From cloud adoption to cloud advantage

• Cloud has become part of the financial markets operating model, but adoption alone is no longer enough to create advantage.
• Firms need cloud-native market data architectures that support direct access, stronger governance, analytics and AI-ready workflows.
• The shift from file movement to direct data consumption can help reduce operational friction and bring trusted data closer to decision-making.

Cloud is now part of the financial markets operating model. Many institutions have migrated applications, analytics and datasets into cloud environments to improve scale, flexibility and efficiency. But adoption alone does not create advantage. The question has shifted from whether firms use the cloud to whether cloud-native market data architectures are strong enough to support faster decisions, larger datasets, AI-enabled workflows, governance and resilience. Across market data, that distinction matters because trusted pricing and reference data sit at the centre of trading, portfolio management, risk, compliance, operations and reporting. Cloud adoption can change where data sits, but it does not automatically change how data is consumed, governed or made useful. If a firm continues to rely on duplicated storage, repeated movement and manual staging, the operating model may remain complex even after migration. Looking ahead, it becomes less about moving data to the cloud and more about designing cloud-native ways to use it. That means thinking about accessibility, query ability, control, metadata and the ability to support analytics and automation from the outset.

The first phase of cloud transformation was largely about access and migration. Firms moved workloads out of on-premises infrastructure and gained optionality. That work delivered benefits, but it also created a new challenge. Once cloud became mainstream, simply having data available through cloud channels stopped being a meaningful differentiator. Buyers now ask harder questions. Can the data be applied natively within the environment where teams work? Can it support analytics, automation and AI? Can it be governed from the start? Can it reduce operational friction rather than recreate it in a new location? Traditional file-based delivery models can create avoidable complexity. Data may be downloaded, staged, transformed, reconciled and maintained across duplicated infrastructure before it becomes usable. Each step can introduce delays, operational overhead and additional control requirements. This was workable when data consumption was more linear. It becomes more difficult when decision windows compress, data volumes expand and teams expect insight to be available closer to the workflow.

Cloud-native access can bring trusted data closer to where decisions are made. A more modern approach treats data as part of the cloud operating model, not as a package that must be repeatedly moved. When pricing and reference data are embedded within modern cloud environments, users can query them directly, combine them with proprietary or third-party datasets and integrate them into analytics pipelines with less operational overhead. This changes the role of data teams. Instead of spending disproportionate effort on ingestion, staging and reconciliation, teams can focus more energy on the business questions the data is intended to answer. Firms need direction, not more choice. Cloud has opened a wide range of providers, platforms, lakehouse models, APIs, feeds and deployment approaches. Flexibility is valuable, but each architectural decision can have long-term consequences. A firm that recreates fragmented data movement in the cloud may gain hosting flexibility without achieving operating advantage. A firm that designs for native access, governance and analytic readiness can build foundations that scale more effectively across use cases.

DataScope Warehouse is LSEG’s cloud-native pricing and reference data solution. It helps firms access high-quality data within the environments where modern analytics and data workflows take place. It supports direct cloud access and SQL-based querying in cloud platforms such as Snowflake and Google BigQuery, with Databricks availability coming soon. This matters because SQL-based access can reduce dependency on traditional file handling and staging processes, supporting a model where data can be queried and integrated closer to the point of use. The value of cloud-native market data is not only operational. Better access can support analytics, reporting, automation and AI-driven workflows. DataScope Warehouse provides a stronger foundation for analytics, automation, operational resilience and future AI initiatives. That framing is useful because it connects cloud architecture to business outcomes.

Reduced data movement can lower complexity. Direct querying can improve responsiveness. Consistent access can align functions across an organisation. Governance built into the foundation can strengthen trust.

The next cloud question for financial institutions is not, have we moved enough data to the cloud? Instead, it will be: have we built the right operating model for using trusted data in the cloud? Cloud advantage comes from architecture that supports speed, scale, control and intelligence. For market data teams, that means moving beyond migration towards native consumption, machine-ready structures and design choices that reduce friction. Cloud is now table stakes. Advantage must still be built.