This session will include a moderated Q&A featuring questions from the live audience.
The past eighteen months have seen the accelerating adoption and democratization of innovative analytics platforms, techniques, and tools supporting end-user analytics. The number of data users benefiting from self-service analytics continues to increase, and data users are becoming more sophisticated in their application of machine learning and artificial intelligence models. The appetite for analytics is being fed by the normalization of, and integration with, data management alternatives (such as graph databases, time-series databases, and other NoSQL platforms), and the hype and hysteria around generative AI are rapidly transitioning into practical strategies for evaluation, adoption, and monetization.
All these analytics activities are critically dependent on having a modernized data platform that can meet the data users’ information needs in a performant yet governed manner. In this talk, David Loshin will discuss the impacts of emerging analytics demands on data platform architecture.
Attendees will learn about:
- Modernized data platform requirements for self-service analytics
- Data fabric capabilities necessary for meeting data user information demands
- Data platform techniques for performance
- Understanding and meeting data requirements for generative AI
- Integrating governance for quality, compliance, and ethical concerns