Large Cap DaaS Companies Q2 Earnings Recap

With the second quarter’s earnings season wrapping up, we thought it would be useful to do a quick recap of the key insights learned across the portfolio of companies within the World of DaaS publicly traded companies list. The first of two posts will focus on the large cap (greater than $10 billion market capitalization) companies. 

These companies tend to be more established, longer-tenured, with more traditional datasets and corporate clientele. This analysis should provide valuable insight into their underlying data trends, as these companies lead in thinking, positioning, and monetizing data.

Aggregate Stats

In aggregate this cohort of DaaS companies saw a loss of 0.5% for the quarter which is considerably lower than 4% the S&P posted for the same period.

Driving Growth

An obvious focus is driving revenue growth, and many management teams discussed improving the monetization of existing datasets on their earnings calls. There were a few common themes, some of which were championed as new strategic initiatives:

• combinations through merger, partnership, and integrations

• superior analysis delivered to clients with upgraded tooling

• changing business models necessitating AI investments

• increasing business decision complexity spurring more end-market consumption

Getting More from Data Synergies and Analytical Offerings

S&P Global discussed the ability to monetize new datasets through its mega acquisition of IHS Markit. Already 4 years in since announcement, the challenge of integrating such a large acquisition requires time, patience, and disciplined management but was a prevalent theme on their earnings call. The company also completed a smaller tuck-in acquisition of Visible Alpha, a fintech which provides consensus estimates data, powering investment professional workflows. As Doug Peterson (CEO) noted, “We continue to create new products from the combined data sets and solutions…”

Investing to deliver superior analytics was also a recurring theme, especially when companies control much of their niche data ecosystem. In such cases, these companies are finding ways to optimally price. Take consumer credit data, a space dominated by three companies. Instead of talking about wholesale, these companies (Transnation, Equifax, and Fair Issac Corporation) spoke about building superior analytics, often powered by investments in AI, sitting on top of their data:

Strategic Cloud Alliances: Unlocking the Power of Data through Tech Giants

Powering these insights requires additional investment in cloud infrastructure, and we heard recurring mention of strategic partnerships. For example, LSEG Group (LSEG LN) touted its strategic partnership with Microsoft (MSFT US), enabling it to plug data into Azure cloud, accessing many of the regulated financial services entities safely within their compliance architectures. CME US and ICE US both talked about their partnerships with Alphabet (GOOG US), providing more compute, better fidelity, and quicker access to their higher frequency financial data. 

Instead of having to build infrastructure and models themselves, they are partnering with a titan in the space. Or as Terry Duffy (CME CEO) noted, “our clients will also be able to utilize Google's artificial intelligence and data capabilities to help develop, test and implement trading strategies to manage their risk more efficiently.” Such partnerships unlock incremental value by moving further up the value chain.

Adaptive Business Models

Strategic shifts in terms of business models were also noted. For example, Thomson Reuters (TRI US) and Relx PLC (REL LN) have held strategically important legal datasets in the form of Westlaw and LexisNexis. While these have traditionally been subscriptions databases for lawyers to comb through case law, both companies spoke about layering in AI solutions to help surface quicker insights, repositioning the product as more than just a digital library.

As the FT noted, this may in the medium term change the industry’s billing practices, as it comes to billable hours, unlocking new monetization channels for these legal data titans. To do so requires an intimate understanding of the actual use cases of the data, rather than simply putting an LLM on top of data and selling it as a product. RELX CEO Erik Engstrom addressed this, noting that they “develop and deploy these tools across the company by leveraging deep customer understanding to combine leading content and data sets with powerful artificial intelligence and other technologies.”

LSEG furthered this, highlighting their Data as a Service offering, which better aligns the consumption of their data with their monetization, while making it easier for clients to access exactly what they need. Or as David Schwimmer (CEO) noted, it was previously “hard to isolate the data you need, or combine it with other data. Sometimes you might not even know what data is available. That’s what we are addressing with the Data as a Service.” Having the right infrastructure should help them unlock a “pay as you consume” model.

Increased Complexity Necessitating Better Decision Making

Increasing global complexity was another theme. We saw this in insurance related data providers. Insurance markets have been volatile given the outsized impacts of storms and wildfires. The insurance ecosystem is reacting by demanding more data in order to increase the accuracy of models which cannot rely as heavily on historical patterns. This is a similar pattern with the upgrade in bank risk modeling after COVID, necessitating new models, more cloud architecture, and far more data.

Moody’s (MCO US) discussed the ability of their platform to partner with data providers in providing better information for modeling catastrophe risk. As Robert Fauber (CEO) noted, “I would say there's a -- with insurers, there's an understanding that you need better and better data and models.”

Other companies discussed the ability of LLMs to find patterns out of unstructured data, that previously was either incapable of being stored in a traditional database structure, or non monetizable, given an inability to perform easy analytics on top of the data. 

Healthcare connected intelligence solution IQVIA (IQV US) noted such:

The same goes for risk titan Verisk (VRSK US):

And the same goes for advertising platform The Trade Desk (TTD US):

A Bright Future For Data Monetization

Clearly these large cap companies are finding new ways to monetize their data through better access platforms, better cross-pollinating (either through partnership or acquisition of new datasets), and upgraded technology, often in the form of cloud infrastructure and AI models. Ultimately, these companies are racing to meet the needs of their clients, who are dealing with greater complexity and uncertainty, necessitating a larger data appetite and greater technological integration.

Given the stickiness of many of their offerings, the mission-criticality of their data, and their ability to make the necessary investments to generate more value, the future looks bright for this cohort of large cap DAAS companies in the public markets. 

In a follow-on piece, we’ll dive into the smaller cap companies, which have different datasets, different monetization paths, a different cohort of clientele, and unique ways of approaching data monetization. Stay tuned.