The Economist was famous for publishing the quote ‘The world's most valuable resource is no longer oil, but data’ back in 2017. And as the volume of data continues to increase exponentially, there is a false perception that this volume automatically translates to value. So, if gathering more data isn’t useful and gathering more useful data isn’t always easy, how can you ensure your data achieves its maximum value? From Alqami’s experience navigating the data marketplace, we have developed a scorecard which we use to rate a company’s data set based on certain characteristics, such as volume, breadth, uniqueness, frequency, history, and more recently the ability to link transaction data to publicly traded companies. As an established business it may be more difficult to implement necessary changes operationally to gain a higher value score. However, it is not impossible and just requires a change to the traditional mentality that large volumes of historic data is of itself valuable. Conversely, the opportunity for start-ups is certainly not the volume of data that they have to begin with, but the ability to set up efficient data operations from the get-go and to enrich their data and create new revenue streams through data services.
Many tech companies these days generate an interesting “exhaust data” as a by-product of their core activity. For example, a SaaS provider may know what corporations purchase, at what volumes and price, or a connected health application may know who gets sick, when, where, what pharmaceutical products were consumed during this period and what were the efficacy of those products. These companies are building data sets that are of immense value to investment managers and buy-side firms looking to beat the market, with the majority not even knowing it. If you are sitting on a growing data asset and trying to figure how to create value through licensing feeds on Wall street, below are some practical tips.
Raw data vs Data products
The first myth we want to resolve is the fact that data-driven products, such as analytical capabilities or insights through API’s are not necessarily more valuable than raw data to its end user. As a business you probably wouldn’t create a raw data feed license as a product to customers on your platform, as they would then have to apply analytics and visual capabilities in order to produce valuable insights for them to use, so what would be the value of your product then? Instead, for example, a procurement platform’s product would potentially aggregate, analyse and present their users data in a intuitive graphical format with percentages showing profit vs loss on items. However, you should also explore the opportunities to monetize the data being generated by your platform by providing it in raw form. This is in high demand from investment managers, particularly as the need for alternative data increases. In fact, some of the world’s largest, innovative banks, hedge funds, asset managers insist on accessing data in its raw form over anything else.
What do hedge funds do with the data?
Hedge funds try to leverage alternative data to gain an information edge over their competitors and generate investment returns, through accurate predictions. They use complex portfolio construction and risk management techniques, and can invest in all sorts of different markets to hedge their bets and as the computer gradually takes over, Big Data and technologies, such as AI are playing a more crucial role in the economy in general. But particularly true at many major ‘quant’ funds, which have been using mathematical models or algorithms to evaluate investments for a long time. And increasingly also true for ‘fundamental’ hedge funds who have traditionally analysed individual stocks or the market as a whole. Ultimately, they want knowledge of something that few others know so that they pre-position themselves before others find out. According to recent study by AIMA, the main uses of alternative data by hedge funds is to help, improve investment decisions, source new investment opportunities and generate outerperformance. Alternative data now offers an opportunity to do this on a different scale and at a higher level of sophistication. More recently, hedge funds have broadened their interest to all sorts of datasets including web crawled data, data sourced from expert networks, consumer spending/lifestyle data, business performance metrics, online reviews and social media sentiment, which have been named by AIMA as the Top 5 alternative data sets used by market leaders. Some of this data comes from companies that are just trying to monetize their exhaust data; other data sets are coming from companies whose primary business model is to offer this data.
What else do they consider when assessing the value of a data set?
Other important characteristics include:
Breadth; any data set that is global or covers a region (APAC, EMEA, North America, LATAM) will yield a higher valuation.
Volume; a data set that contains a high proportion of the available data on the subject will tend to be more valuable. A higher proportion of the available data will reduce bias and hence reduce statistical errors.
Stock symbols; data to be linked to publicly listed companies for them to include them in their investing models. As a result, the more linked public companies, the more valuable the data.
Uniqueness; private data that would be near-impossible to replicate is considered more valuable than public (scraped) data.
Frequency; daily data tends to be more valuable to fund clients. Weekly and monthly datasets are still valuable to certain types of funds who are longer term in their approach.
And finally, data that is highly consistent with limited errors or gaps tends to be much more valuable.
Are you allowed to sell your data?
When evaluating a data set these considerations also come into play:
Data should be strictly anonymised, meaning no PII data,
- Good news is that hedge funds are not advertisers and don’t care about particular individual
- Any restrictions on the use of a data set will reduce its value
Liability and risk,
- Potential liability and risks associated with the data use will reduce its value.
How much are they willing to pay?
Hedge funds are an extremely opaque industry and each fund will be willing to pay something different depending on the perceived value of the data set. That being said, from our experience hedge funds are willing to pay a median of $100,000 per license feed. To get a sense of overall market size, there are probably 10,000-15,000 hedge funds. It is worth mentioning that banks and other types of investment managers can also be customers for your data assets.
The hedge fund world can be challenging to navigate, particularly as an individual business and if data services isn’t your core business. There is also a wide cultural gap between corporate and the hedge fund world. This is the opening that Alqami sees an opportunity to bridge, especially as the interest in alternative data is surging.