For TigerGraph, it's the right time for graph databases.
As organizations harness more data from more sources, they need to discover the data points that relate to one another and combine them to derive insights based on that data. Traditionally, organizations have stored their data in relational databases, and while strengths of relational databases include accuracy and ease of use, one of their limitations is lack of scalability.
Graph databases, however, don't struggle with scale.
Unlike relational databases, in which data points can only connect with one other data point at a time, graph databases enable data points to simultaneously connect with multiple other data points, enabling users to quickly discover relationships and derive insight.
Social networks such as Facebook and LinkedIn use graph technology to connect people, and other common use cases for graph technology include supply chain management and fraud detection.
TigerGraph, founded in 2012 and based in Redwood City, Calif., is a graph database vendor.
As data grows exponentially, TigerGraph founder and CEO Yu Xu says organizations will recognize the shortcomings of relational databases and seek out the speed and scale of graph databases. As a result, the vendor has aggressive goals, both in terms of product development and revenue growth.
To lead that product development, TigerGraph recently hired Jay Yu as its new vice president of product innovation. He previously spent 18 years at Intuit where he most recently served as a distinguished engineer.
But Yu won't only be tasked with developing the vendor's product strategy. He will also oversee the vendor's new innovation center in San Diego and be responsible for hiring about 100 people to work there.
Recently, Xu and Yu discussed their vision for TigerGraph, including financial goals in preparation for a potential initial public stock offering and technology goals such as making graph technology easy to use. In addition, they spoke about what they see as a paradigm shift from relational databases to graph databases, and how TigerGraph is poised to take advantage.
Jay, what drew you to TigerGraph after 18 years at Intuit?
Jay Yu: In the last three years at Intuit, I was driving a knowledge graph project. I developed my passion for graph there, and was fortunate enough to evaluate TigerGraph as a technology and benchmark them against other vendors. Over the years, I developed more passion for graph, and when the opportunity came [to move to TigerGraph], I wanted to jump in and focus on advancing graph technology and making an impact on the industry. I truly believe in graph and believe that there's a right way to do graph.
Also, the leadership team drew me to TigerGraph. When I talked to Yu Xu, we had long discussions and shared our dreams and vision. The vision is that graph technology is going to take over the whole world, whether people like it or not. We believe that in five to 10 years, graph is going to replace relational technology. TigerGraph is at the forefront of that. Finally, there's a huge difference between startups and large, established companies. Intuit is great, but being that big, it can't [innovate] as fast. In terms of having a direct impact on technology, I can have a bigger impact by coming to TigerGraph.
Yu Xu, did you previously have a director of product innovation?
Yu Xu: Previously, we did have leadership in product, but not the position and responsibilities Jay will have. His position is about more than just the product. We had people to plan a release, build a roadmap, do customer education and respond to customer feedback. He's going to do that. But he's also going to focus on innovation. He'll do things around the graph interface, and also we're going to build more applications for things like supply chain optimization and customer journey. We want customers to see the value of graph more quickly.
Jay, as you take over your new role, what's your vision for TigerGraph over the next few years?
Jay Yu: What I'm going to be focused are a couple of things. One is, how can we make sure graph technology can be easily adopted so people don't have to go through a huge learning curve? That means simplifying and streamlining it to make it accessible to normal developers, giving them tools they're familiar with integrated with API interface like a GraphQL or Power BI. That will help make sure people can realize the benefits of graph right away so they don't have this long period of learning and experimentation before they can see the business value. That's going to be one of the top areas I'll be focused on. This includes having a layer of tooling that makes it super simple, making it so they don't have to learn the graph query language and we can translate it into something they're familiar with. In our innovation pipeline, we're also thinking about combining graph technology with augmented intelligence, natural language processing, and making a low-code/no-code way for people to utilize graph technology.
Jay YuVice president of product innovation, TigerGraph
Both Yu Xu and I see the power potential of graph, but unfortunately that power isn't being utilized because people don't understand it or are afraid of it. They're so comfortable with relational technology, and we want to make it super simple for them to move on to what we consider the future of the data platform.
Yu Xu, same question -- what's your vision for TigerGraph over the next few years?
Yu Xu: This year, our goal is to triple our revenue. Another goal is to reach more than $100 million in annual recurring revenue in less than three years. That will essentially get us ready for an initial public stock offering. That's the financial side.
In terms of team growth, we've doubled the size of our team from the end of last year.
On the product side, we want to be the leading graph company. Our vision is that in five years, the new generation of developers will choose graph databases and use graph query language as the de facto choice for building new applications. Just as people picked up Java and SQL, we want the new generation to use graph query language.
Now that we have the graph database to support the graph query language, there's no reason the new generation can't build on top of graph databases. Graph is the next big thing for databases.
Jay, what are some trends you're seeing in analytics and data management, and how will those trends shape your plans for TigerGraph's product development?
Jay Yu: The obvious trend is that data is growing exponentially. When you're overwhelmed with data, the relationship, the connection, the rich semantics around data become more important. Graph is the way to represent that. Anything else is not going to be able to scale. People are hungry -- they want more data and they want to discover more insights. Things like data training, machine learning and deep learning can only go to a certain scale. People are realizing that to overcome those limits, they need to augment machine learning and deep learning with what we call symbolic AI, and graph knowledge is a huge part of that. That plays really well into TigerGraph's vision.
We believe that graph will take over … because people will realize that's the only natural way to represent their data and process their data without moving their data around.
Yu Xu, again the same question I posed to Jay -- what are some trends you're seeing in analytics and data management, and how will those trends shape your plans for TigerGraph's product development?
Yu Xu: In technology, a paradigm shift doesn't come around very often, but we're fortunate to be in this stage where traditional relational databases are not going to be the king anymore. People are realizing the limitations of traditional relational databases, but there is a solution. The issue is that they can't scale out. If you have complex relationships and complex problems, relational databases [struggle]. But graph databases, mathematically speaking, are more powerful. Now, with more data, the timing is perfect for graph. Also, with digital transformation, people need more insights. They need to go beyond simple aggregation and simple reporting. They want to give customers the best journey, the best customer experience, and that's all about connecting the data.
We're building applications, building UI, adding machine learning capabilities on top of TigerGraph. We're not just going to be a simple graph database company. Graph databases are so intuitive to so many applications like social networks, supply chains, power grids. We're at this unique stage where graph databases can combine data and deliver the insights people need.
While graph databases have the potential to be one of the next big things in data and analytics, what are some challenges TigerGraph faces?
Jay Yu: For the industry, the overall challenge is the adoption curve. Graph is so new, and people are so used to relational technology. TigerGraph could be at the forefront, but we need the whole industry working together to broaden the category and help people to overcome the hurdle and be part of the future instead of living in the past.
Same question to you, Yu Xu?
Yu Xu: The challenge is having the right people at this stage. We don't want to dilute the talent and change the culture of a startup. We want to continue our growth and expand.
Editor's note: This Q&A has been edited for clarity and conciseness.