From assumed data to trusted decisions.

On True North 🧭, I sat down with Raj Munusamy, Product Portfolio Manager for Data Platforms & Agentic AI at Saint-Gobain, a 350+ year-old industrial giant operating across 1,200+ manufacturing plants worldwide.

Raj is a rare bridge between worlds: automation & control, historians & OT systems, and modern cloud-scale data platforms and AI. In this session, we go deep into what really breaks when industrial data scales, and why data trust, not algorithms, is the limiting factor for AI in manufacturing.

We discuss Saint-Gobain’s Metriks manufacturing data platform, the reality of Bronze / Silver / Gold data layers, and the often-overlooked “historian gap” where data-quality context is lost as signals move from sensors to dashboards and models.

Timeseer.AI engagement 🤝
Timeseer.AI is deployed as the Trust Layer next to the historian, currently live across 70+ Saint-Gobain plants (and scaling) to continuously validate Bronze-layer time-series data, detect issues early, and restore confidence in the data that feeds dashboards and AI.

Key takeaways:
🧠 Data availability ≠ data trust
🧪 The Bronze layer is where trust is won or lost
📉 Missing, stale, drifting data silently kills dashboards and AI
🧩 Historians store data, not confidence
🚫 Auto-fixing data can hide root causes in continuous manufacturing

We also covered:
⚙️ The “historian gap” (Sensors → PLC → SCADA → Historian)
📊 Why teams still spend massive time validating data
🔍 Detect → Score → Resolve → Serve as a trust framework
🛠️ Why Saint-Gobain chose a trust layer instead of building ad-hoc fixes
📈 What it takes to scale data trust across plants, teams, and use cases

No hype. No theory.
Just what needs to be true for industrial analytics, AI, and autonomy to scale.

🎙️ True North Podcast