The study, conducted in partnership with Greenwich Associates and sponsored by UBS Asset Management, found that asset managers are at varying stages of their journeys to develop robust advanced analytics (Machine Learning (ML), Natural Language Processing (NLP), and Smart Robotic Process Automation) and alternative data capabilities.
More than half of respondents reported that business stakeholders are satisfied or very satisfied with their data and analytics programs, though most have just begun their journey, which indicates modest expectations for immature programs or that asset managers are significantly overestimating their progress early on.
The four most aggressive managers are investing 67% of the total annual investment made by the entirety of the respondents in advanced analytics. In general, managers spend anywhere from less than $1 million to over $100 million annually with the median skewed all the way down to below $5 million.
Last year, the top three firms reported 33% of total spend among participants on alternative data, ranging from $1 million to $100 million annually, with a median spend falling just below $10 million.
In terms of the application of advanced analytics and alternative data by asset management firms, the study found that 49% have an analytics strategy in place and 63% have one for data.
The survey also found that 49% of respondents are using alternative data for the purposes of alpha generation, on par with 50% last year, while another 17% are in trials and proofs of concept (POCs). This year, 100% of the managers surveyed in the later stages of the journey are using machine learning to some extent and even 9% of the managers at the starting stage are generating value.
Predrag Dizdarevic, co-founder of Element22, said: “This year, by expanding the scope of our survey, we were able to assess a broader cross-section of the industry, revealing increased insight into firms at all phases of their journey in the use of advanced analytics and alternative data. A key finding is that many firms in the early stages overestimate their progress, but even the most advanced asset managers realize they will need to continue to invest heavily to find new ways of generating alpha, and they acknowledge this is a journey without a final destination.”
Thomas Heinzl, Chief Operating Officer, UBS Asset Management, said: “The research underscores the importance of investing in data and advanced analytics to drive efficiencies and process changes, along with the ability to generate returns. We began our analytics program four years ago by focusing on operational improvements, which allowed us to combine our own data with external data sources. By applying machine-learning techniques, including natural language processing, we are able to enhance and augment the work that our portfolio managers and analysts do, demonstrating our ability to leverage technology to drive alpha for our clients.”