Exabel, before it was obvious

Inspired by Peter Wagner’s recent piece on Snowflake I’d like to take a moment, as we celebrate our latest seed round, to memorialise our thinking around Exabel’s strategic direction. Here are some of the more, and perhaps less, obvious reasons we think we’re on fire along with a hint or two about what sets us apart from the crowd.

The obvious

We chose to address buyside equity investment teams as the first segment for Exabel’s data and insight platform for several good reasons:

  • The capital markets “Data and Analytics” category is already big, growing at a healthy lick and is ripe for disruption. ($34BN and 10% CAGR as of writing for the “Bloomberg super-category” and $2BN each for the Alt-data and AI-tools sub-categories, themselves growing at a riotous 20-50% depending on whose estimation you value.) Investors continue to be insatiably demanding of, ever more, data-driven insight.
  • By “ripe for disruption” we mean this space is dominated by huge, slow-moving incumbent vendors, saddled with hard-to-unwind commitments to unpopular old world “locked-in” data eco-systems. The dinosaurs don’t deliver much in the way of technology innovation either, instead they’re reduced to growth-by-acquisition which in turn results in horribly disjointed user experiences. Available analysis tools are either shiny but relatively simplistic Tableau-style visualisation layers, or they’re so complex they need to be operated by Phd level quants and data-scientists, introducing layers of interpretation, handoff and time delay between investing teams and valuable results.
  • Demand for, and the hype around, data-driven insights are sky high, but few have succeeded in landing value in-house. A few phenomenally successful fund superstars (think Rentech, Two-sigma or Bridgewater) have demonstrated the astonishing out-performance that the right combinations of data and technology can deliver, but the upfront time, talent and technology barriers to following their lead are prohibitively high. Blueprints for success are jealously guarded trade-secrets sold to the highest bidder by the few experienced experts who find a way to circumvent their anti-compete clauses.

The less obvious

During the nearly 4 years of our technology incubation phase we’ve learned a thing or two.

While the above are all good reasons to paint a target on the investment manager market segment, we needed to bring some fresh thinking to a space which has proved notoriously difficult for young startups to successfully address at scale. During the nearly 4 years of our technology incubation phase we’ve learned a thing or two, solved some tough problems and built a world class, exceptionally talented team to execute on our strategic goals.

  • Alternative data is not nearly as widely distributed as one might imagine, particularly given how widely reported its potency has been over the years. In fact, the Alt-data eco-system is surprisingly immature and inefficient, with sellers struggling to articulate, and buyers struggling to evaluate, the value of all but the most obvious dataset offerings. In many cases, however interested investment teams are in Alternative data, they simply lack the quant / data science skills and technology infrastructure to spot, prove and land value. We saw the opportunity to remove the two biggest barriers to success, providing first the platform for value to be showcased by a provider, and then the mechanism for signals and insight to be packaged and delivered direct to the teams who care about them most. We believe that new eco-system entrants like the Amazon Data Exchange (ADX) and evolving standards for pushing ETL upstream will combine with our capability to accelerate adoption across a far larger market than has previously been addressable.
  • There is an often underestimated base layer of market, price history and entity relationship structuring data, the assembly and maintenance of which is a pre-requisite before we can even begin to tackle more novel alternative datasets, complex modelling problems or perform reliable historical backtests. This scaffolding is non-trivial to assemble and is critical to achieving reliable, trustworthy results. The Exabel base-data layer is a critical piece of our IP which gets more valuable with every corrected price and new idiosyncrasy identified and handled.
  • Fully automating our financial analysis and AI modelling pipeline in such a way that it could be operated responsibly by non-technical investment team users, without needing interpretation by Quants, was a far harder problem than we expected. It has been the topic of several recent discussions that data science pipelines are so human-dependant that AI startups can’t scale like pure SaaS product companies and instead look more like services organisations. We definitely put this in the “hard but not unsolvable” category and have succeeded in building a reliable, fully unattended backend to the Exabel platform. This means we can, and will, scale with a cloud product, not a services, gross margin.

The secret sauce

Anyone with real experience building data-driven insight in a buyside investment operation will recognise some or all of the points above as table stakes in the proposition to bring a viable new data and insight offering to market, but “table stakes do not category-kings make…s (?)” so what is the secret sauce that will make Exabel shine above all others?

People make, people use, people love and people recommend.

The true secret in Exabel is first our people and, through them, our understanding of, and commitment to, those people who use and love our product. As I mentioned earlier, we’ve gathered together a world class team of front and back-end engineers, mathematicians and data scientists and then complemented this core technical team with applied quant and portfolio management talent, bringing experience from some of the world’s most successful investment funds. Finally we add a commercial team with experience from some of the globe’s hottest technology scale-ups and we have ourselves a formidable execution machine.

Delivering truly easy solutions to tough problems at scale is the pinnacle of excellence.

I’m privileged to spend significant time around some wicked-smart people. I’ve noticed that a common pitfall of hungry, super-smart teams is to hunt for the biggest, hairiest problems on earth and solve them with incredibly clever, but also very complex, technology solutions. While these solutions may be ok for the very “top of the pyramid” specialist buyers, they can rarely be “scaled-down” to “scale-out” to a larger mid-market audience.

What the world really needs are super-easy solutions to really tough problems.

  • At Exabel we recognise that delivering truly easy solutions to tough problems at scale is the pinnacle of excellence we constantly strive towards.
  • If we have to make a choice between being the best or the easiest, we choose to be the easiest, every time. (Building a great business around being easiest will eventually buy us all the time and resource we need to also consistently be best in the end!)
  • It is our intention to make those who use our product not just like it, but love it, not just recommend it, but rave about it, for that we need a user experience that is not just great, but addictive!

So in summary, we’ve got a world class team addressing a big and fast growing market opportunity ready for disruption, with a scaleable platform that has built and continues to build hard-to-replicate IP assets over a 4 year period and we’ve landed this technology into a beautiful, valued, easy and addictive user experience loved by our early adopters … and that, ladies and gentlemen, is why we’re on fire!

– Exabel CEO Neil Chapman

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Exabel is a financial technology company based in Oslo, New York and London.

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