Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out.


When Warren Breakstone wanted to make it easier for S&P Global Market Intelligence customers to consume the trove of finance data the company holds, he turned to cloud data specialist Snowflake.

As managing director and chief product officer for data management solutions at S&P Global Market Intelligence, Breakstone recognized that the choice of cloud data platform was a key concern for his organization, which is a division of finance giant S&P Global. His team is continually on the lookout for new ways to create innovative data-led products for its major clients, which include finance firms and blue-chip enterprises across a range of sectors.

The organization was keen to take advantage of the cloud and make it easier to use data held on the S&P Global Marketplace, which brings together the firm’s data and information from third-party sources. After a period of evaluation, the organization started working with Snowflake last year. Here, Breakstone discusses why he selected Snowflake and how its technology forms a platform for further innovation.

This interview has been edited for brevity and clarity.

VentureBeat: What was the aim of the implementation?

Warren Breakstone: What we’re focused on is productizing data — creating new data-driven products, linking all of that together and combining it so that clients can get incremental value. And then also making it available to clients in the way they want to consume it. And that’s what we’ve really done with Snowflake, which is make all our data on the S&P Global Marketplace available through the Snowflake distribution and couple it with Snowflake compute power, so that clients can take advantage of bigger data queries, and all the advantages of compute power, so that they can study and research and analyze and evaluate, not just our data, but our data in combination with their own data.

VentureBeat: What was the business challenge that you were looking to solve?

Breakstone: The big challenge has always been that different clients have different means of bringing data into their environments. Some want it through our Xpressfeed solution, which is our bulk-delivery technology that automates the ingestion of data directly into their environments. Others want to access the data through APIs. Then there’s a third tranche, who want it through pre-packaged software products, such as our Capital IQ platform. The challenge is being able to support all the different clients and the different ways that they want to consume data.

What Snowflake provides us is a modern addition to our array of distribution, and has additional advantages such as the ability to utilize the compute power as data gets bigger and bigger. Clients want to do new and interesting things by bringing different data sets together, so the ability to access compute power is so important. That has opened up all sorts of new opportunities for us and for our clients in the way we deliver new capabilities, new content, new products, and additional value.

VentureBeat: How did you deal with the build versus buy question?

Breakstone: The challenge was more around who we would partner with. We have many home-built delivery solutions, such as Xpressfeed, which we’ve enhanced with what’s called a loader, which is a piece of software that automates the ingestion of data for our clients. And that’s a great product and clients love it. But clients also are increasingly looking to the cloud. And that’s where we had to make a decision: How best do we approach that opportunity, and who do we partner with to get there? And that’s what led us to Snowflake.

VentureBeat: Why did you select the Snowflake cloud data platform?

Breakstone: First and foremost, it was about being closely connected to our clients — and our clients were talking about Snowflake and the opportunities that it provided to them. So as we were doing a pretty robust review of the landscape and different partners, and knowing that we wanted to get into cloud-based distribution, the question was how best to do it. Snowflake was one of the alternatives we considered.

We then needed a solution that would support our clients based on where they are today. Clients are on different solutions — some are on AWS, some are on Google Cloud Platform, some are on Azure. How do we support all of those different clients, based in the environments that they’ve stood up? That also was another plus in the Snowflake column because it’s a cloud-agnostic solution; we can build it once and serve many.

VentureBeat: What were some of the other technological factors that led you to Snowflake?

Breakstone: We did various tests to see what the compute was like relative to other alternatives in the market and we were very impressed. Some of that came back to the initial architecture that Snowflake has built itself on, where they’ve separated their compute from their storage, and because you’ve separated those two, you’re able to get a bit more performance out of the compute.

Snowflake also has connections to other applications and tools in the space. Various visualization and analytic tools are already connected to Snowflake. Once we put our data into Snowflake, if a client wants to consume that data through a third-party visualization or analytics tool, more often than not, that provider is already connected with Snowflake, which makes the process for us to get the data into that solution and into their environment much less complicated because there’s a pre-existing pipe.

VentureBeat: How did you implement the Snowflake cloud data platform?

Breakstone: That involved a tight partnership between our technology group and our product management organization, where we first prioritized — based on customer needs — what data we were going to add to Snowflake’s environment and in what order. And then we were able to work with Snowflake to develop a rigorous and repeatable process, where we would be able to load the data into that environment. It was a very partnership-oriented approach. And we got there quite quickly; far smoother than we had expected.

The challenges were really one of prioritization. We have hundreds of different datasets, so where do you start? Do you start with the bigger, most complex data sets? Do you start with the simpler ones that are easier to load? We had a group of clients who partnered with us and helped us set those priorities. And that was very useful.

VentureBeat: What does the implementation mean for other investments in the data stack?

Breakstone: We’ve just introduced our Marketplace Workbench, which is a platform that we’ve built on top of Snowflake and Databricks, who are a partner of Snowflake. This new platform enables our clients to use our data in a collaborative development environment, using a programming language of their choice, whether that’s Python or R or SQL, to get more out of the data.

So, what we’re happy about is that this isn’t just a singular, one-off type of opportunity for us. This is something that we continue to build on, and we build on it in a way that’s relevant to our clients. It’s not about us, it’s about how our clients are able to generate value and utility from these various connected solutions that are all built on top of our data.

VentureBeat

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:
  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more
Become a member