Systems | Development | Analytics | API | Testing

AI and the Future of Finance: Decoding Earnings Calls

In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions is joined by Liam Hynes, Global Head of New Product Development at S&P Global Market Intelligence. They discuss how S&P Global Market Intelligence utilizes AI to analyze corporate earnings calls to guide and improve financial reporting. These insights help businesses enhance communication, refine executive performance, and predict market outcomes. The conversation also highlights the use of Snowflake Cortex AI platform and the importance of data-driven decision-making in the financial sector.

Unlocking Disney's Magic: AI And Automation In Multimedia Storytelling

In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions is joined by Nick Winfrey, Vice President of Data Science and Data Strategy at The Walt Disney Company. They explore Disney's extensive use of AI, automation, and data science to optimize advertising and enhance fan experiences. The conversation also delves into the importance of diversity in data teams, the crucial role of AI and ML in media and entertainment, and how Disney leverages audience behavior and loyalty across different platforms.

Snow Report: What's Happening At Snowflake In May

Learn about recent additions to the Snowflake Horizons catalog as well as new resources for updating your data skills in this month's edition of the Snow Report. You'll also find information on Summit 2025, Snowflake's premiere annual conference, as well as links to podcasts, news announcements, and more.

The AI-Driven Enterprise Advantage with Teresa Tung, Global Data Capabilities Lead at Accenture

In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions is joined by Teresa Tung, Global Data Capabilities Lead at Accenture. They discuss how enterprises can accelerate and broaden the application of data to attain more business value through agentic AI, the pivotal role of proprietary data as a competitive advantage, and the need for data practitioners to adapt to new responsibilities involving data quality and AI agent interaction.