<![CDATA[The success of a private bank is significantly dependent on managing the data quality efficiently so that the operations can run effectively, ensure compliance with regulations, and make its customers happy. Having poor data quality can also result in some pretty major monetary losses, operational inefficiencies, or damage to your reputation. This paper explores the application of the OCTAVE Allegro approach within a Supply Chain Data Quality Issue Management System, to deal with these challenges. The use of an information security risk assessment tool such as OCTAVE Allegro enforces a structured method to gather, analyze, and prioritize data quality risks. It details the benefits of its approach — greater risk comprehension, more effective mitigation strategies, and adherence to industry norms. Using this framework, banks can improve decision-making, enforce data governance policies as well as prevent more serious and costlier data-related errors. Implementation challenges such as how to make OCTAVE Allegro applicable to external requirements, and organizational resistance are explored, and this leads to an evaluation of the proposed strategies. In the end, this paper shows that the implementation of OCTAVE Allegro effectively helps private banks construct a safe and trustworthy data ecosystem. The approach enhances how the process supports improved data quality risk management and ultimately success in a growingly data-centered sector.]]>
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