Modern Data Platforms and Asset Management: Trends Shaping the Future

In the dynamic world of finance and asset management , data continues to be a transformative force, shaping the way firms navigate and capitalize on investment opportunities. As the financial industry has evolved, so too has the role of data, with cloud-native, self-service modern data platforms at the forefront of this transformation.

In this blog, we define the qualities of a modern data platform, consider who its users are in asset management, and delve into key trends like data as a product, interoperability, and observability that are shaping the future of modern data platforms in this industry.

What Is a Modern Data Platform, and Who Uses It in Asset Management?

A modern data platform is a versatile solution for asset and investment management companies seeking to harness the power of financial, market, portfolio, risk, and other types of data effectively. It offers a comprehensive range of tools and capabilities that enable users to easily discover and analyze data within the platform, regardless of their technical expertise. Consequently, both tech-savvy and non-technical users can access and utilize data to analyze portfolios, assess risks, model performance, and more.

Modern data platforms cater to two distinct sets of customers: external customers, or end-users, who require personalized experiences, and internal customers who need frameworks, infrastructure, and tools for data access, management, and storage. These platforms provide helpful context for data, such as column descriptions, history, and lineage, allowing users to gain insights with minimal reliance on the data or IT team. Think of it as a GPS for data, guiding members of your firm to the right information without unnecessary detours.

In asset management, modern data platforms serve a diverse range of data consumers, including quant research and trading teams, regulatory reporting, ESG, and asset servicing business units. Each unit has specific use cases that a modern data platform should address, such as risk analysis, portfolio management, and compliance reporting. By providing tailored solutions for each business unit, a modern data platform enables asset management companies to unlock the full potential of their data and confidently achieve their business goals.

Top Technical Qualities of a Modern Data Platform

• Cloud-native infrastructure: A cloud-native infrastructure provides scalability, flexibility, and cost-effectiveness, allowing asset and investment managers to easily scale their data capabilities and ensuring enhanced security and reliability.
• Self-service features: Self-service features empower users to independently access and analyze data, promoting agility, efficiency, and a data-driven culture throughout the organization.
• Data as a product: Treating data as a product enables asset and investment managers to curate, transform, and deliver data in a user-friendly manner, extracting maximum value and driving innovation.
• Open API and integration framework: An open API and integration framework facilitates seamless connectivity and interoperability with other systems, streamlining processes and enhancing the overall effectiveness of data-driven initiatives.

Business Benefits of a Modern Data Platform

• Democratization of data: A modern data platform enables data discoverability, sharing, and collaboration across the organization, making data accessible, understandable, and usable by different users and roles. Users can self-service their data needs without compromising on quality or security.
• Security and enhanced data confidence: Modern data platform architecture provides robust tools and approaches to ensure data security, compliance, and governance, protecting data from unauthorized access, modification, or disclosure. Adhering to relevant laws and regulations increases trust and confidence in data and its usage.
• Cost-effectiveness: It optimizes data costs and performance by allowing users to pay only for the resources and services they use, avoiding expensive hardware investments. The platform can scale capacity and performance to meet changing data needs and demands.
• Innovation: It enables data-driven insights and actions by applying advanced analytics, machine learning, artificial intelligence, and data visualization. Insights can be integrated directly into applications and processes.
• Hyper-connected enterprise: Modern data platform architecture connects data with other systems and platforms within and beyond the organization, exposing data through APIs or other interfaces. Leveraging best practices and solutions from the community or other providers enhances connectivity.
• Gateway to AI/ML: It provides the necessary foundation for implementing generative AI, allowing asset management companies to achieve business value while maintaining data security and governance. Integration of generative AI further enhances the platform's value for asset management companies.

A Note on Build vs. Buy in Asset Management

While many technology companies claim to offer a one-size-fits-all, commercial, off-the-shelf modern data platform, the reality is that such a solution does not exist, especially in the complex environment of asset management. Whether it's AWS, Azure, Google Cloud, Oracle, Snowflake, or any other cloud-based solution, custom software engineering and orchestration are necessary to tailor the platform to your specific strategy and requirements.

Data Trends Shaping the Future of Asset Management

To build a truly modern data platform for asset management, it is crucial to consider the latest trends and their relevance to data management systems in this industry. Here is an overview of the key data trends that are shaping the future of asset management:

Data as a Product / Data as a Service

By adopting a mindset of data ownership and product thinking, asset management companies can treat data as a strategic asset that drives business value. With the concept of data as a product, the focus shifts to user experience and business cases, giving data consumers more consideration.

While many asset managers still rely on classical MS SQL server-based systems, which can be migrated to the cloud, not all prioritize adopting a "tech company" mindset with a product-oriented approach. While data as a product offers attractive benefits, the primary concern for many asset managers is moving away from legacy technology, as it poses a greater threat to productivity.

For larger organizations, scaling up through decentralization and domain orientation can be advantageous. This is where data mesh comes into play. Data mesh is a decentralized data architecture that organizes data by specific business domains, such as marketing, sales, and customer service. This approach grants greater ownership to the producers of each dataset. Similar to the data as a product framework, data mesh enhances data quality, accessibility, interoperability, and usability, thus fostering a data-driven organizational culture.

Open Data Access & Interoperability

Interoperability holds significant advantages for asset managers and the industry as a whole, but the adoption of standards has been historically slow. However, there is a growing trend towards interoperability as regulators push for open access to data, even as vendors continue to operate on different standards.

One major obstacle hindering progress is the lack of consistency in how asset managers describe data in terms of format. However, data access has become more standardized, with APIs enabling collaboration without the need for flat files. Platforms like Snowflake also provide built-in data-sharing mechanisms, acting as marketplaces and distribution hubs. This emphasis on interoperability is driven by the need to quickly utilize new data sources, particularly alternative data, to support investment strategies.

Overall, the trend towards open data access and interoperability is expected to continue as the industry modernizes data exchange from flat files to the cloud and from FTP to APIs.

Observability & Bitemporal Modeling

Asset managers have a growing need to record decisions in a more comprehensive manner, including decision-making factors and forecasts at the time of the decision. This enables them to analyze performance attribution, conduct post-trade analysis, prove best execution, and perform cost analysis.

Traditional platforms often lack observability and the ability to drill down into data with granularity. However, with the use of bitemporal databases, asset managers can address these challenges. Bitemporal databases allow asset managers to go back in time, enabling them to understand how algorithms calculate portfolio value, track data origins and transformation processes, and make informed decisions for the future.

While this problem has not yet been fully solved, the development of more bi-temporal databases will address the need for observability and enable asset managers to have a comprehensive understanding of their data history.

Conclusion

In conclusion, modern data platforms are pivotal in shaping the future of asset management by democratizing data, ensuring security, enhancing data confidence, and optimizing costs. They integrate advanced analytics, machine learning, and artificial intelligence to deliver and scale data-driven insights while accelerating innovation.

By adopting trends like data as a product, data mesh, open data access, interoperability, observability, and bitemporal modeling, asset management firms can enhance their modern data platform capabilities and increase competitive advantage. Importantly, these trends position firms for success, empowering asset managers to treat data as a strategic asset, tailor solutions, streamline collaboration, enhance decision-making, and potentially improve investment performance.

Originally published here .