Snowflake Summit - how London Stock Exchange Group gets Investment Managers the data they need

London Stock Exchange Group’s financial data arm Refinitiv has a very specific business need - to ensure its finance customers and clients in other sectors have simple and quick access to game-changing insight.

Austin Burkett, Global Head of Quantitative Analytics and Content at London Stock Exchange Group (LSEG), manages the quantitative analytics (QA) business within Refinitiv, explains: 

We were looking to solve the business challenge of our customers. Two years ago, they were coming to us in droves and saying to us, ‘We really want to see your QA service available natively in Snowflake. In my seven years at the firm, I hadn't seen a level of demand like that. And so it motivated us to start our development work.

Since launching QA on the Snowflake platform just over a year ago, Burkett says his team has seen a “huge amount of uptake” from customers:

These are customers that need a large amount of data concerning publicly traded companies, deep histories, broad coverage, or key performance indicators. Taken together, they form an investment theory, which they test using our data. They layer their applications on top of our data to determine if an investment strategy works. So, they need a very long history of high-quality data.

LSEG’s target customer are Quantitative Investment Managers who use data to inform their decision-making strategies for some of the world’s largest hedge funds. Burkett’s team also sends data to investment banks that have Asset Management divisions. The QA data is plugged into these customers’ internal systems:

We share licensed data that is owned by Refinitiv into our customers’ Snowflake accounts. It’s our data combined with lots of other third-party data and their own proprietary data. The principal value proposition we bring with our QA database is concordance across all those disparate data sets. It’s really easy to integrate all these datasets to fuel a quantitative investment workflow.

Creating value from data

The high level of integration makes it much easier and quicker for data scientists and data engineers at Investment Management firms to use LSEG’s information in their financial analyses, according to Burkett: 

Quantitative investors need to combine many different types of data sources together. Our QA database enables a primary key between all those data sets. The data resolves down to a fundamental element, called a QA ID, which enables customers to map all these different datasets at the entity level, which makes it much easier to undertake their workflows.

Providing that primary key and a high level of integration via Snowflake removes a range of administrative headaches and creates a series of benefits for users. First of all, the implementation is multi-cloud, explains Burkett:

We want to engage with our customers wherever they want to use the data. So, we've provided QA over multiple cloud regions and providers, and we’re sharing the information directly into their Snowflake accounts.

Second, LSEG’s customers can get away from the burden of extract, transform and load, which can involve effort and risk:

Snowflake eliminates a lot of that troublesome and risky work that they have to do to utilize data from financial data providers like us.

Finally, Burkett’s division provides data to its clients via an elastic, scalable model:

They can spin up as much compute as they like. If a customer licences a new data set from us, and they want to calculate thousands of new investment signals out of it, all they need to do is spin up a data warehouse in Snowflake and make that calculation.

Pushing into new areas

One of the challenges Burkett’s team has had to meet head-on is the wide range of IT foundations used by LSEG’s clients:

A lot of our Quantitative Investment customers have built up a huge amount of sophisticated infrastructure over the decades. You can't just change that on a dime, of course – and we recognize that.

While some firms are ready to adopt the Snowflake data feed from day one, others take several months to adopt the service, while others are still to make the switch:

It’s a wide spectrum. And in those cases, we have other means of delivering QA to the customer, such as natively through Azure. We're a multi-cloud organization and we want to be delivering our data in the environments that makes sense to our customers. But with the huge level of customer demand for QA data through Snowflake, it made sense for us to start delivering it there.

As well as delivering data to its clients via Snowflake implementations, LSEG also now lists its QA data on the Snowflake Marketplace, to “increase customer eyeballs”.  While Refinitiv usually focuses on financial institutions, Burkett says the Snowflake Marketplace gives his team the opportunity to serve customers in other sectors:

We want to show that we are there, that we can deliver data through Snowflake, and that's what we've achieved through our Marketplace listing. It’s all about ever-expanding customer types and use cases. Even though the data in QA has been largely focused on investment-oriented use cases until now, there's no reason why non-financial institutions couldn’t take a look at it.