Friday, April 16, 2021
TigerGraph Cloud, a cloud agnostic database-as-a-service offering, enables users to leverage graph analytics on AWS, Azure, and GCP. TigerGraph Cloud not only fully manages clusters, but also provides a use case library via starter kits, which greatly reduces customers’ time to business value and decreases the learning curve to specific graph database use cases such as fraud detection, recommendation, customer 360, AI and in-database machine learning. This session provides an end-to-end overview of TigerGraph Cloud with a short demonstration that showcases starter kits and other unique functionality.
How Intuit, Jaguar Land Rover, Xandr and Unitedhealth Group Are Driving Business Outcomes With Graph Database & AIJoin on Crowdcast
The COVID-19 pandemic has accelerated the pace of digital transformation across all industries. Organizations are looking for ways to accelerate their analytics, AI and machine learning projects to increase revenue, manage risks and improve customer experience. Join us to learn about the three core capabilities necessary to drive the business outcomes:
- Connect internal and external datasets and pipelines with a distributed Graph Database - UnitedHealth Group is connecting 200+ sources to deliver a real-time customer 360 to improve quality of care for 50 million members; Xandr(part of AT&T) is connecting multiple data pipelines to build an identity graph for entity resolution to power the next-generation AdTech platform.
- Analyze connected data for never-before insights with Advanced Analytics - Jaguar Land Rover has accelerated supply chain planning from three weeks to 45 minutes, reduced supplier risk by 35% and is driving 3 times the business value from their data. NewDay, a leading specialist financial services provider and one of the largest issuers of credit cards in the UK uses advanced graph analytics to prevent and preempt financial fraud.
- Learn from the connected data with In-Database Machine Learning - Intuit has built an AI-based customer 360 with in-database machine learning for entity resolution, personalized recommendations and fraud detection. It is driving their transformation into an AI-driven expert platform. 7 out of the top 10 banks are driving real-time fraud detection and credit risk assessment with in-database machine learning.