrated data quality management tasks is increasing in the context of growing volume, variety, and criticality of data used. Despite this, organizations still have significant gaps in understanding the interconnections between data quality, process quality, and information systems. The purpose of this study is a systematic analysis of existing methodologies and concepts for data quality management, as well as the identification of key challenges in their practical implementation. The paper presents a review of scientific literature, outlines standard elements and a framework for an integrated approach to data quality. A high-level data flow scheme based on the Data Lakehouse architecture has been developed, reflecting the interaction of system components. The necessity of developing new methods and algorithms for optimizing big data quality, which go beyond traditional paradigms focused on structured data, is substantiated. Key problems often ignored in practice are identified and systematized, and criteria for the successful implementation of an integrated data quality management approach are formed.
About the Authors
Oksana Igorevna Zakharova
Povolzhskiy State University of Telecommunications and Information Science (PSUTI)
Russian Federation
Russian Federation
PhD. (Engineering), Associate Professor; Department of Information Systems and Technologies, Povolzhskiy State University of Telecommunications and Information Science
Vlad Sergeevich Korobeynikov
Povolzhskiy State University of Telecommunications and Information Science (PSUTI)
Russian Federation
Russian Federation
PhD student, Povolzhskiy State University of Telecommunications and Information Science
References
1. Sheng Y. Exploring the mediating and moderating effects of information quality on firm's endeavour on information systems // International Conference on Information Quality (ICIQ 2003). Cambridge, Massachusetts, USA, November 7-9, 2003. P.252–355.
2. Wang R. A product perspective on total data quality management [Электронный ресурс]. URL: https://dl.acm.org/doi/10.1145/269012.269022 (дата обращения: 21.09.2025).
3. Su Y., Jin Z. A methodology for information quality assessment in the designing and manufacturing processes of mechanical products // International Conference on Information Quality (ICIQ 2004). Cambridge, Massachusetts, USA, November 5-7, 2004. P. 447-465.
4. Kyung-Seok Ryu, Joo-Seok Park, Jae-Hong Park A Data Quality Management Maturity Model // ETRI Journal. 2006. № 28. P. 191–204.
5. Batini C., Scannapieco M. Data Quality: Concepts, Methodologies and Techniques [Электронный ресурс]. URL: https://dl.acm.org/doi/10.5555/1177291 (дата обращения: 21.09.2025).
6. C. Batini, C. Cappiello, C. Francalanci, A. Maurino Methodologies for data quality assessment and improvement // ACM Computing Surveys. 2009. № 41. P. 1–52.
7. Jeusfeld M., Quix C., Jarke M. Design and analysis of quality information for data warehouses // International Conference on Conceptual Modeling. Berlin, Heidelberg, Germany, November 16-19, 1998. P. 349–362.
8. Chapman A., Richards H., Hawken S. Data and information quality at the Canadian institute for health information [Электронный ресурс]. URL: http://mitiq.mit.edu/ICIQ/Documents/IQ%20Conference%202006/Papers/Data%20and%20Information%20Quality%20at%20the%20Canadian%20Institute%20for%20Health%20Information.pdf (дата обращения: 20.09.2025).
9. Eppler MJ., Muenzenmayer P. Measuring Information Quality in the Web Context: A Survey of State-of-the-Art Instruments and an Application Methodology [Электронный ресурс] URL: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b7e04978992851255d26fd8a00b6673ea9f27f84 (дата обращения: 20.09.2025).
10. Falorsi P., Pallara S., Pavone A., Alessandroni A., Massella E., Scannapieco M. Improving the quality of toponymic data in the italian public administration. In Proceedings of the ICDT Workshop on Data Quality in Cooperative Information Systems (DQCIS) [Электронный ресурс] URL: https://www.istat.it/ (дата обращения: 20.09.2025).
11. Su Y. and Jin Z. A methodology for information quality assessment in the designing and manufacturing processes of mechanical products // International Conference on Information Quality (ICIQ 2004). Cambridge, Massachusetts, USA, November 5-7, 2004. P. 447–465.
12. Loshin, D. Enterprise Knowledge Management – The Data Quality Approach [Электронный ресурс] URL: https://dl.acm.org/doi/10.5555/362436 (дата обращения: 20.09.2025).
13. Scannapieco M., Virgillito A., Marchetti M., Mecella M., Baldoni R. The DaQuinCIS architecture: a platform for exchanging and improving data quality in Cooperative Information Systems // Inform. Syst. 2004. № 29. P. 551–582.
