Tammer Kamel spent some formative years as a quant. Now a self-declared "Wall Street refugee", he runs five-year-oldold Quandl, a data outfit tailored for quantitative analysts. Quandl burst on to the scene by making data sets more useful, delivering data the way a modern quant wants data. The company has recently expanded into developing its own alternative data sets that are being used by some of the smartest investors around.
Kamel joins us on the Tearsheet Podcast this week to talk about how the data business is evolving around the changing nature of quantitative investing. We talk about where demand is coming and how his firm is coping with the challenges of building for scale.
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Below are highlights, edited for clarity, from the episode.
As a quant, what were your biggest pain points?
The frustration I had was kind of a universal pain point for quants. Data is never easy to find and it's never easy to get into the format of the system you want. If you talk to any analyst, they'll tell you they spend half their lives -- maybe more -- trying to find, format, validate, organize, merge, and ultimately set up the data for the analysis they want to do. The analysis is often the pleasant and easier part of the task.
The problem is data is spread out across all different systems and protocols. One data set you can get as a .csv and another as an API -- still others may be in a PDF and you have to pull the data out of the PDF. You have to remember that many of the incumbents in this space aren't motivated to let the data they offer flow easily to many places. The whole terminal model is built on this idea that one person will sit at this piece of hardware and use this software. it's a great business model but particularly incompatible with how quants need to work. They need to pull data from different sources, amalgamate them, run analysis and pump this data through programs they've written. The status quo on Wall Street doesn't facilitate this.
The move into providing alternative data
With Quandl's core business, we do really well with niche quants, fintechs, small funds and lots of retail-type customers who need data. But, it's very difficult for Quandl to displace a large incumbent at a large institution. It's a hard sell to go into a bank and tell them to rip out Thomson Reuters because we have a better API. The growth market for us is to bring new, powerful data to these institutions. If I go into these institutions and say, here's a data set that you cannot get this data set on Bloomberg and it makes you better at doing what you do -- well, that's a much easier sell.
More fundamentally, we realized that two things are going on: one one side of us, the global data explosion is happening with satellites, drones, mobile phones, the internet of things, and every company is becoming data-centric at a time when professional investors are very hungry for new ways to generate alpha. There is no more potent alpha source than an information advantage. We are looking for gold nuggets in the sea of data that the world is awash in.
How do you price unique alternative data?
Pricing is tricky because the value of a particular data set depends on the marginal impact it has for a particular customer. I can bring a data set to Customer A and it can be absolutely potent for them because they now have access to something they've never had before. I can bring the same data set to Customer B, who has access to other data sources, and ultimately the marginal impact of this data source is less than for Customer A. We price data based on what we believe the full impact of the data set is. We know what it's worth in an absolute sense because we think like investors. Typically, that looks like $40,000 to $500,000 a year, depending on the data set.
