In recent years, the adoption of AI by leading global asset managers has sparked significant interest across the institutional investor community. The question now arises: How can smaller firms compete, and what realistic opportunities does AI present to them?
Historically, investment managers of all sizes have relied heavily on data-driven approaches, often utilizing Excel spreadsheets and legacy datafeeds to organize and analyze data and extract key findings. The shift from on-premise systems to a modern enterprise data model, such as Snowflake, seems out of reach for many burdened with old data processes. How can they get started and gain access to new AI technologies?
To delve deeper into this topic, we spoke with industry experts Natalie Sendele, Co-CDO of Continuus, and Bryan Lenker, Financial Services Field CTO at Snowflake, to explore how asset managers can embark on this journey, its timeline, and the transformative outcomes achievable through modern data architecture and AI integration. Read on to learn more about some of the key points from that webinar, "Modern Data Architecture for AI with Snowflake."
Evolution of Data Architecture: From Retroactive Deficiency Solution to Proactive Strategic Advantage
Over the years, there has been a transformative shift in data architectures, from merely addressing gaps and deficiencies to proactively identifying strategic outcomes. To realize the true capabilities of a modern data strategy, firms must embrace technological advancements and explore what is now possible instead of just solving immediate problems.
In order for an enterprise data architecture to adapt to and handle innovations like generative AI, it must be built on a foundation of scale, security, and governance. The fundamentals of an enterprise data model – getting data in and out – remain the same, but a modern architecture must emphasize allowing data owners to own their data. Snowflake is on the forefront of modern data tools and technologies, and offers data sharing capabilities with a core advantage of seamless data transfer without the risk of data errors or process failures.
Getting Started with Enterprise Data Architecture: An Incremental Approach
Business alignment and clear use cases are essential drivers for any large-scale architecture project. When planning a data architecture initiative, it's crucial to deliver immediate benefits tied to a customized roadmap. By taking an incremental approach, each milestone is tackled with accelerators, maintaining momentum and ensuring ROI throughout a multi-year project.
A strong starting point for an enterprise data architecture project is identifying data providers that offer Snowflake's data sharing capabilities. Many firms are introduced to Snowflake because it enables seamless data transfer, eliminating the need to replicate processes overnight. This approach offers an immediate win by reducing error-prone batch processing from the outset.
Results from Modern Data Architecture: Tailored Solutions for Sustainable Growth
A modern data architecture offers key benefits like flexibility, speed to market, and accessibility. However, each firm's needs and achievable outcomes will differ, making it essential to tailor solutions and results to the organization. Setting clear expectations is as crucial as the deliverable itself. A modern data architecture strategy should encourage business users to envision use cases beyond current limitations and prepare stakeholders for a future state that may not yet be possible.
Recent innovations in analytics, quantitative research, and native app development have revolutionized data access. While firms have focused on modernizing technology and reducing costs, we have seen a significant rise in AI-related use cases that make data more accessible, even for non-programmatic users. For example, Snowflake enables users to query unstructured data in documents, helping users gain insights from data that has historically been difficult to analyze.
Implementing AI: Enhancing Business Insights and Efficiency
A strong data strategy is essential for successful AI implementation. Without organized and secure data, the benefits of generative AI cannot be fully realized. AI serves as a powerful accelerator and time saver. For example, AI can transform a 100-page datafeed user guide into an interactive Streamlit app for quick and custom access. Efficiency of this kind allows the people within an organization to focus on other knowledge and skills that move the business forward.
That being said, AI should be applied carefully. If data is messy or there are regulatory or legal ramifications to consider, it may not be the best use case. Early experimentation can help identify the best AI use cases for your firm, and a comprehensive data strategy that includes a foundation for scale, security, and governance will best position the organization for future AI capabilities.
Partnering for Success: Leveraging Experienced Partners like Continuus
Migrating to Snowflake and fully leveraging its capabilities can be challenging, and Snowflake experts recommend partnering with service providers like Continuus to optimize the process and accelerate ROI. With 15 years of experience in enterprise data architecture for asset managers, Continuus offers unique industry insights and the expertise needed to ensure impactful results and long-term success.
Natalie and Bryan's insights highlight the tangible benefits of modern data architectures and AI technology. At Continuus, we're dedicated to helping asset managers leverage Snowflake to evolve to a modern data architecture, achieve their specific data and business goals, and unlock greater efficiency for both the individual and the organization.
Start your journey to modern data architecture today; let us guide you toward success.
