Examines data strategies to improve quality and transparency in financial institutions, highlighting effective operational models.
Persistent data fragmentation and inconsistent data quality continue to undermine trust, increasing operational risk and cost across capital markets and securities services. This paper examines how financial institutions – banks, asset managers, insurers and service providers – can build an enterprise data foundation that supports faster time-to-market, stronger transparency and scalable artificial intelligence (AI) adoption. It proposes a practical operating model combining clear data ownership, measurable quality management and end-to-end traceability, aligned to business outcomes across core front-to-back processes. The paper also provides implementation guidance on sequencing, governance and change management to convert data strategy into repeatable execution and, where relevant, industrialised data-as-a-service delivery. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Choquet et al. (2026) studied this question.
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