Cloud computing has become a fixture of the financial markets operating model, but according to LSEG Data & Analytics, simply being in the cloud is no longer enough to stand out.
Institutions have spent years moving applications, analytics and datasets into cloud environments to gain scale, flexibility and efficiency. The more pressing question now, LSEG Data & Analytics argues, is whether those cloud-native market data architectures can actually support faster decisions, larger datasets, AI-enabled workflows, governance and resilience.
That distinction carries particular weight in market data. Trusted pricing and reference data underpin trading, portfolio management, risk, compliance, operations and reporting. Moving that data to the cloud changes where it lives, but not necessarily how it is consumed, governed or put to work. Firms that continue to depend on duplicated storage, repeated data movement and manual staging may find their operating models just as complex after migration as before.
LSEG Data & Analytics describes the first phase of cloud transformation as one focused on access and migration. Firms shifted workloads away from on-premises infrastructure and gained optionality. Yet once cloud became mainstream, cloud availability stopped being a meaningful differentiator. Buyers are now asking tougher questions: can data be used natively where teams work, can it support analytics, automation and AI, can it be governed from the outset, and can it reduce operational friction rather than simply relocate it?
Traditional file-based delivery is a key pain point. Data is often downloaded, staged, transformed, reconciled and maintained across duplicated infrastructure before it becomes usable, with each step adding delay, overhead and control requirements. That model worked when consumption was more linear. It struggles as decision windows shrink, data volumes grow and teams expect insight closer to their workflows.
A more modern approach, LSEG Data & Analytics suggests, treats data as part of the cloud operating model rather than a package to be shuttled around. When pricing and reference data sit within modern cloud environments, users can query them directly, blend them with proprietary or third-party datasets and feed them into analytics pipelines with less effort. Data teams can then spend less time on ingestion and reconciliation, and more on the business questions the data is meant to answer.
The firm also cautions that the cloud has created a crowded landscape of providers, platforms, lakehouse models, APIs and feeds. Flexibility is valuable, but every architectural choice has long-term consequences. A firm that recreates fragmented data movement in the cloud may gain hosting flexibility without any real operating advantage.
LSEG’s answer is DataScope Warehouse, its cloud-native pricing and reference data solution. It supports direct cloud access and SQL-based querying in Snowflake and Google BigQuery, with Databricks availability coming soon. By reducing reliance on file handling and staging, it aims to bring data closer to the point of use and provide a foundation for analytics, automation, operational resilience and future AI initiatives.
For LSEG Data & Analytics, the next question for financial institutions is whether they have built the right operating model for using trusted data in the cloud, not how much they have migrated. Cloud is now table stakes. Advantage must still be built.
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