Why Your LLM Performance Problems Are Actually Data Infrastructure Failures

Your LLM issues are really data issues

Phoebe Sajor identifies that most LLM failures are rooted in underlying data infrastructure rather than model architecture. Inconsistent definitions for entities like ‘customer’ create fundamental breaks in both analytics and machine learning pipelines. These issues demonstrate that weak governance directly correlates with AI performance degradation.

Why This Matters

Ideal models require high-quality inputs, but technical reality often involves schema changes and weak governance that degrade AI readiness. Without robust metadata management, companies face systemic failures in their AI initiatives. Transitioning to semantic intelligence platforms like Collate allows for better observability across the data ecosystem. This shift from manual governance to automated metadata graphs is essential for maintaining reliable machine learning outcomes in production environments.

Key Insights

• Inconsistent definitions for core entities like ‘customer’ directly break analytics and ML models (Sajor, 2026).
• Schema changes without proper governance lead to catastrophic failures in AI-ready data pipelines.
• Collate provides a semantic intelligence platform built on a semantic metadata graph for cross-ecosystem discovery.
• AI observability is critical for identifying when data issues masquerade as model performance problems.
• Metadata management serves as the foundation for both data discovery and governance in modern ecosystems.

Practical Applications

• Use Case: Deploying Collate for discovery and governance across a data ecosystem. Pitfall: Allowing schema changes without observability, resulting in broken ML outputs.
• Use Case: Establishing a semantic metadata graph to unify entity definitions. Pitfall: Using inconsistent definitions for ‘customer’, which leads to inaccurate analytics.
• Use Case: Implementing AI observability to monitor data pipeline health. Pitfall: Weak metadata management preventing the discovery of root causes for model errors.

References:

• https://stackoverflow.blog/2026/04/28/your-llm-issues-are-really-data-issues/