14. De Amicis F., Batini C. A methodology for data quality assessment on financial data [Электронный ресурс]. URL: https://cir.nii.ac.jp/crid/1570291224348968704 (дата обращения: 20.09.2025).
15. The Global Data Management Community [Электронный ресурс]. URL: https://www.dama.org/cpages/body-of-knowledge (дата обращения: 20.09.2025).
16. International organization for standardization [Электронный ресурс]. URL: https://www.iso.org/standard/81745.html (дата обращения: 20.09.2025).
17. International organization for standardization, International electrotechnical commission [Электронный ресурс]. URL: https://www.iso.org/standard/78914.html (дата обращения: 20.09.2025).
18. International organization for standardization, International electrotechnical commission [Электронный ресурс]. URL: https://www.iso.org/standard/34343.html (дата обращения: 20.09.2025).
19. International organization for standardization, International electrotechnical commission [Электронный ресурс]. URL: https://www.iso.org/ru/standard/35736.html дата обращения: 20.09.2025).
20. International organization for standardization, International electrotechnical commission [Электронный ресурс]. URL: https://www.iso.org/standard/81088.html (дата обращения: 20.09.2025).
21. Центр компетенций НТИ по большим данным МГУ [Электронный ресурс]. URL: https://bigdata.msu.ru/standards/ (дата обращения: 20.09.2025).
22. Monte Carlo Data [Электронный ресурс]. URL: https://www.montecarlodata.com/blog-data-quality-testing/ (дата обращения: 20.09.2025).
23. Monte Carlo Data [Электронный ресурс]. URL: https://www.montecarlodata.com/use-cases/data-quality-monitoringtesting/ (дата обращения: 20.09.2025).
24. Monte Carlo Data [Электронный ресурс]. URL: https://www.montecarlodata.com/product/data-observabilityplatform/ (дата обращения: 20.09.2025).
25. Pure Storage Blog [Электронный ресурс]. URL: https://blog.purestorage.com/purely-educational/data-fabric-vsdata-lake-vs-data-warehouse/ (дата обращения: 20.09.2025).
26. Ofner M., Otto B., Osterle H. Integrating a data quality perspective into business process management // Business Process Management Journal. 2012. № 18. P. 1036-1067.
27. Al-Sai Z., Gandomi A., Al-Sai Z., Al-Nuaimi E., Al-Jaroodi J., Mohamed N., Al-Neyadi H., Al-Bayati A., Al-Kahtani M. Big Data Maturity Assessment Models: A Systematic Literature Review // Big Data Cognition and Computation. 2023. № 7. P. 1-27.
28. The DGI Data Governance [Электронный ресурс]. URL: https://datagovernance.com/ (accessed: 20.09.2025).
29. IBM Data Governance Framework [Электронный ресурс]. URL: https://www.ibm.com/products/cloud-pak-for-data/governance (дата обращения: 20.09.2025).
30. Heidi C., Nikiforova A. Towards augmented data quality management: Automation of Data Quality Rule Definition in Data Warehouses [Электронный ресурс]. URL: https://arxiv.org/abs/2406.10940 (дата обращения: 20.09.2025).
31. Hazen B., Boone C., Ezell J., Jones-Farmer L. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications // International Journal of Production Economics. 2014. № 154. P. 72-80.
32. Leghemo I., Osinachi D., Chinekwu S., Chima A. Continuous Data Quality Improvement in Enterprise Data Governance: A Model for Best Practices and Implementation // Engineering Research and Reports. 2025. № 27. P. 29-25.
33. Taleb I., Serhani M., Bouhaddioui C., Dssouli R. 2021. Big data quality framework: a holistic approach to continuous quality management // Journal of Big Data. 2021. № 8. P. 1–41.
34. Bello H., Ige A., Ameyaw M. Deep learning in high-frequency trading: conceptual challenges and solutions for real-time fraud detection // World Journal of Advanced Engineering Technology and Sciences. 2024. № 12, P. 35-46.
35. Cappiello C., Cerletti C., Fratto C., and Pernici B. Validating data quality actions in scoring processes. Journal of Data and Information Quality (JDIQ). 2018. № 9. P. 1–27
Review
For citations:
Zakharova O.I., Korobeynikov V.S. Integrated data quality management within the organization. The Herald of the Siberian State University of Telecommunications and Information Science. 2025;19(4):28-47. (In Russ.) https://doi.org/10.55648/1998-6920-2025-19-4-28-47
Views:
209
JATS XML
Email this article 
