Sell-side firms need a targeted strategy to harness the power of data analytics and tailor their offerings to clients.
As things stand, adoption of data analytics by sell-side firms for research and advisory is yet to take wing. Products, investments and execution are disparate generally, and monetization remains a challenge. Besides, most firms are cautious about investing in data analytics due to tepid returns from earlier investments, uncertainty over revenue streams, and fear of attenuating traditional research revenues.
A successful data analytics strategy is one that enables monetization by triangulating target clientele, research integration aims, and data offerings.
Firms can monetize by establishing, or ramping up, dedicated data units that offer clients subscription-based enriched products and actionable advisory services. Integration of data analytics into research can bolster mindshare and protect current positioning.
Partnering with third-party data vendors and technology providers, and bridging skill gaps are crucial in this milieu.
Expanding budgets and taking small but focused steps are equally important. To truly transform their data analytics practice, sell-side firms will need to increase their data analytics budget from 2-3% currently to 5% of their overall research budget, and channel it according to their data strategy.
Our estimates indicate bulge-bracket firms and data-focused boutiques would require a cumulative three-year outlay of $30-60 million across data, talent and infrastructure. For regional firms and independent research providers (IRPs), that could be $6-15 million. The scale of outlays would vary based on current data analytics maturity level and overall research strategy, too.
For data-driven research and insights, enriched data products and client advisory services, sell-side firms should start small and expand gradually. Firms need to identify specific research products, sectors and themes, and channel budgets towards analytics-ready data, visualization to enhance client engagement, and incubation of unique data sets.
