Data for Investment Managers: Evolution or Revolution?

The investment management industry knows that harnessing the power of data can generate greater value for the firm and drive competitive advantage. Investment managers must prioritise the most valuable use cases, master data governance, technology architecture and business efficiency to unlock actionable insights, which is a significant technical challenge.

Recently, at the 8th Investment Data & Technology Summit, hosted by Fund Business, we discussed the challenge of managing the evolving role of data and technology in investment management with fellow industry leaders. We explored how to cut-through the hype, leverage disruptive new influences such as artificial intelligence (AI), and harness the power of data and technology for growth.

Navigating Trends and Hypes: Transformation in Practice

We discussed the practicalities of building an infrastructure capable of delivering on the promise of data. Different types of architectural constructs – data lakes, lake houses, vaults and mesh or fabric – come with their own pros and cons. However, investment management firms don’t have to embrace only one approach – hybrid arrangements can work well.

Importantly, infrastructure and governance must be inclusive. Building technical structures requires a pool of talented engineers, working closely with investment teams to build a data system that delivers purposeful insights. Having an intermediary who is able to articulate the investment teams’ requirements to technologists may assist in minimising the time to business value.

There is also widespread recognition that not all data is equal. Identifying the most important data – and building structure around it – is a critical task. Following this, determining if the remaining data may still have value across the organisation is important as well. Firms must also have the flexibility to rapidly assess and secure new sources of data that could provide insights and advantage.

AI technologies such as large language models (LLM) and natural language processing (NLP) have much to offer, but identifying the right use cases, rather than falling for the hype, is crucial.

Applications of these advances already in use include solutions that ensure insights secured from data is accurate, complete and reliable. For example, machine learning can be used to flag anomalies in data as part of a broader data verification program. Sentiment analysis is another area of significant interest as well as using AI tools to increase productivity.

Foundations for Data Architecture

The challenge is to build a data architecture that users can trust and include solutions that ensure insight is reliable. Implementing architecture that makes it easier to track and manage data consumption patterns will make it easier for users to access the right data to solve key business problems.

To achieve this, investment management firms need a long-term vision for the evolution of their architecture, a robust implementation strategy and supporting governance framework.

In an area that is evolving rapidly, it will be important to move forward with agility. Data architects must be able to respond to immediate business needs while staying focused on longer-term strategic objectives. As a result, legacy technologies may no longer be fit-for-purpose. While many traditional data solutions provide core capabilities such as aggregation, they struggle to deal with unstructured data. Open API standards have much to offer here. Cloud native architectures help to improve resilience, agility and scalability, which support productivity, efficiency and the adoption of new initiatives in areas such as AI.

Data Centralisation

Many investment management firms are struggling with the issue of data centralisation. Decentralisation has the potential to create an additional data management burden, but centralised platforms that pull in data and then deliver information to data consumers is resource intensive.

Nevertheless, a centralised platform can deliver economies of scale, as a single, enterprise-wide source of truth. The solution may be a cloud-native data warehouse built on an extensible and scalable model to capture the breadth and depth of data generated across the organisation, as well as from external sources. In addition, enterprise data management platforms provide interoperability, lineage and governance, regardless of how data is used.

Leveraging Partners

Suppliers, vendors and other partners will play a key role in helping investment management businesses build the data architectures they need now and into the future. However, many businesses want fewer but deeper partnerships with the aim of simplifying their operating environments. Those partners will also need to have strong relationships with one another to reduce operational friction and support an architecture capable of providing an enterprise-wide single source of truth.

State Street’s Alpha^® Data Platform is fully open and interoperable, enabling the integration of both client data and third-party sources. It provides real-time data with transparency into data availability, definitions, usage and lineage, along with the scale and flexibility to meet the demands of today’s shifting markets.

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