Brief

The Next Growth Horizon in Financial Market Infrastructure: Advanced Data and Analytics

Innovative uses of data and analytics stand to generate strong new revenue streams for exchanges, clearinghouses, and other behind-the-scenes companies.

By Christian Westermann, John Fildes, Thomas Olsen, and Claudio Sorgente

Published on 八月 26, 2021
min read


At a Glance Advanced data and analytics has emerged as a major source of new revenue growth for financial market infrastructure companies; we expect 5% to 7% annual growth through 2030. Demand has been spurred on both the buy side (for alternative data and product-related information) and the sell side (to streamline labor-intensive tasks). Regardless of their strategy, leading firms are finding success by building on existing strengths and assets, carefully selecting use cases, leaning on targeted M&A, and choosing the right distribution channel. Behind the gyrations that generate headlines about financial markets, the business of market infrastructure is simmering with new competitive dynamics. Incumbent financial market infrastructure (FMI) companies such as exchanges, clearinghouses, central securities depositories, and custodians face a range of challenges to their revenues and margin. Alternative capital markets have shown strong growth, including private placements of equity and debt and the emergence of markets such as digital assets, providing new sources of liquidity. At the same time, traditional boundaries are being redrawn with the convergence of listed and over-the-counter markets, and direct-to-consumer and interdealer markets, along with the arrival of new industry utilities. During this evolution, advanced data and analytics has emerged as a significant opportunity to generate strong revenue growth in the short term. The financial data and analytics market globally had a compound annual growth rate of 6.5% from 2017 through 2020, when it reached roughly $35 billion. We expect 5% to 7% annual grown through 2030 (see Figure 1), mainly driven by market data but bolstered by an increasing share of alternative data, such as carbon, mortgage, or environmental, social, and governance (ESG) data, with an estimated compound annual growth rate of 10% to 20%. And we expect analytics solutions to grow at a rate of 8% to 11%. Figure 1 Revenues from financial data and analysis should show strong growth in the medium term Demand for data and analytics has been spurred on both the buy and sell sides. On the buy side, there is demand for alternative data that goes beyond descriptive market, pricing, and reference data (such as company fundamentals and events). Some alternative data covers a market, such as a country or industry, while other data consists of product-related information, including ESG metrics. This opens opportunities to link existing data sets with artificial intelligence and cloud-based technologies to create new data and analytics products. On the sell side, clients increasingly want data solutions to streamline labor-intensive tasks such as predictive default analytics, liquidity scoring for individual bonds on secondary markets, or “request for quote” auto-pricing based on trading preferences. In addition, expansion of regulatory reporting requirements on both the sell and buy sides has heightened demand, as has further adoption of AI in the financial sector, which requires huge volumes of data and advanced analytics for use cases such as fraud detection and risk mitigation. In response to increased demand, some incumbent FMI providers have started to invest in building out their data and analytics offerings and to compete against specialists such as FactSet, S&P, and IHS Markit (recently acquired by S&P). Some are taking on the role of provider of consolidated and aggregated financial data. Nasdaq, for instance, expanded its data provisioning business by investing in Matter, a platform for ESG data, sustainability analyses, and reporting services, and Quandl, which delivers financial, economic, and alternative data sets through flexible, user-friendly interfaces. Other incumbents have focused on analytics, providing insights from propriety and acquired data sets. LSEG has acquired Beyond Ratings to enhance its existing ESG index and Yield Book to boost fixed income analytics. And incumbent Deutsche Börse bought Axioma, a provider of portfolio analytics, to build Qontigo, an index and portfolio/risk analytics business. Our analysis of the leading incumbents finds that those with clear strategic focus, supported by systematic and strategic investments in data and analytics, are able to capture a larger share of industry revenues than companies that are more passive in this area. This outsize growth stems not only from simple dissemination of raw or integrated data, or provisioning of analytics services, but also from improving infrastructure services through innovative technology. A good example is Nasdaq’s recent acquisition of Verafin, a provider of financial crime management and fraud detection software, which strengthened the company’s existing anti-crime solution. Factors for success Incumbent FMI providers that successfully compete against data and analytics specialists have a dedicated focus on four dimensions: Build on existing strengths and assets. Develop a strategic approach, supported by distinctive products that leverage proprietary data and combine traditional and alternative data with advanced analytics. Carefully select data products and use cases. Identifying the right use cases to start with, and executing them well, will generate awareness in the market and lay down a path to a broader approach. A provider could, for instance, offer unique data sets or analytic-intensive insights into markets and economic indicators, both valued by certain customers. Lean on targeted M&A. M&A can serve as a shortcut to establish new capabilities and build out data sets. Targeted M&A should help a company obtain data sets with wide coverage, access to specific data, or particular types of analytical skills and technologies. Choose the right distribution channel. Deciding how to disseminate data is a critical step: Should it be a standard channel such as Bloomberg or an alternative data platform such as Quandl? Or should the data be disseminated using smart contracts, following decentralized finance trends? As an example of the latter, the Pyth network, built on the Solana blockchain, is a specialized service for latency-sensitive financial data, to bring unique data on-chain for decentralized access. Financial markets offer investors an ever-expanding array of choices for structuring portfolios. As those choices expand, so will demand for penetrating analysis to help determine their risk profiles and other characteristics. Technologies such as the Pyth network offer new ways to disseminate data, but they also pose a threat to some participants in the industry by disintermediating their ability to sell exchange data. Firms that stake out positions now in advanced data analytics will be better positioned to capture new, profitable revenue streams over the coming decade.

