Having just returned from yet another extremely productive Jefferies’s BattleFin conference in Miami, our team was reflecting on several themes we observed occurring in the financial services and hedge fund artificial intelligence (“AI”) market. With 470 attendees, the Jefferies BattleFin conference is the premier event to observe, hear, and meet with experts related to alternative data and AI for hedge funds. The conference itself continues to grow in size, and in the diversity of data providers and technology services relevant to the technology-driven investor. We were excited to participate in a panel discussion yet again this year about a topic we have a high degree of conviction and experience, and it was extremely productive to meet with so many new and familiar industry experts.
An overview of these themes from this year’s event include:
- Increased acceptance of Data-Engineering-as-a-Service using qualified third-party technology partners like B23
- Machine Learning at-scale is becoming more tightly coupled to Public Cloud infrastructure
- More pragmatism around the amount of alpha that alternative data can provide by itself
- Challenges to adopting new, innovative ideas with so much turnover and cross-pollination
Data-Engineering-as-a-Service for Hedge Funds
An emerging theme that was very prevalent this year was the acceptance of outsourced data engineering or Data-Engineering-as-a-Service. Many of the institutions we spoke with are quickly aligning themselves to this trend, which is consistent with our observations also occurring in non-financial services verticals as well. It was obvious that more and more institutions continue to pursue a strategy to outsource the “undifferentiated heaving lifting” of data engineering in order for those same firms to focus on higher order outcomes with respect to quantitative investment analysis.
Funds are increasing passing on building themselves cloud-based data lakes, or developing durable and performant extract, transform, and load (“ETL”) applications hosted on distributed processing engines. Instead, they are turning to more qualified trusted technology partners. The result from utilizing Data-Engineering-as-a-Service is a familiar SQL-based dashboard such as Tableau, custom-data API’s, or a smaller more feature-rich data set that can be modeled using Excel or Jupyter notebook on a commodity laptop.
Focus on Core Competencies
As any business and management consultancy will advise its clients on day-one, focusing on core-competency and outsourcing undifferentiated tasks, especially when those undifferentiated tasks can be executed better and at lower cost by a third party, is just a fundamentally good business plan. The core competency for financial institutions is clearly not data engineering but making profitable investment decisions increasingly using data-driven investment strategies. This basic premise is not lost on most organizations we met with at BattleFin.
Not unlike how we all continue to see first-hand how cloud computing is reinforcing that building and operating compute and storage infrastructure at-scale is non-differentiated for almost all organizations, that same level of awareness for data engineering is quickly becoming apparent to most institutions we met with at the conference.
Embracing Cloud-Based Machine Learning
After meeting with many of the financial institutions and technology vendors, another theme we observed was the recognition that using cloud-based machine learning from Google, Amazon, Microsoft, etc. was a more effective strategy than build-it-yourself AI. The amount of training data and cheap infrastructure available to these “tech titans” makes it difficult for organizations to keep pace. The lightbulb moment that cloud is both a better infrastructure and AI capability is a trend that has gone from almost zero understanding a year ago, to full acceptance this year. The net result is that cloud-based AI is an even greater forcing function for many firms to move forward even more aggressively on their adoption of cloud computing
Hype Fading Around Alternative Data as a Singular Source of Alpha
Another observation is that the hype around alternative data continues to be high, and it’s clear to us that the most productive organizations are using a combination of traditional financial data, some alternative data, and old-fashioned human intuition to make informed business decisions. We are skeptical that all but a few institutions are capable of making investment decisions solely using alternative data, no matter what most data providers may say.
A final observation is related to cross-pollution and movement or personnel between firms of a small group of data buyers. It is unclear to us how organizations will protect the alpha they are hoping to generate using alternative data as those individual data buyers change employers on a frequent basis. It is unclear to us how new innovative ideas will permeate into this environment in what is perceived as a closed ecosystem of people and technology providers. As a technology company looking from the outside-in, we feel there is significant opportunity to bring new ideas, technologies, and ways of doing things to enhance what is already working very well, and not just to replicate what others might be doing already.