Data is boring, right? A look at some innovative market data projects underway

Market data, despite its importance to financial markets, is rarely a true hotbed of innovation. Aside from the growth of alternative data (which I admit, I always find very cool) and the issue of data latency—which spurred a budget-thirsty race-to-zero and drove the introduction of technologies such as co-location with marketplaces, microwave data networks, and dedicated hardware for processing data—most data issues revolve around how you get a price (or other piece of data) from A to B, and how you pay for it.

I’m not saying these aren’t important issues; it’s just that the basics of data management haven’t changed much in a long time. But each element of the financial data industry is now facing some form of disruption that could usher in much more innovative approaches.

Let’s start with the data itself. We understand alt data and sentiment data. But at the intersection of these lies the data that either made or broke hedge funds during the GameStop rally: unstructured messages on Reddit chat rooms. Were data scientists considering Reddit a source of market data before a bunch of amateurs outwitted some of the smartest minds on Wall Street? Probably not. They probably believed that buying feeds of investor activity from RobinHood, other retail platforms, and social media was sufficient insight into how crowd sentiment was moving on a stock. Now they may need to re-evaluate where they look for indicators of how markets will move.

Next, let’s look at how the process of moving data from A to B is changing: one challenge is how to handle these new data types that don’t conform to numerical values that can be easily tabulated. Tools like knowledge graphs—which visualize relationships between data points three-dimensionally—offer the potential to not only identify a firm’s exposures and risks, but also to help define and connect data types that defy traditional descriptions.

But these new data types still need to link to “traditional” market data that reflects specific assets traded on markets. So that data, and the basic mechanisms that distribute it correctly, aren’t going away anytime soon. However, they are evolving, largely driven by the potential of the cloud to host new generations of data platforms—ranging from Refinitiv’s Real Time Distribution System to solutions from new contenders like BCC Group or Pegasus Enterprise Solutions—and to replace dedicated networks for data transmission.

And here are some areas where another, hitherto-overhyped, technology may find its forte. Blockchain has been like a hammer looking for a nail beyond digital currencies. But it has some potential use cases here: arguably, its distributed ledger, which provides an immutable record of “transactions,” could perform the function of proprietary identifiers, without the need for a proprietary identifier, and support trading in new asset classes by synthesizing them as digital assets. In addition, that immutable ledger can potentially solve a major data headache—the process of tracking and reconciling market data usage against license agreements, to ensure firms aren’t over-using and under-reporting data usage. DLT management tools from firms like startup TradeX could establish an accurate record of who is permissioned to use data, and leveraging digital keys, can ensure that only those individuals or applications actually receive the data.

But what about the elephants in the room? I mean Google, Amazon, and Microsoft, whose clouds underpin an increasing amount of capital market systems. AWS not only provides hosting (including for exchange-specific clouds, such as Nasdaq’s), it also runs the AWS Data Exchange. Google, likewise, is working with exchanges like CME Group to deliver data via the cloud. And Microsoft? Not only has the company announced major partnerships with FactSet and State Street, but it is teaming with Pimco, Man Group, State Street, IHS Markit, and McKinsey & Company to form a new tech company geared toward solving operations pains for buy-side firms.

Hey, I said market data wasn’t innovative. I never said the innovators wouldn’t find it attractive. And at the end of the day, these disruptors will see opportunities in areas that are slower to drive or adopt innovation by themselves.