TigerGraph Raises $105 Million to Accelerate Graph Analytics on the Cloud
Record Funding for Graph Database Category, Led by Tiger Global, Signals the Next Wave in Analytics to Fuel Powerful Insights for Artificial Intelligence
TigerGraph, provider of the leading graph analytics platform, announced it has raised $105 million in Series C funding, the largest funding round to date within the graph database and analytics market. The round was led by Tiger Global and brings TigerGraph’s total funding raised to over $170 million.
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The investment reflects TigerGraph’s growth and the massive potential as businesses continue to move to the cloud. With the transactional and analytical workloads moving to the cloud made possible by companies like Snowflake, Confluent, and Databricks, TigerGraph is quickly becoming the graph database of choice to connect, analyze and learn new insights from the data. With its distributed native graph architecture, TigerGraph helps organizations scale fast and analyze many different aspects of data to be used with each other to form new models and generate new insights. These new patterns and insights enhance a company’s analytics or machine learning capabilities and can be deployed anywhere with multi-cloud flexibility and support the data security requirements for regulatory compliance.
Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace, driving an urgent need to find new insights about their customers, products, services, and suppliers. Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, and identify new insights such as similarity patterns to deliver the next best action recommendation. Data-driven solutions require intelligent apps and connected data that leverage powerful graph engines to connect, analyze and learn from the data companies are storing in the cloud. These events helped TigerGraph experience massive growth, more than doubling revenues and customers over the past year. It has also continued building a very active and fast-growing developer community, receiving the highest marks in a recent analyst report.
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“By 2023, graph technologies will facilitate rapid contextualization for decision making in 30 percent of organizations worldwide,” according to Gartner.1 Mark Beyer, Distinguished VP Analyst with Gartner, shared the following in a Gartner report regarding the adoption of graph technology in the enterprises, “To Graph or Not to Graph? That Is Not the Question — You Will Graph.”2 Organizations of all sizes are adopting graph-based analytics and AI by leveraging the relationships in the connected data to drive better outcomes. TigerGraph is galvanizing the graph and AI community, organizing the first open industry Graph + AI Conference featuring presentations by innovators including UnitedHealth Group, Jaguar Land Rover, Intuit, Intel, Xilinx and Accenture. TigerGraph is deeply involved and on the steering committee for the development of the Graph Query Language standard, GQL, along with other database vendors such as Oracle and Neo4j, and will enthusiastically support the GQL standard immediately upon finalization.
“For over 40 years, businesses’ #1 data management challenge has been how to easily ask business questions across all of their data in real-time to guide their operations. The human brain connects data to derive new insights and helps us decide what to do next. TigerGraph’s mission is to power an enterprise brain with graph and AI that discovers these new insights within the enterprise data stored in the cloud and on-prem,” said Dr. Yu Xu, founder and CEO of TigerGraph. “TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns. This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”
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