Metadata

Author(s): Lovász Zoltán

DOI: https://doi.org/10.65513/MaMi.2025.10.56

Publisher: Nemzetközi Oktatási és Kutatási Központ Alapítvány

Volume: October 2025

Volume number: 34

Issue number: 10

Journal: Hungarian Quality Journal

ISSN (Print): ISSN 1416‑9576

ISSN (Online): ISSN 1789-5510

Pages: 56–65

Keywords: prudential data quality, banking regulation, operational resilience

Abstract

Since the 2008 global financial crisis (GFC), the stability of the international and domestic banking system has become less dependent on physical assets and increasingly dependent on the integrity of the information infrastructure. The evolution of the regulatory environment—led by the BCBS 239 principles, the BRRD directive, and the DORA regulation—has elevated data from an administrative by-product to a strategic asset. By synthesizing international literature, relevant EU directives, and specific Hungarian regulatory frameworks (MNB recommendations), this study points out that traditional, static data quality measurement methods are insufficient to meet modern prudential expectations. The article argues for a new diagnostic approach based on neo-institutional organizational theory, agency theory, and resilience planning. The analysis demonstrates that true prudential data quality in the 21st-century Hungarian banking system means not only syntactic accuracy, but also semantic consistency, crisis resilience, 24-hour availability, and the overcoming of technological silos.

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References

  • Admati, A. R., & Hellwig, M. F. (2013). The bankers’ new clothes: What’s wrong with banking and what to do about it. Princeton University Press.
  • Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431
  • Al-Ruithe, M., Benkhelilfa, E., & Hameed, K. (2019). A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 23, 839–859. https://doi.org/10.1007/s00779-019-01247-4
  • Armbrust, M., Ghodsi, A., Xin, R., & Zaharia, M. (2021). Lakehouse: A new generation of open platforms that unify data warehousing and advanced analytics. In Proceedings of the CIDR 2021 Conference.
  • Basili, V. R., Caldiera, G., & Rombach, H. D. (1994). The goal question metric approach. In J. J. Marciniak (Ed.), Encyclopedia of software engineering. Wiley.
  • Baudino, P., Gnezda, J., & Poloni, P. (2018). Bank failure management: The role of deposit insurance and resolution funds (FSI Insights on Policy Implementation No. 8). Bank for International Settlements.
  • Basel Committee on Banking Supervision. (2013). Principles for effective risk data aggregation and risk reporting (BCBS 239). Bank for International Settlements.
  • Binder, J. H. (2016). Bank resolution: The European regime (pp. 29–54). Oxford University Press.
  • Boxenbaum, E., & Jonsson, S. (2017). Isomorphism, diffusion and decoupling: Concept evolution and theoretical challenges. In R. Greenwood et al. (Eds.), The SAGE handbook of organizational institutionalism (pp. 77–101). SAGE Publications.
  • Buneman, P., Khanna, S., & Tan, W. C. (2001). Why and where: A characterization of data provenance. In Proceedings of the International Conference on Database Theory. Springer. https://doi.org/10.1007/3-540-44503-X_1
  • Conway, M. E. (1968). How do committees invent? Datamation, 14(4), 28–31.
  • DAMA International. (2017). DAMA-DMBOK: Data management body of knowledge (2nd ed.). Technics Publications.
  • Dehghani, Z. (2020). Data mesh principles and logical architecture. MartinFowler.com.
  • Dehghani, Z. (2022). Data mesh: Delivering data-driven value at scale. O’Reilly Media.
  • DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
  • European Banking Authority. (2019). Handbook on valuation for purposes of resolution. EBA.
  • EDM Council. (2020). Data management capability assessment model (DCAM) v2.2 user guide. Enterprise Data Management Council.
  • Európai Parlament és a Tanács. (2014). A 2014/59/EU irányelv a hitelintézetek és befektetési vállalkozások helyreállítását és szanálását célzó keretrendszer létrehozásáról.
  • Európai Parlament és a Tanács. (2022). Az Európai Parlament és a Tanács (EU) 2022/2554 rendelete a pénzügyi ágazat digitális működési rezilienciájáról (DORA).
  • Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
  • Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
  • Hollnagel, E. (2011). Resilience engineering in practice: A guidebook. Ashgate Publishing.
  • Inmon, W. H. (2005). Building the data warehouse (4th ed.). John Wiley & Sons.
  • Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
  • Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152. https://doi.org/10.1145/1629175.1629210
  • Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling (3rd ed.). John Wiley & Sons.
  • Laney, D. B. (2018). Infonomics: How to monetize, manage, and measure information as an asset for competitive advantage. Bibliomotion.
  • Luburić, G. (2017). Quality of data in the context of the three lines of defense model. Journal of Central Banking Theory and Practice, 6(1), 37–53. https://doi.org/10.1515/jcbtp-2017-0003
  • MacCormack, A., Baldwin, C., & Rusnak, J. (2012). Exploring the duality between product and organizational architectures: A test of the “mirroring” hypothesis. Research Policy, 41(8), 1309–1324. https://doi.org/10.1016/j.respol.2012.04.011
  • Magyar Nemzeti Bank. (2022). 19/2022. (XII.1.) számú ajánlás a hitelintézeti adatszolgáltatások összeállítási folyamatának kialakításáról, működtetéséről és kontroll funkcióiról.
  • Magyar Nemzeti Bank. (2025). 15/2025. (XII.9.) számú ajánlás a felügyeleti és szanálási célú adatszolgáltatások visszamenőleges módosításáról.
  • Marcella, A. J., & Stucki, C. (2015). Business continuity, risk and audit: The audit of business continuity management. CRC Press.
  • Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340–363. https://doi.org/10.1086/226550
  • Panko, R. R. (1998). What we know about spreadsheet errors. Journal of Organizational and End User Computing, 10(2), 15–25. https://doi.org/10.4018/joeuc.1998040102
  • Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993). Capability maturity model for software, version 1.1. Software Engineering Institute.
  • Simon, H. A. (1957). Administrative behavior: A study of decision-making processes in administrative organization. Macmillan.
  • Sims, C. A. (2003). Implications of rational inattention. Journal of Monetary Economics, 50(3), 665–690. https://doi.org/10.1016/S0304-3932(03)00029-1
  • Single Resolution Board. (2024). Expectations for banks. SRB.
  • Stein, B., & Morrison, A. (2014). The enterprise data lake: Better integration and deeper analytics. PwC Technology Forecast, 1, 1–9.
  • Wand, Y., & Weber, R. (1990). An ontological model of an information system. IEEE Transactions on Software Engineering, 16(11), 1282–1292. https://doi.org/10.1109/32.60316
  • 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
  • Wang, R. Y. (1998). A product perspective on total data quality management. Communications of the ACM, 41(2), 58–65. https://doi.org/10.1145/269012.269022

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