With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.
Retaining customers is more important for survival than ever. For businesses that rely on very high user volume, like mobile apps, video streaming, social media, e-commerce and gaming, fighting churn is an existential challenge. Data scientists are leading the fight to convert and retain high LTV (lifetime value) users.
Historically, analytics has not always been a priority feature for software vendors. Many applications typically are built with analytics bolted-on later, as standalone tools. But the changing needs of today’s business users has accelerated the importance of providing in-built ways to monitor and explore their data while they use your software.
