Your LLM issues are really data issues
AI and LLMs face significant challenges when working with real-time, structured production data due to issues like schema changes, inconsistent data definitions, and poor governance. These data problems can disrupt both analytics and machine learning models, undermining AI reliability. Companies need robust metadata management and data observability practices to make their data AI-ready.
- ▪Schema changes and inconsistent definitions, such as differing interpretations of 'customer,' can break AI and analytics systems.
- ▪Weak data governance and lack of metadata management contribute to AI failures in production environments.
- ▪Collate, a semantic intelligence platform, uses a semantic metadata graph to improve data discovery, governance, and AI observability.
- ▪Real-time data processing systems like Apache Kafka and Apache Spark are critical for handling large-scale data in distributed environments.
- ▪The evolution from Hadoop to cloud-based data solutions has improved scalability but not solved underlying data quality issues.
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April 28, 2026Your LLM issues are really data issuesRyan welcomes Harsha Chintalapani, co-founder and CTO at Collate and co-creator of Open Metadata, to the show to discuss why AI and LLMs struggle with real-time, structured production data. They explore how schema changes, inconsistent definitions (like “customer”), and weak governance can break both your analytics and MLs, and what companies can do to get their data AI-ready, from metadata management to observability.Collate is a semantic intelligence platform built on a semantic metadata graph for discovery, governance, and AI observability across your data ecosystem.Connect with Harsha on LinkedIn.Congrats to user buttonsrtoys, who won a Famous Question badge for their question Possible to edit PDF without embedded font…
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