At a Glance

Advanced data and analytics has emerged as a major source of new revenue growth for financial market infrastructure companies; we expect 5% to 7% annual growth through 2030.
Demand has been spurred on both the buy side (for alternative data and product-related information) and the sell side (to streamline labor-intensive tasks).
Regardless of their strategy, leading firms are finding success by building on existing strengths and assets, carefully selecting use cases, leaning on targeted M&A, and choosing the right distribution channel.

Behind the gyrations that generate headlines about financial markets, the business of market infrastructure is simmering with new competitive dynamics. Incumbent financial market infrastructure (FMI) companies such as exchanges, clearinghouses, central securities depositories, and custodians face a range of challenges to their revenues and margin.

Alternative capital markets have shown strong growth, including private placements of equity and debt and the emergence of markets such as digital assets, providing new sources of liquidity. At the same time, traditional boundaries are being redrawn with the convergence of listed and over-the-counter markets, and direct-to-consumer and interdealer markets, along with the arrival of new industry utilities.

During this evolution, advanced data and analytics has emerged as a significant opportunity to generate strong revenue growth in the short term. The financial data and analytics market globally had a compound annual growth rate of 6.5% from 2017 through 2020, when it reached roughly $35 billion. We expect 5% to 7% annual grown through 2030 (see Figure 1), mainly driven by market data but bolstered by an increasing share of alternative data, such as carbon, mortgage, or environmental, social, and governance (ESG) data, with an estimated compound annual growth rate of 10% to 20%. And we expect analytics solutions to grow at a rate of 8% to 11%.

Revenues from financial data and analysis should show strong growth in the medium term

Demand for data and analytics has been spurred on both the buy and sell sides. On the buy side, there is demand for alternative data that goes beyond descriptive market, pricing, and reference data (such as company fundamentals and events). Some alternative data covers a market, such as a country or industry, while other data consists of product-related information, including ESG metrics. This opens opportunities to link existing data sets with artificial intelligence and cloud-based technologies to create new data and analytics products.

On the sell side, clients increasingly want data solutions to streamline labor-intensive tasks such as predictive default analytics, liquidity scoring for individual bonds on secondary markets, or “request for quote” auto-pricing based on trading preferences.

In addition, expansion of regulatory reporting requirements on both the sell and buy sides has heightened demand, as has further adoption of AI in the financial sector, which requires huge volumes of data and advanced analytics for use cases such as fraud detection and risk mitigation.

In response to increased demand, some incumbent FMI providers have started to invest in building out their data and analytics offerings and to compete against specialists such as FactSet, S&P, and IHS Markit (recently acquired by S&P).

Some are taking on the role of provider of consolidated and aggregated financial data. Nasdaq, for instance, expanded its data provisioning business by investing in Matter, a platform for ESG data, sustainability analyses, and reporting services, and Quandl, which delivers financial, economic, and alternative data sets through flexible, user-friendly interfaces.

Other incumbents have focused on analytics, providing insights from propriety and acquired data sets. LSEG has acquired Beyond Ratings to enhance its existing ESG index and Yield Book to boost fixed income analytics. And incumbent Deutsche Börse bought Axioma, a provider of portfolio analytics, to build Qontigo, an index and portfolio/risk analytics business.

Our analysis of the leading incumbents finds that those with clear strategic focus, supported by systematic and strategic investments in data and analytics, are able to capture a larger share of industry revenues than companies that are more passive in this area.

This outsize growth stems not only from simple dissemination of raw or integrated data, or provisioning of analytics services, but also from improving infrastructure services through innovative technology. A good example is Nasdaq’s recent acquisition of Verafin, a provider of financial crime management and fraud detection software, which strengthened the company’s existing anti-crime solution.

Factors for success

Incumbent FMI providers that successfully compete against data and analytics specialists have a dedicated focus on four dimensions:

Build on existing strengths and assets. Develop a strategic approach, supported by distinctive products that leverage proprietary data and combine traditional and alternative data with advanced analytics.
Carefully select data products and use cases. Identifying the right use cases to start with, and executing them well, will generate awareness in the market and lay down a path to a broader approach. A provider could, for instance, offer unique data sets or analytic-intensive insights into markets and economic indicators, both valued by certain customers.
Lean on targeted M&A. M&A can serve as a shortcut to establish new capabilities and build out data sets. Targeted M&A should help a company obtain data sets with wide coverage, access to specific data, or particular types of analytical skills and technologies.
Choose the right distribution channel. Deciding how to disseminate data is a critical step: Should it be a standard channel such as Bloomberg or an alternative data platform such as Quandl? Or should the data be disseminated using smart contracts, following decentralized finance trends? As an example of the latter, the Pyth network, built on the Solana blockchain, is a specialized service for latency-sensitive financial data, to bring unique data on-chain for decentralized access.

Financial markets offer investors an ever-expanding array of choices for structuring portfolios. As those choices expand, so will demand for penetrating analysis to help determine their risk profiles and other characteristics. Technologies such as the Pyth network offer new ways to disseminate data, but they also pose a threat to some participants in the industry by disintermediating their ability to sell exchange data. Firms that stake out positions now in advanced data analytics will be better positioned to capture new, profitable revenue streams over the coming decade.