The big news in the data world this month has been the merger between LSE and Refinitiv. It is unlikely to be last such tie up, as @jbaksht puts it in a recent tweet below. One obvious reason is that for exchanges, monetising their dataset is becoming an ever more important revenue source. In many cases, data is more of a revenue earner than exchange fees.
Exchanges are buying data companies because they need growth. Large information businesses are scarce. Expect to see a lot more consolidation on the heels of the Refinitiv + London Stock Exchange merger. #FinTech pic.twitter.com/KnPQZTa82w
— Jeremy Baksht™💣 (@jbaksht) July 31, 2019
However, within the financial world, it is not just exchanges who can monetise their datasets. Banks and other organisations which aggregate large amounts of financial data are looking towards ways they can gain value from their datasets. As part of their everyday business, banks also generate vast amounts of “exhaust data” as a result of the transactions they make for their clients. A bank can’t suddenly open up their vast databases and sell every scrap of data externally, given client confidentially. However, there are lots of other things banks and similar organisations can do with their data to provide value for clients, whilst satisfying legal requirements.
Of course none of this is particular easy. Banks often don’t have a good idea which datasets they have. Monetising data requires a large amount of teamwork between many different teams, who might not always be very cooperative with one another. One way to help with the data monetisation process is to bring in an external party to provide a neutral and objective viewpoint. Cuemacro can help your organisation whether it is a bank, an exchange or a corporate, to monetise your datasets. We can work with you to identify which datasets could be useful, how to value them, and which data products could be developed from them. Below, we give a few ideas to give you a start!
Banks and other financial organisations can create aggregated datasets, that can give useful information to their clients, whilst at the same time blurring details that might identify particular counterparties. This might be in the form of aggregated flow data. One such company which does this is CLS, which settles the majority of deliverable FX trades. They have released an FX flow dataset, based upon the trade flow they observe. I wrote a research paper about this dataset.
Banks spend vast amounts of money creating aggregated datafeeds from many venues in order to trade on these markets electronically. In FX there is a huge proliferation of venues, which makes this a particularly expensive and cumbersome process. Banks could provide access to such data within a single aggregated datafeed for their clients. Of course there are still licence costs, but for a client from a technology perspective, it’s probably easier to connect to one source rather than many.
The examples I’ve given are only a handful of many different potentional ways a bank can monetise their dataset. It isn’t easy, but the example set by exchanges, suggests that banks really need to get a handle on monetising their datasets. In an environment, where traditional revenue sources for banks are coming under strain, data monetisation could become a key revenue stream for banks. If you’d like to know more about how your financial organisation can monetise your dataset, get in touch with Cuemacro. Also please do read The Book of Alternative Data, which I’m coauthoring with Alex Denev and will be available on Wiley in 2020.