People to Watch 2022
Yu Xu
Founder & CEO, TigerGraph
Scale and performance have been TigerGraph’s calling cards since the company burst upon the graph database scene a few years ago. Are those characteristics still resonating with customers today?
Yes. Enterprises continue to accumulate more data and want to gain deeper insight from their data. Scale and performance for advanced analytics are still critically important for enterprises to make timely and better informed business decisions.
Graph databases have been around for years. What is stopping organizations from using them more widely?
Graph momentum is no doubt accelerating. Gartner predicts that 80% of enterprises will use graph databases in 2025, a 7X growth. In the past, previous generations of graph databases didn’t scale to big datasets or perform for advanced analytics.
This is a big reason why companies are not using graphs widely. For example, many TigerGraph customers – such as UnitedHealth Group and some of the largest banks – were not new to graph. They had been using graph solutions for quite a while before TigerGraph. The difference? TigerGraph enabled them to ingest their biggest datasets to get the maximum query performance needed (that was otherwise unattainable with previous generations of graph databases).
Since TigerGraph launched out of stealth about three years ago, we have been helping such customers to turn their PoCs/ demos to production, and enabling them to leverage the full benefits of graph for more use cases, across larger teams. These customers have gained monumental business value.
Another thing would be the lack of standardization of a graph query language. A graph database is the most powerful database (in terms of expressiveness) which also means graph query languages are flexible and have advanced features not available in other database languages.
Lack of standardization slows down graph adoption, but this is going to change soon! ISO, which standardized SQL for RDBMS about 40 years ago, is going to release an international graph language named GQL in about 18 months. My team at TigerGraph has been working with other companies on the ISO committees to make sure GQL is powerful, easy to use, and similar to SQL. We are excited to share more in the coming months.
What do you hope to see from the graph data community in the coming year?
We are seeing exciting progressions when it comes to using hardware to accelerate graph analytics, specifically as it relates to ways graph algorithms are intensively computing to unleash deeper insights. TigerGraph is working closely with Xilinx and Intel on hardware accelerated graph analytics. We hope to see more innovations in this space.
Additionally, it is no secret that graph augments current AI and machine learning solutions well. In fact, as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology.
In the coming year, TigerGraph will release more graph-AI solutions and data science libraries. Our hope is that more data scientists will leverage the power of graph in their projects.
