Carlyle-backed YipitData is reportedly exploring a sale that could value the alternative data and analytics provider at between $2.5 billion and $3 billion, offering a useful indication of how investors are valuing proprietary data assets as artificial intelligence reshapes the information industry.
The prospective sale comes as the rapid development of AI is prompting investors and information providers to reconsider where the most defensible value lies within the data and analytics chain.
According to Reuters, New York-based YipitData is working with Goldman Sachs on a potential sale, with discussions involving strategic buyers and private equity firms at an early stage. There is no certainty that a transaction will take place.
The potential valuation is nevertheless significant. YipitData is expected to generate approximately $280 million in annual recurring revenue (ARR) this year, with revenue growing at more than 30%, according to Reuters. At the upper end of the reported range, that would put its valuation at approaching 11 times ARR.It would also represent a substantial increase on December 2021, when Carlyle led a $475 million investment round that valued YipitData at more than $1 billion.
Data scarcity in an AI-rich world
AI is making increasingly sophisticated analytical capabilities available to a much wider range of organisations. Generative AI and large language models can interrogate information, identify patterns, summarise research and provide interfaces through which users can interact with increasingly large quantities of data. That potentially makes some elements of the analytics layer easier to reproduce. The underlying information on which those tools operate, however, can be much harder to replicate.
Reuters identifies this as one factor behind heightened investor interest in companies possessing proprietary datasets. Such data can provide differentiated inputs for AI models, while difficult-to-reproduce information assets can continue generating revenue even as AI puts pressure on parts of the traditional technology market. That creates an interesting dynamic for the alternative data industry. As analytical tools become more capable and accessible, competitive differentiation may increasingly depend on access to data that other organisations don’t have.
The ability to source information is only one part of that proposition. Data also needs to be cleaned, structured, mapped and maintained before it becomes sufficiently reliable for systematic investment research or other forms of analysis. Providers that have spent years developing those capabilities can therefore possess assets that aren’t straightforward to reproduce simply by applying newer AI technologies.
Alternative data moves beyond the hedge fund
YipitData also illustrates how the alternative data industry has expanded beyond its original customer base. Founded in 2010 by Vinicius Vacanti and James Moran, the company provides research and analytics derived from alternative datasets covering areas including e-commerce, payments, software, ridesharing and consumer technology. Its customers include hedge funds, private equity firms and asset managers, but also major corporations including Walmart, Lowe’s and Ulta Beauty.
Alternative data originally developed largely around investment managers seeking information that could provide an informational edge over conventional market and company data. Increasingly, however, many of the same datasets and analytical techniques have applications in corporate strategy, competitive intelligence, market research and other business functions. The boundaries between alternative data, market intelligence and broader information services are consequently becoming less distinct.
A broader market for proprietary data
YipitData isn’t the only information business attracting substantial investment. Reuters points to Sixth Street’s acquisition of commodities intelligence provider Kpler, valuing the company at more than $3.7 billion, and S&P Global’s $1.8 billion acquisition of private markets data provider With Intelligence, completed last November.
Different businesses and datasets are involved, but the transactions share an underlying characteristic: each has built information assets and associated workflows that would be difficult for customers or competitors to reproduce quickly.
For alternative data providers, that could become increasingly important as AI develops. The technology will undoubtedly transform how investment firms discover, interrogate and analyse information. It may also lower barriers to building analytical products that previously required considerable specialist development.
What AI can’t necessarily manufacture is a long-established proprietary dataset, together with the sourcing relationships, historical depth, taxonomy and quality controls required to make it useful. If YipitData ultimately changes hands at anything close to the valuation currently being discussed, it would therefore represent more than a sizeable return for its investors. It would provide another indication that, in an increasingly AI-enabled information market, differentiated data itself is becoming an increasingly valuable asset.
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