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 especially for online channels. Knowledge Graphs combined with machine learning are driving three key data science capabilities to deliver the business outcomes organizations need:
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 and deliver call center efficiencies. 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 three times the business value from the data.
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. Knowledge graph combined with machine learning is central to Intuit’s transformation into an AI-driven expert platform.
Join us for this latest Data Science Central webinar as we share design considerations and deployment best practices from these case studies by combining knowledge graphs with machine learning.
Speaker:
Dr. Victor Lee, Head of Product Strategy and Developer Relations – TigerGraph
Hosted by:
Kurt Cagle, Community Editor – Data Science Central
