The rise of the full stack graph startup

July 21, 2014

The last year has seen a growing interest in graphs and the emergence of full stack graph startups. These startups rely on graph technologies to take on established industries like logistics, marketing or law. They find new ways to work with massive datasets and extract insights from connected data.

Introducing the full stack graph startup

According to Chris Dixon, a full stack startup‘s goal is to “build a complete, end-to-end product or service that bypasses existing companies”. Tesla is a good example. The company could have chosen to become a car-battery manufacturer. Instead of that it went for the fences and achieved the much harder goal of becoming a car manufacturer. In the process, Tesla can deliver a great customer experience, doesn’t have to negotiate with older companies and control its margins. All of this because it has chosen to go full stack.

What is a full stack graph startup then? A full stack startup derives an unfair advantage from its graph technology. This unfair advantage can materialize in industries :

  • that are data oriented ;
  • where data is highly complex and connected (ie hard to model with traditional tabular oriented databases) ;
  • and where great value can be derived from the connections in the data ;

As a result full stack graph companies tend to focus on a few key sectors like logistics, social networks or marketing. The most successful full stack graph startup probably is Facebook. As early as 2007, Mark Zuckerberg presented Facebook as a “Social Graph” that links people, tastes, places, objects, etc. Facebook relies on that graph to monetize its marketing platform. The data challenges that come with building a social graph are enormous : the social graph is a large dataset (as in very large), made of connections between entities and the value lies in analyzing these connections quickly.

To solve this problem, on its way to become the company it is today, Facebook has designed a graph-oriented data structure called Tao. Facebook has also backed Giraph, a graph computation framework, that it uses to understand a trillion-edge large graph. By solving these tough problems, Facebook is able to deliver products like “Graph Search” other social networks (I’m looking at you Google+) would struggle to come by.

To be clear : a full stack graph startup is not just a startup that happens to use a graph technology. A lot of startups do but most often, these graph technologies are an operational advantage not a strategic one. Medium uses a Neo4j graph database but would probably survive without it. Strip Facebook from its graph technologies and it’s dead.

Meet the full stack graph startups

Here is a (incomplete) list of some the most interesting full stack graph startups :

  • Crosswise’s technology helps build a device graph. This graph links a phone, pc, tablet and TV to a single user without using any personally identifiable information. With that information brands can target their audience wherever they are ;
  • Crunchbase has been around for a while as a good dataset for the startup world. Recently, it re-branded itself  as “the business graph“. It is not just a marketing move. Crunchbase no relies on a graph database. That means that it can store more information in a more complex way. This change is reflected in the new user interface ;
  • Doximity is a Linkedin for physicians. Albeit flying under the radar it boasts that 4 out of 10 doctors in the US are among its users. Now wonder it has raised $54 million. It relies on graphs to make it easier for physicians to connect with their colleagues ;
  • Elementum is a startup looking to make supply chains easier to operate. They are taking a graph approach to that problem. Elementum wants to get supply chain participants (product manufacturers, their suppliers, the logistics hubs, the retailers and the customers) to drop relational databases and see the supply chain fundamentally as a complex graph or web of connections. With $44M in funding they are off to a good start ;
  • Lumiata provides predictive health analytics. To produce accurate insights and predictions related to symptoms, diagnoses, procedures and medications, Lumiata has developed the world’s first medical graph, which organizes and analyzes hundreds of millions of valuable data points ;
  • Ravel wants to speed up the research lawyers have to go through for each case. Their solution combines graph visualization and machine learning to bring the underlying connections between cases or courts ;
  • Senzari is a music-related startup. They provide MusicGraph that maps the real-world structure of the musical universe. It contains the relationship between millions of artists, albums, and songs. Senzari uses this data to bring targeted musical recommendations ;
  • Shutl is a London-based technology start-up offering a rapid fulfillment service by connecting online retailers with local same-day couriers. The startup company is best known for offering delivery of online shopping orders in 90 minutes or less. It was recently acquired by Ebay ;

Of course, to be successful, these startups also have to build products people love, find ways to monetize the value they create and all the things that come with creating a great business. But by relying on graph technologies, they have an unfair advantage compared to other companies in their fields. And that can be the difference between failure and success.

The burgeoning graph technologies ecosystem offers great opportunities for the entrepreneurs willing to tackle difficult problems. In industries where value lies in graph-like data, startups can offer new services and better performances than incumbents. At Linkurious, a graph visualization company looking to democratize graphs, we are looking forward to a world where companies unlock the value of graphs.

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