Saildrone: Democratized Ocean Data

Saildrone: democratizing ocean exploration and metocean data

Today’s discussion in big data and analytics revolves around Amazon & e-commerce, fake news, Facebook, Clinton & Trump, internet service providers and their client’s search history, online behavior and ad clicks, uber and Google maps. Massive amounts of data are generated and stored on the cloud. While the cost of data transmission and storage has dramatically decreased, advances in the gathering data from the physical world are lagging behind. The ocean, a key component of the global climate ecosystem, covering over 70% of the Earth’s surface area remains largely underexplored.

Enter Saildrone.

Saildrone is a Bay Area startup, which has created an autonomous ocean-faring trimaran that surveys the ocean. The drone is designed by the team who set world land-sailing speed record. Composite and carbon fiber materials form the structural and sail elements of an unmanned vessel, drastically reducing vessel weight relative to conventional survey vessels.

The drone is propelled by its wind sail, and cruises at an average speed 3-4 knots, having the endurance to stay in the ocean from up to an advertised “12+ months” at time.

The drone measures temperature, salinity, wind, wave, current, fishery ecosystem characteristics, still imagery, video and radiation. A partnership with the NOAA provides the cutting edge sensor technology. Satellite uplink allows for real-time data upload and download by scientists. Real-time data upload allows for near real-time analysis of data and therefore on the go mission revision and redefinition, while the drone is deployed.


Saildrone. NYTimes

Business Model & Competitors

With conventional manned vessels costing upwards of $80’000 per day, saildrone brings down the cost of ocean exploration by massively reducing capital and operational requirements in ocean exploration and surveying. At a cost $2500 / day / vessel, Saildrone enables survey applications, previously not economical under manned vessels. The company’s technology could potentially reduce carbon emission spent per exploration survey as well.

While fixed buoys cost on the order of tens of thousands of dollars per year in capex, with opex costs incremental, buoys are restricted to measuring ocean characteristics in a fixed point in the ocean.


Air, agriculture, insurance, manufacturing, oil & gas and shipping industries all utilize meteorological forecasting services. More cost effective access to data will enable measurement, analytics and ultimately feed into a more accurate environmental, fisheries and weather model. Weather forecasting services are predicted to be just over 1.5B market by 2021. Global fisheries are a $148B market. Agriculture insurance product market is $23+B market.

Proposed use of spare capacity & data

Saildrone advertises that 30 days of surveying is worth up to $1M of research ship time in an open research contest. This signals that there is excess capacity. Since the marginal cost of sending out the drones to survey is nil, a new business model is proposed. Saildrone can sail its spare drone capacity, while collecting data and open up a competition to find and extract the most value out of the data.

With Saildrone raising 15M+ Series A in 2016, it has at least a couple of years to tackle the challenge of democratizing and opening metocean data to the world. With only a few pilot missions publicly listed, Saildrone would be wise to find a recurring/subscription/contract use of its’ drones, while developing its’ next generation of technology.





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Student comments on Saildrone: Democratized Ocean Data

  1. Great post! It’s exciting to see that Saildrone brings down the cost of ocean data by so much! The new business model makes sense because Saildrone may not have the expertise on data analytics. However, in long run, I think Saildrone need to build up its analytics arm or a platform for analytics to capture more values and increase customers’ stickiness. Otherwise, companies can choose to buy similar vessels and collect data by theirselves.

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