In this episode Denis invites Thomas to speak on the topic of industrial time series data quality. we delve into the challenges and importance of ensuring data reliability and observability in industrial settings. Thomas shares his extensive background in industrial time series data and his current work with Timeseer, a platform focused on data quality and observability. The conversation covers various aspects, including the differences between data reliability and observability, the challenges of moving data from the shop floor to the cloud, and the need for a proactive approach to data quality management. Finally, we discuss real-world examples and the technical and organizational components required to address data quality issues.
Notable Quotes
- 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.
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