Companies have access to more data than ever before – according to IDC, worldwide data will grow 61% by 2025. However, when it comes to adopting AI, there is a difference between companies who merely have internal data and those who have precise, accurate data. The first step to delivering trusted AI is having the right type of data. R "Ray" Wang, principal analyst and founder of Constellation Research, joins The AI Forecast to discuss the value of precision data as we enter what he calls “the dark ages of data”.

To accelerate development, organizations need to supercharge more users to immediately discover and collaborate on relevant data, apps, and models. At the same time, organizations must ensure the platform they work on is secure and that the right people have the right access. Protecting sensitive and/or Personally Identifiable Information (PII) is critical. The Snowflake Horizon Catalog provides built-in governance and discovery for the AI Data Cloud to make all of this easy.

Are you struggling with the challenges of managing your data lake as you strive to address issues ranging from security headaches to troubleshooting complex pipelines? This BUILD 2024 session addresses those challenges with a look at how Snowflake makes it easier to onboard Apache Iceberg into your data lake. The session dives into new features that simplify security, streamline data ingestion and transformation, and enhance integration with your existing tools. You’ll also see how Snowflake provides enterprise-grade redundancy to the data lakehouse architecture, making it easier for teams to work together globally.

Hear from guest speaker Adrien Chenailler, Head of Machine Learning at OCBC Digital Bank on his successes using Cloudera.

Join us for another enlightening session in our Weekly Walkthrough series, "FinOps Metrics That Matter," where we focus on the critical aspect of rightsizing your Databricks infrastructure for optimal performance and cost-efficiency. Achieving the right balance between performance and cost is paramount. However, a striking 80% of data management experts grapple with precise cost forecasting and management (Forrester). The primary culprits? Insufficient granular visibility, data silos, and a lack of AI-driven predictive tools.