Getting different teams, business units and even companies to work together toward a common goal not only maximizes efficiency, but drives innovation. Effective collaboration on data and AI has never been more closely tied to success. At Snowflake, we’re removing the barriers that prevent productive cooperation while building the connections to make working together easier than ever.

This fall, thousands of leaders in the financial services industry gathered at the annual Money 20/20 conference to talk trends in payments, compliance, fraud reduction, treasury and transactions and more. Conversations centered on the theme of “Human x Machine,” and while AI was a focus, there were plenty of other insights around real-time data analytics, security considerations and customer strategies that are guiding the future of money.

At Snowflake BUILD, we are introducing powerful new features designed to accelerate building and deploying generative AI applications on enterprise data, while helping you ensure trust and safety. These new tools streamline workflows, deliver insights at scale, and get AI apps into production quickly. Customers such as Skai have used these capabilities to bring their generative AI solution into production in just two days instead of months.

It’s easy these days for an organization’s data infrastructure to begin looking like a maze, with an accumulation of point solutions here and there. While some businesses find ways to stitch together many tools with complex pipelines, wouldn’t it be better if you could remove some of the steps? What if you could streamline your efforts while still building an architecture that best fits your business and technology needs?

The stage is set for a new era in marketing, and marketers have never had so much data and technology at their fingertips. But to deliver the ROI that enterprises require today, marketers must have a strategic mindset and fine-tune the tools, tactics and approaches in their marketing data stack. Snowflake is here to help marketers evolve and accelerate their marketing impact with our third annual Modern Marketing Data Stack report and global virtual event.

Adtech and martech companies are engaged in a fierce battle for audience attention. Customers are bombarded with thousands of ads and marketing messages every day, and the average attention span is plummeting, so it’s no wonder they tune out — or turn on ad blockers. But it’s not all doom and gloom. The global adtech market is expected to grow at a rate of 22.4% through 2030, and martech’s projected growth rate is 18.5% through 2032.

The Snowflake AI Data Cloud has democratized data for thousands of customers, removing data silos and powering data sharing and collaboration use cases. Many customers have been able to unlock enormous value from their data with Snowflake, including safely collaborating on sensitive data using Snowflake Data Clean Rooms and Data Governance features. However, some highly sensitive data has remained off-limits due to regulatory requirements and privacy concerns — until now.

Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.

To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.