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/.
Choquet et al. (2026) studied this question.