By Mick Hittesdorf, OneTick Senior Cloud Architect
The financial services industry is undergoing appreciable operational and technological change, driven by demand for high-quality data and the well-documented benefits offered on the back of cloud-based services.
This whitepaper, based on a 12-question survey of 31 capital markets professionals from a range of firms across the North American, European Union, and UK financial services industries, reveals that real-time and historical tick data are crucial to the majority of respondents, supporting their day-to-day operations and long-term analysis.
Unsurprisingly, firms continue to look for ways to wean themselves off of legacy systems and technologies, instead of opting for RESTful application programming interfaces, cloud-native formats such as Apache Parquet, and analytics tools such as Python and Snowflake. According to the survey, Python is now the dominant programming language, followed by SQL and Java.
This runs true with our day-to-day experience at OneTick and is the major driver for our mission to maximize OneTick interoperability at both the data and API layers.
Some key findings include:
- 42% of respondents work in the traditional asset management industry, while 26% work at hedge funds or funds of funds.
- Real-time data is the most popular data type, selected by 29% of respondents, followed by delayed intraday data, short-term historic data, and medium-term historic data, each selected by 22% of respondents.
- Bespoke Python was the most popular data analytics platform, used by 41% of respondents, followed by Snowflake (36%).
- Cost was the greatest viewed barrier to cloud migration, cited by 28% of respondents, followed by the complexity of moving business processes to the cloud, selected by 26%.
- Respondents see scalability and elasticity, as well as greater availability and choice of datasets, as the two most significant benefits of cloud migration.
While respondents recognize the benefits of scalability, flexibility and broader data access on the back of adopting cloud-based services, they remain cautious due to cost and implementation complexity. Regardless, cloud adoption is reshaping analytics by lowering operational demands. The survey also highlights artificial intelligence’s growing role across the industry, with firms focusing on improving data quality as a means of enhancing AI-driven insights.
The Modern Cloud-centric Analytics Stack for Diverse Requirements
The survey’s findings highlight an industry that is simultaneously diverse in its operational requirements and increasingly unified around a modern, cloud-centric analytics stack. Across firms’ types and regions, respondents demonstrated a need for flexible access to a range of data types, latencies and delivery mechanisms, reflecting the diversity of investment strategies and business functions synonymous with today’s markets.
Despite this diversity, strong consensus emerged around a number of themes. Python— especially bespoke, performance-optimized variants—is clearly the dominant language for developing analytics, valued by users for its transparency, speed of development and rich ecosystem. Cloud-native platforms such as Snowflake and Databricks are similarly well established, enabling firms to store and analyze vast datasets while integrating advanced analytics and AI capabilities.
Saving Costs with Scalability, Additional Datasets, and Faster Time-to-Market
As if the industry needed any more convincing, the survey reinforces the notion that the cloud model will, at some point, be the only model used by capital markets firms to access their technology, data and ancillary services.
While cost concerns, migration complexity and vendor lock-in remain key barriers to overcome, respondents clearly recognize the benefits of scalability, elasticity, access to additional datasets and faster time-to-market.
Crucially, most firms now see the cloud as a direct driver of reduced analytics costs and lower maintenance overheads, while also supporting efficient experimentation and deployment.
The Importance of Data QUALITy with Heightened Demand for AI
Finally, AI is already shaping firms’ analytics strategies, particularly when it comes to alpha generation and ease of access to insights. However, the emphasis respondents place on data quality is a reminder that AI’s value is directly contingent on the quality and trustworthiness of the data it consumes. As a whole, these findings point to an industry converging on cloud-based, AI-enabled analytics where flexibility, efficiency and speed are the key competitive differentiators.
Discover how firms are moving away from legacy technologies and embracing cloud-based tools with the view to improving their all-round operational efficiency and accuracy. Read the full white paper today.
Want to learn more? Register for our upcoming webinar today.
Joining me will be:
Victor Anderson
Global Content Director, WatersTechnology
Peter Ottomanelli
VP & Head of Technology Investment Management and Compliance, American Century Investments
Best wishes,
