Optimizing data and analytics in capital markets is a moving target, influenced by factors such as market evolution, technological advances, and shifting demand from end-user traders and investors.

Providers of data and analytics aim to improve their offerings with cues from prevailing trends, which may change from year to year or even quarter to quarter.

Current trends driving development and provision of data and analytics include an increased need for transparency in private assets and other less-liquid markets; the rise of artificial intelligence as a technological enabler; and a wider distribution of institutional-grade datasets and the tools needed to extract the most value from them.

Dan Dalton, Global Head of Data Analytics Sales at JP Morgan, likened the development stage of private markets today to where some public securities markets were a quarter-century ago. “You’re seeing the same challenges,” Dalton said. “How do you build transparency in those markets? You need pricing, you need indices, you need intelligence from experts.”

Dalton noted AI itself isn’t new, as many large clients of JP Morgan have been using it for years. “What’s new is who can use it,” he said. “New tools are democratizing some AI and machine learning techniques, which had been the realm of clients who could invest heavily in technology and quantitative resources.”

Voices of experience

A 25-year JP Morgan veteran, Dalton spoke with Simon Smith, Head of Front Office Data and Analytics Sales and himself a 17-year JP Morgan vet, about the evolution of data and analytics, the present landscape, and the outlook for the future, both from a broader marketplace and a JP Morgan-specific perspective. A transcript of the conversation was provided to Markets Media.

Dalton had been in an operations leadership role on the research side before moving to data and analytics in the early 2010s. He was drawn by a challenge: “We were producing so much content and amazing intellectual output, but perhaps the way we were positioning it and serving it for clients to receive it could do with a bit of tuning. So I put my hand up.”

Since starting as an intern in data and analytics for equity derivatives, Smith has stayed in the domain, moving through engineering and product roles to his present sales role. He said the product role required increasing time with clients to explain what JP Morgan built and how to derive the most value from it, which was a natural segue to a more client-centric sales role.

As the largest US bank, JP Morgan has unparalleled breadth in its product offerings, but Dalton and Smith both emphasized the importance of the “one conversation” ethos with clients. A large asset manager who needs regulatory reporting support will have different data and analytics needs than a startup hedge fund who wants pre-trade data for systematic trading; JP Morgan customizes to suit while keeping it under one umbrella.

For example, “we’ve got people aligned around our pricing and index products because the client at the other end is buying both of those in tandem,” Dalton said. “So it makes a lot of sense to have one connected client discussion.”

Smith said client meetings hold a bit of the unknown and are rarely square hole, square peg.  “It’s very often trying to make a solution that fits the client’s workflow,” he said. “How do we create something that’s fit for purpose, using the vast amount of technology and resources that JP Morgan has?”

Another trend in data and analytics is that structured data is no longer the only data – written and other unstructured data is being mined for its utility in the investment process. “This content can be transformed into something else,” Dalton said. “People are familiar with that, but again, it’s the tools that are coming out that are democratizing and enabling more people to do that.”

The future is AI

Going forward, the future of data and analytics will be largely driven by the evolution and adoption of AI.

Smith noted that some quant trading clients whose edge is in extracting information from content are asking for more commentary, transcripts and earnings models. “I’m not quite sure where that ends up from their point of view, but they’re continually pushing the frontier of what we provide,” he said.

Such firms “need to continue moving to make sure they are getting an information edge and not just getting the same information in an easy-to-digest format that everyone else has,” Smith added. “Those who are on the journey behind them are benefiting from what falls out behind that.”

Dalton said that one year ago, AI was about sourcing, collating, and aggregating data, as well as figuring out governance and access. “Fast forward to now — people are actually using AI, and that pre-work has been very beneficial.”