Data good enough for operations is not necessarily analytics-ready.

Exactly one month ago I published the first episode of my Industrial Data Quality Podcast. In my opinion, a topic of critical importance that is all too often overlooked, especially with all the buzz around AI.

In the most recent episode, I had the pleasure of inviting guest speaker Thomas Dhollander, co-founder of Timeseer.AI. Together we explored critical challenges in industrial time series data reliability and observability.

Key takeaways:

• Data quality is not just a technical issue — it’s a people and process problem, deeply tied to governance and ownership.
• Data management at many companies is still reactive — fixing issues only after models break or KPIs look suspicious. When companies scale their data-driven operations, they need to turn to proactive data management to avoid ending up in firefighting mode.
• Data maturity varies by company and by industry — utilities and pharma often lead, some other industries may still view data as a byproduct.
• Data should be treated like a product — with quality checks, documentation, and accountability — especially as you scale analytics. This is also true for OT data.
• AI needs data quality — ML and AI depend on quality inputs and sensor drift or misconfigured tags can quietly corrupt your entire model output. Interestingly, AI is also a key enabler in scaling data quality.
• Moving data to the cloud introduces new risks — missing context, inconsistent pipelines, and ownership confusion.