How Rathbones Asset Management built strong data foundations

Stephen Wood, chief operating officer at Rathbones Asset Management (RAM), faced a challenge. His organization wanted to build strong data foundations to help deliver fresh insight to its professionals and embrace emerging technologies, such as agentic AI.

The solution to this challenge came via technology specialist Snowflake’s data platform.

“We wanted to create a golden source of data for each of our foundational domains,” he says.

“We wanted to overlay the data quality checks, the business rules, all those good things, and then everything would be built off that data lake. That's where we found ourselves working with Snowflake’s AI Data Cloud.”

Latest Videos From

Watch full video here:

Taking a long-term view

Wood, who spoke with ITPro at the recent Snowflake Summit 2026 conference in San Francisco, says this process began two years ago, when RAM, which is part of the Rathbones investment group, was looking for a way to manage its information assets more effectively.

The shift to Snowflake is part of a broader digital transformation to support business growth. The organization’s assets under management grew quickly before the move to Snowflake. However, RAM was still using a portfolio management system that was customized for the wealth management element of the wider Rathbones group.

Fortuitously, a solution came from within the broader Rathbones group. The firm was in the first phase of its Snowflake deployment, and Wood suggested to senior executives that the technology could help his organization manage its data headache:

“We said, ‘Look, we can take advantage of this technology,” he adds. “We want to lead the way in terms of building out our core foundational data domains in a Snowflake environment.’”

Transformational stages

Stage one of the transformation project involved a point-to-point data share between the existing Charles River investment management solution and the Snowflake platform. Wood says this data-sharing process covered a trading view of the organization’s funds, such as securities bought and sold.

Stage two, meanwhile, involved building out RAM’s data store and the static reference information for its funds. Wood says this process allowed his team to build the Open Funds template, which external vendors use as a standard for consuming the organization’s data.

The success of this initial work meant senior executives could see further opportunities, with Wood adding: “Then we started to get into conversations where we were saying, ‘Okay, now we need historical fund performance, our quant risk calculations, what we call subscriptions and redemptions, and the cash flow in and out of our funds from clients.’”

Today, RAM has nine domain-related data sets established in its Snowflake data platform. These data sets cover a range of areas, including performance, risk, sustainability, and unstructured information, such as the decisions behind the firm’s investment activities.

Foundation for future success

Wood says the integrated data sources provide a foundation for change, including potentially embracing emerging technology. Work undertaken so far means his team can start exploring AI, including Snowflake’s agentic services. At the San Francisco event, Snowflake announced new features for CoWork, its personal agent for knowledge workers, and CoCo, its coding agent for developers and data engineers. Wood can see the agentic technology’s potential.

“When you combine all the information, you can start to do some powerful things,” he says.

“We're now in a scenario where we can say, ‘Looking over all this data, we can start to analyze the performance of our investment decisions and give product specialists new insights.’”

RAM’s agentic explorations are already underway. The organization has a test environment for CoCo and CoWork that’s running with millions of rows of data. “At this stage, the outputs look very decent, so it's a near-production environment,” Wood says of the explorations, before outlining other potential applications.

He adds: “The future direction is that you start to get into sales and distribution. The most valuable data in our CRM system today is the call notes written up after every meeting that say clients are interested in some funds and pulling away from others. No one could analyze that data until now, because, after you've had two or three calls with the client, the information disappears down the chain in Salesforce.

“Now we're looking at the idea of using agentic technologies to consider the long-term buying trends of clients. The salespeople know their clients, and they know what they're buying. But if we see the general trends, then that insight can go into a product cycle that makes us think, ‘Do we need to be looking at new funds, do we need to broaden our horizon? Are we at risk in certain areas?’”

Fuelling innovation

The work Wood’s team has undertaken means they can investigate native applications and emerging technologies with confidence, rather than focusing on point-to-point data shares with Snowflake. That investigatory work is crucial because, as a smaller business, RAM will never have a team of engineers to build out a big AI platform.

“That approach doesn't make any sense,” he says. “What does make sense is that we've got Snowflake technology that is central to our data strategy. Now, our work is about how we can find the tools that interplay with the platform, so we reduce the Frankenstein's monster of connectivity and integrations that can slow projects down.”

Wood reflects on the digital transformation program he’s overseen and says that, as with any financial services firm, the key challenge is ensuring exploratory projects are prioritized in an industry that often contends with a regulatory burden and technical debt. Sometimes, due to these intractable challenges, innovation gets stifled because of people and budget constraints. Wood says his organization has circumvented these issues.

“I think Snowflake, and how Rathbones operates, has allowed us to take control of our environment,” he says, referring to the split between the group implementation of the platform and his organization’s deployment. “There's a broad Snowflake environment, and then we've got a ring-fenced environment, with all our data sets, which means the implementation has been built in a way that allows more self-service capability.”

Wood suggests the key to long-term success will be staying on top of Snowflake’s product advances. For example, his team received buy-in for its CoWork and CoCo explorations by explaining how a trial of the agentic technologies could benefit the group.

“There are good ways to try and ensure that you get that prioritization if you need to,” he says.

“When you've got a real-world business case and value proposition that is going to materially benefit the business, versus just being a nice technology idea, the investment ultimately comes through.”