FINBOURNE Technology, in collaboration with the Financial Times, also hosted a panel on capitalising on the power of data in asset management. Thomas McHugh, FINBOURNE Technology’s co-founder and CEO participated in the session alongside the Chief Data Officers of OFI Asset Management and UBS Asset Management.
Watch the full session here.
On investment data management innovation
Investment innovation often comes from the front-office at many firms and the technologists are responsible for finding the solutions to the problems.
Data consumers within the organisation come across the spectrum from portfolio managers and analysts to operations, compliance, and client reporting. Research roles and the front-office are the heaviest consumers of data, but other parts of the organization downstream from the front-office data requirements also need to be considered and catered for.
Risk, compliance, and operations are on the receiving end of all the data used in the front-office for investment analysis. Investment teams, like sustainability, who make use of newer and proprietary data sets, are also large consumers of data. The question becomes how to cast the widest net to cater for everyone’s requirements.
On innovation enablement, and tooling
Innovation enablement is hard to pinpoint because many analysts and PMs now have knowledge of Python and data science, so the innovation around solutions isn’t necessarily driven by technology teams. The front-office drives a lot of the data requirements, but there is increasing usage across areas like sustainability, risk, and compliance.
The enterprise data model is the limiting factor in proactive data management. A static model cannot cater for all the needs of the business in the new age of data driven processes. The focus of data is beyond maintaining or enhancing STP and is now critical for investment analysis and product differentiation so tools that can flexibly consume, organize, and process disparate and unique data sets are required.
All panellists agreed that researchers and data analysts need better tools to process the growing volume of data, but tooling is only one part of the challenge.
Tooling around data management needs to deliver an accurate view of data provenance and lineage, uniformity of data models, the ability to connect source and downstream systems, and a robust overall data strategy.
The balance between capability development and engagement is also crucial. Developing incredible technical capability does nothing for the business if you can’t engage the people it was designed for. The overall challenges larger firms noted are maintaining the structure of the data, tracking usage, maintaining entitlement, and managing, storing, and querying unstructured data.
