In the world of data analytics, machine learning and AI, you need to work across your entire dataset to get the most insights. If you’re operating a bank in multiple countries, for example, you need to be able to pull records from each of your locations. 

As simple as that sounds, it’s a remarkably complex process when you consider that each of those countries has its own laws around what personal data you can access and how you can access it. The traditional method of hauling data on a shared server to run algorithms doesn’t work when you’re dealing with sensitive financial or health information.

Things get even more complex when multiple companies want to share data (as drug companies developing a vaccine may want to do, for example).

We invested in DataFleets because co-founders David Gilmore and Nick Elledge found an elegant solution to this messy data problem. The company’s privacy-preserving data engine lets data scientists access and analyze data wherever and however it’s stored, without compromising privacy. Employees get fettered access to the data they need to do their jobs, and companies remain compliant with privacy laws because data stays where it’s supposed to be.

We felt it was the right team to solve the problem. David Gilmore has a wealth of experience building big data systems at Digital Reasoning providing solutions to large finance and health care providers.; Elledge has the right business background; and they had a great working relationship based on their shared history. Most importantly, they shared a vision for what they wanted to achieve. 

“We’re moving from a data ownership to a data access economy,” Elledge told TechCrunch last month.

We liked the business because we saw a much bigger opportunity than just the banking vertical that they are currently focused on, many other industries share the same data restrictive characteristics that need Datafleet’s solution like healthcare.

The need for fettered access to data across borders and organizations is only growing, and DataFleets is taking a novel approach to solving that complicated problem. Assuming the team can execute, that’s generally a recipe for success.