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Governing Financial Big Data Ecosystems: Data Quality, Statistical Integrity, and Institutional Accountability in Monetary and Financial Statistics
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
The transformation of monetary and financial statistics over the past two decades has been inseparable from the broader evolution of data-intensive infrastructures, institutional transparency mandates, and the growing reliance on big data architectures within central banking and financial regulation. As financial systems have become more complex, interconnected, and digitally mediated, the informational foundations that support monetary policy, prudential supervision, and financial stability oversight have undergone substantial change. This article develops an extensive theoretical and methodological examination of data quality governance within monetary and financial statistical systems, with particular emphasis on the institutional practices and statistical frameworks developed by central banks. Drawing on scholarly debates in data management, data lakes, master data management, and data quality assessment, the study situates monetary and financial statistics as a unique domain in which technical data challenges intersect with legal accountability, confidentiality requirements, and public trust.
The article argues that central banking data infrastructures represent a hybrid epistemic regime that combines traditional statistical rigor with emerging big data paradigms. Within this regime, data quality is not merely a technical attribute but a governance outcome shaped by reporting standards, institutional codes of practice, and evolving accounting frameworks such as International Financial Reporting Standards. Through a qualitative, literature-grounded analytical methodology, the study interprets how changes in publication practices, reporting criteria, and statistical dissemination—exemplified by reforms in monetary and financial statistics—reshape the meaning and usability of financial data for policymakers, researchers, and the public (Bailey, 2014; Bailey and Owladi, 2013). The article further examines how data quality dimensions such as accuracy, completeness, timeliness, consistency, and interpretability acquire distinctive significance in the context of mutually owned financial institutions, credit unions, and other specialized reporting populations governed by central banks.
By integrating perspectives from big data scholarship and data quality frameworks with institutional analyses of central banking statistics, this research contributes a theoretically enriched understanding of how data governance operates in high-stakes financial environments. The findings highlight that improvements in data availability and analytical sophistication do not automatically translate into better decision-making unless accompanied by robust data quality governance, transparent methodological documentation, and sustained institutional accountability (Bank of England, 2013a; Batini et al., 2009). The article concludes by outlining future research directions that bridge financial statistics, data governance theory, and critical data studies, emphasizing the need for interdisciplinary approaches to sustain trust in financial data infrastructures in an era of accelerating digital transformation.
Keywords
References
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