Graph databases are crossing the chasm to mainstream use cases, adding features such as machine learning to their arsenal and becoming more cloud and developer friendly. Last year was a breakout year, and graph database growth and evolution is well under way in 2019.

If you don’t know what OpenCorporates does, this is a good opportunity to learn. Whether directly or indirectly, chances are OpenCorporates has already been valuable to you in some way. OpenCorporates crawls the web and aggregates and structures information about corporate ownership and structure all over the world.

Its database contains information on nearly 165 million companies and counting. Although this information is almost entirely collected from the public domain, from places such as national business registries for example, having it all in one place is very useful. You can think of OpenCorporates as the Google of corporate information.

Whether you are doing market research or investigative journalism, having this information, and being able to query it, is invaluable. As untangling the web of interconnections on corporate ownership and relationships is something that relational databases struggle with at this size and complexity, OpenCorporates is now using a graph database to power its back-end.

OpenCorporates has been around since 2010. It was launched by Chris Taggart and Rob McKinnon, both veterans of the UK open data scene, as more-or-less a proof-of-concept, with three million companies in three jurisdictions (UK, Jersey, and Bermuda). It has now grown to 165 million companies from 130 jurisdictions.

OpenCorporates acts as a social enterprise, putting the public benefit before profit. The data it collects are available either as dump or via an API, and made available via a dual licensing scheme. For commercial use, a fee is charged, while for non-commercial use access is free, and licensing is tailored for each use.

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