This inaugural study revealed that some asset management firms have reached an inflection point in generating alpha, improving business operations and increasing client acquisition and retention with alternative data and advanced analytics.

The study highlighted that the survey participants are at varying stages of a four-year journey to develop robust alternative data and advanced analytics capabilities. The latter principally includes Machine Learning (ML) and Natural Language Processing (NLP), with Smart Robotic Process Automation (SRPA) largely in trials. Some observations from the study:

• The majority of firms, 55%, are still in the early stages, while 10% have just started their journey. At the other end of the spectrum, only 10% of firms are breaking new ground. This is when firms are generating substantial and sustainable value from their programs.
• Asset managers tend to invest heavily in the beginning to build a data and analytics foundation with ample innovation labs and experimentation. In the middle of the journey, investment declines slightly as the programs focus on areas of success while jettisoning losing propositions. After building business confidence, investment then increases and the fortunate few reach the inflection point of dramatically increased investment and even greater return on investment.
• The asset managers in the middle of their journey plan to increase their investment in alternative data by 240% over the next three years as they start to see benefits in generating alpha and client acquisition.
• The firms who are most aggressively pursuing advanced analytics and alternative data strategies are investing between 2-3% of annual revenue. Of the investments being made by asset managers, measured as a percent of annual revenue, the top three firms are making one-third of the investments.

In terms of the application of alternative data and advanced analytics by asset management firms, the study found:

• Advanced analytics has seen high adoption, with 85% of firms using this capability at some level while 55% are using alternative data.
• 50% of those surveyed are using alternative data for the purposes of alpha generation, while 30% are using such data for client acquisition and retention purposes.
• 95% of asset managers are using or developing advanced analytics to generate alpha, compared to 75% for business operations and 70% for client acquisition and retention.
• ML stood out as the most mature type of advanced analytics solution, with 90% of asset managers deploying it in some way.

Overall, there was no correlation between the size and type of asset manager and their use of advanced analytics and alternative data. Regardless, a successful advanced analytics and alternative data program requires investment in talent. Some firms currently employ as many as 500 people in data and analytics teams and as many as 200 data scientists.

Commenting on the benchmark study, founding partner of Element22, Predrag Dizdarevic, said, "The benchmark study reveals broad-based experimentation with advanced analytics and alternative data across all types of asset managers. The leaders are realizing substantial value from their programs, especially in alpha generation, and we expect this to grow in the coming years. Newcomers should be as aggressive as possible in ramping up their programs, otherwise they risk falling insurmountably behind the leaders which could be a key differentiator in the industry."

Ulrich Koerner, President, UBS Asset Management, said, "Amid an environment of downward pressure on fees, and an increasing shift from active to passive investment strategies, asset managers must find ways to differentiate themselves and remain competitive in the coming years. With more alternative data available than ever before the most successful firms will likely be those that leverage advanced data analytics solutions across their business to generate value for themselves and their clients.”