Building a Next Generation Data Architecture

This blog was originally published as part of Citisoft’s Outlook 2022

Every decade or so the asset management industry sets forth on a path to redefine the tools, systems, people, and governance required to serve the data needs of the investment managers, their clients, and the countless providers that feed and service the industry. Just as cloud-based application architectures have become standard practice, cloud-based data architecture is front and center as an industry-wide strategic priority.

For years, the industry has been deploying technology that largely served three main objectives: data storage, data normalization, and data distribution. This technology was deployed in a data multi-tier architecture consisting of ingestion, integration, warehouse, and reporting layers surrounded by bespoke marts and business applications. Based on the pace of change in the industry, these architectures will become legacy by 2025. Significant investment has been made in the ability to intake and store multiple sources data, provide referential and integrity controls, normalize and master data, and provide standard reporting across consumers. However, the industry still struggles with overhead, cost containment, latency, data access, and governance. Most organizations have also ended up with disparate data marts that constrain rather than enable the real-time data access desired by high functioning organizations. Many asset managers are stuck in a fruitless and never-ending governance exercise aimed at providing enterprise-wide data quality, control, and access.

Because of the lingering challenges around all things data, coupled with strategic rationalization and migration of business application architecture and continued momentum towards outsourced services, the next generation of data technology and cloud enablement focuses on providing more seamless integration, computing power, automation, and access to data.

Organizations that have not yet defined and started to execute on a strategic cloud vision for data are already behind the curve. The cost of data continues to increase, and legacy data, integration, and reporting architectures will soon fall short of the tooling the industry needs to manage client demands, complex investment strategies, investor demographics, and regulatory requirements.

The good news is that most leading vendors and service providers are on their way to delivering, and in some cases, already implementing cloud-based solutions for asset managers. Technology and data teams are making decisions about the extensibility of those new capabilities and how internal data strategy and architecture need to be designed to benefit from the enhanced scalability, tooling, and empowerment of analytics. Governance must be revisited with respect to data quality management, security, entitlement, and partnership models. The operating model and skill deployment surrounding data is also increasingly important as reliance and oversight of external counterparties for data accuracy become paramount to creating efficient internal data operations.

Asset managers must also prioritize the evolution of their data architectures with the concurrent modernization of industry solutions. Determining how to migrate from legacy and outdated front office and operational platforms, while also evolving internal data architecture requires significant strategic planning around interdependencies, transition states, and the risks associated with transformational change.

Ultimately, if you subscribe to Darwin’s theory of biological evolution, and apply it to the asset management fintech realm, the evolution of data technology and cloud enablement will indeed lead to natural selection and the extinction of legacy architectures, providers, and potentially managers that don’t evolve.
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