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Without a doubt, 2023 has shaped up to be generative AI’s breakout year. Less than 12 months after the introduction of generative AI large language models such as ChatGPT and PaLM, image generators like Dall-E, Midjourney, and Stable Diffusion, and code generation tools like OpenAI Codex and GitHub CoPilot, organizations across every industry, including government, are beginning to leverage generative AI regularly to increase creativity and productivity.

The best marketing is truly data-driven, creating powerful product promotions and offers through an understanding of customer needs and preferences. But for many organizations, building this understanding is more akin to solving an ever-growing jigsaw puzzle (with no easy edge pieces!) than reading data insights from a beautiful dashboard.

When businesses share sensitive first-party data with outside partners or customers, they must do so in a way that meets strict governance requirements around security and privacy. Data clean rooms have emerged as the technology to meet this need, enabling interoperability where multiple parties can collaborate on and analyze sensitive data in a governed way without exposing direct access to the underlying data and business logic.

Historically, only a few AI experts within an organization could develop insights using machine learning (ML) and predictive analytics. Yet in this new wave of AI, democratizing ML to more data teams is crucial—and for Snowflake SQL users, it’s now a reality.

BigQuery Omni’s new cross-cloud materialized views lets you perform cross-cloud analytics.