Lost in translation: Why semantic chaos is the real AI risk
AI scales fast on whatever meaning you give it. A semantic layer for AI ensures it works from one trusted definition to dramatically reduce risk.
erwin Data Intelligence 15.1
AI doesn’t have a data problem. It has a navigation problem.
AI data governance is the missing navigation layer for enterprise AI, providing the context, meaning, and direction required to drive AI success.
Snowflake heist: How to crack the code on Snowflake ROI
Learn how to unlock Snowflake ROI with a carefully planned seven-step heist.
Beyond the platform: Why Quest and Snowflake are better together for AI-ready enterprises
Maximize your Snowflake investment with Quest: AI-ready data modeling, observability, replication, and governance across your full enterprise data estate.
Why CIOs must address identity & data sovereignty amid geopolitical risk
Geopolitical risk has made data sovereignty and identity sovereignty urgent architectural priorities for AI and cloud. Here’s what CIOs must address.
data products
Housekeeping for AI: Why AI Data Governance, Identity and Platform Control Must Come First
Go full Marie Kondo for AI success. Clean up AI data governance, identities, and platforms in three easy steps for fast, responsible AI.
Data lakehouse strategy: Build a foundation for real-time, AI-ready insights
Explore a practical data lakehouse strategy to support real-time analytics, AI readiness and scalable architecture.
Common observability challenges and steps to overcome them
Learn how observability helps IT pros to make better decisions, enhance reliability, and drive improvements.