Panel Recap: Alternative Data for Traditional Managers
HFM hosted another great EU Tech Summit this Monday, 22nd of February covering popular and pressing topics with industry experts. FINBOURNE was excited to participate in the panel on Alternative Data for Traditional Managers. We were represented by our Sr. Engineer, Steve Collie who was in great company with Waqar Rashid from Aspect Capital, Tim Mace from Man Group, and Sam Livingstone from Jupiter Asset Management.
The discussion touched on four main data focused themes and there were some great nuggets of advice, best practice, and upcoming trends.
On data sourcing:
The speakers had some very interesting remarks around sourcing data in an era where data sets are growing exponentially, both in terms of quantity and complexity, putting focus on data governance, data quality, and data delivery and distribution at scale:
Platforms and solutions need to meet the needs of the people using them. Complex data users may require more complete solutions including software development kits (SDKs) and centralised access to complex data sets via data virtualisation, whereas simpler data users may just require access to dashboards or data in Excel format.
The growth in popularity of “micro-data” blended with macro factors was noted in reference to understanding the global impact of the COVID pandemic necessitating that even bottom up firms need to take macro factors into consideration.
Many traditional managers, especially the larger and more established ones, have a large on-premises infrastructure investment. New cloud and SaaS solutions are bringing down the total cost of ownership and crucially removing barriers to quickly onboard data providers and large data sets.
On change catalysts and data solutions:
The conversation then moved to which teams in the business are driving change and what they are looking for from data solutions. There was consensus around the group on the drivers, and best practice when evaluating a vendor:
Requirements from the front-office are driving the need for increasing amounts of data and more accurate and efficient ways to derive insight from it.
The best practice that is emerging from data driven managers is to find and apply technologies for specific problems. Single platforms don’t necessarily solve all challenges, so firms are in a constant build versus buy analysis cycle to derive insight from structured and unstructured data sets.
On data marketplaces and services:
The topic of vendor and data marketplaces turned into an interesting discussion around the latest offerings and where value can be found in a sea of commoditised data sets.
Data marketplaces are becoming ever more sophisticated as vendors make data available more easily. The services are essentially commodities now because everyone has access to the same data, so finding unique and complex data sources and being able to derive insights from them is driving Alpha. The advantage of marketplaces is that they are turn-key in nature and many of the issues around normalisation and cleansing are removed with the plug and play model.
The value in data providers and services are in the uniqueness of the offering. If they have a historic set of hard-to-find data, like a data set that has been scraped from websites and is not readily available, then that data set has value because of its unique content and consistent timeline.
On talent sourcing:
The panelists universally agreed that the skillset for a good data analyst starts with a strong engineering background. People that have attention to detail, a structured thought process, and a natural curiosity are at the top of the talent pool. The panel agreed that the engineering background does not have to be in financial services and that some of the best performers are from other sectors completely. The problems investment forms are trying to solve for are generally ones of scale and optimization so flexibility in tools and approach to solutions are also valued.
To find out more about how we solve for alternative data challenges, talk to us today.
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