By Abhik Pal, Senior Director & Global Practice Head, CRISIL’s Global Research & Risk Solutions
The search for new sources of alpha by buy-side firms has necessitated the rapid adoption of data analytics. In response, some sell-side firms have started integrating data analytics into research to stay relevant and provide differentiated offerings.
The sell-side firms that have made this a priority have gone about it in several different ways. For example, some firms have focused on curating data sets and providing alternative data-enabled research and advisory services, while others focus on artificial intelligence or machine learning (AI/ML) analysis and primary surveys and collaborate with external data consultants.
Despite the immense opportunity for companies to differentiate themselves with clients, and to help those clients unlock new sources of value, widespread adoption of data analytics by sell-side firms for research and advisory purposes has yet to take wing. Products, investments, and execution are generally disparate, and monetization remains a challenge.
From our interactions with research heads over the years, it appears that a common concern has been inadequate clarity on incremental revenue generation that can be directly attributed to data analytics efforts. Even when there is a strategic drive, firms are concerned about the cannibalization of traditional research revenues.
To succeed, sell-side firms need to define a clear data analytics strategy and channel budgets accordingly. Sell-side firms can start by clearly defining the purpose of their data analytics practices, based on an assessment of client needs and their own positioning. The following are several components of building a successful data analytics strategy.
Offer focused, data-driven differentiated research and advisory services to protect positioning. Despite monetization challenges, integration of data analytics into research by sell-side firms is essential to differentiate offerings from those of competitors and to protect positioning. At the same time, considering that such integration rarely adds meaningful revenue, it is imperative to minimize additional costs. Integration, with a focus on sectors and themes that are a firm’s core strengths, will drive differentiation while being cost-effective.
Establish dedicated data units to monetize enriched data products and advisory services. While differentiated research will help garner mindshare, it will not be sufficient to drive monetization, in our view. One of the roadblocks is the need to widely disseminate any information that could provide actionable insights to clients. We believe the way forward is to establish or ramp up dedicated data units that can provide customized, curated and insight-ready data sets, analysis, and advisory services to select clients, and thereby ensure a sticky subscription-revenue base.
Build partnerships with key data vendors and technology providers. The alternative data marketplace is proliferating with data sets ranging from established ones (consumer transactions, web-crawled data, etc.) to fast-rising ones (ESG, geo-location, etc.). Sell-side firms with expertise in specific sectors should aim to expand such partnerships with data vendors to incubate and develop unique sector-specific data sets. Firms should look at the possibility of exclusive arrangements and continually use that data to establish ongoing value in their chosen areas.
In a similar vein, sell-side firms should leverage partnerships with technology providers to set up their own data and analytics infrastructure. While repurposing existing infrastructure might suffice to start with, eventually firms should scale up their infrastructure to handle data storage, processing, and distribution requirements, in line with their chosen strategy.
Assess talent skills and gaps. Firms can start small with a central hub consisting of data scientists and data analysts, catering to multiple sectors. In addition, firms can leverage in-house talent and augment selectively to fill a skills gap. As the data analytics practices start to build up, firms should then invest in data advisory talent that can drive monetization efforts.
Increase data analytics spending to 5% of research budget. Our market interactions suggest that sell-side firms typically spend 2-3% of their annual research budget on data analytics. Most firms adopt a conservative approach due to uncertainty over investment returns and the inherent challenges in fully integrating data analytics. To adopt a targeted approach towards research integration and data offerings, we recommend increasing the spend on data analytics to about 5% of a research budget, which translates into a cumulative three-year budget of $30-$60 million for bulge-bracket firms and data-focused boutiques, and $6-$15 million for regional firms and Independent Research Providers (IRPs). Of these investments, nearly half should be in talent, with the rest split almost evenly between data and infrastructure.
In our view, these approaches will help firms to enhance research with data-driven insights and enable them to provide differentiated data products and advisory services to clients, thereby effectively unlocking value in data and analytics.
