‘Alternative’ Data Drives S&P Global’s Growth

The Fortune 500 data company onboards AI and machine learning capabilities to make hay with unstructured datasets.
David McCannOctober 18, 2019
‘Alternative’ Data Drives S&P Global’s Growth

In a world increasingly driven by data, S&P Global could hardly be better positioned. The $6 billion company offers a broad range of data, ratings, analytics, and benchmarks products to players in the capital and commodities markets worldwide.

Most of the data in the world is “unstructured.” Generally, the term refers to text-heavy information that’s not organized in a defined manner. Harnessing such data, which falls into a category of information known as alternative data, is a key to S&P Global’s continued growth.

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Ewout Steenbergen

“We decided we needed to make a very hard push in technology innovation,” says Ewout Steenbergen, the company’s CFO. “There are so many unstructured data sets. The question is how to complement our existing data sets with these new data sets, and how we can use technology to make sense out of that.”

A pair of companies S&P Global acquired in 2018 exemplify the company’s push in that direction. One, Panjiva, uses machine learning and artificial intelligence to convert unstructured supply chain data, such as import-export data and bills of goods, into structured data. The company tracks more than 40% of the world’s merchandise trade (by dollar value) via cargo ships.

“If anyone wants to know how many cars or refrigerators are being imported by companies in the United States or are transported from one country to any other country, we now have that information available for our customers,” Steenbergen notes.

“Investors today don’t wait until a company publishes its financial results,” he continues. “They’re looking for additional datasets and indicators for how a company is doing during a quarter. If they see a company is [consistently] importing a certain level of goods and suddenly that goes up or down, it’s an indication of what’s happening with demand for its products.”

Intelligent Search

S&P Global’s other key 2018 acquisition was Kensho Technologies, a provider of analytics, AI, machine learning, and data visualization systems to global banks and investment institutions, for which it paid $550 million.

Kensho is helping S&P Global on a number of fronts. For one, it enabled the construction of a modern search engine for its vast, growing database of companies. “It’s not just a keyword search — you can put in intelligent, difficult questions and get real answers,” according to Steenbergen.

More importantly, Kensho algorithms are allowing S&P Global to upload data exponentially faster than previously possible.

A month after the acquisition, a link was established to a database of information on private companies licensed from Crunchbase. Next up was a much larger private company database from a source that Steenbergen couldn’t mention.

Previously, taking in new datasets was a very labor-intensive process involving cross-linking the new and existing information. A new company in the database could have shareholders, executives, or board members that were already in it, for example, or be a subsidiary of a company in another jurisdiction.

The large private company dataset had several million records “that we linked to over a weekend,” Steenbergen says. “Our chief technology officer told me it previously would have taken teams of people years to do it, which we wouldn’t even have done because the dataset would have been completely outdated by then.”

S&P Global executives stress that alternative data generates the greatest value when combined with other datasets.

Several months before the Panjiva acquisition, Warren Breakstone, chief product officer of data management solutions for S&P Global Market Intelligence, wrote that “while alternative data today is all the rave, it is most useful in concert with traditional information to drive useful insights. For example, combining and linking shipping data with traditional supply chain data and company-level financial data tells a more complete story.”

In another example, S&P has access to a dataset on foot traffic in malls — how many people visit them and what areas of malls get the most traffic, for example.

“That’s interesting information, but perhaps not so useful,” says Steenbergen. “What’s important is that we have layered that dataset on top of our rich database of REITs [real estate investment trusts].”  That allows clients to answer questions like what happens to foot traffic when a certain retailer opens up next to another one or when an anchor store liquidates.

“Those are great insights that can inform business or investment decisions,” the CFO says.