Industrial AI Podcast Time Series Data Quality
May 21, 2025
Join Thomas Dhollander, Co-founder and CPO of TimeSeer.AI, as he shares insights from his engineering and applied machine learning background. He discusses the critical role of trusted IIoT data for proactive operations and why data quality often outstrips model complexity. Thomas highlights common pitfalls like sensor failures and metadata issues that disrupt analytics. He also explains how TimeSeer empowers data stewards, enabling effective data management and responsive workflows across organizations. AI-driven solutions and low-code validation processes are game-changers for operational integrity.
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Segment Responsibilities For Data Work
- Triage data problems by user role: manual validation, digitalization teams, and operations each need different tools.
- Thomas Dhollander advises mapping capabilities to these three buckets to prioritize automation and stewardship.
A Complete Pipeline Beats Point Solutions
- Effective IIoT data platforms combine connectivity, a time-series asset model, scalable pipelines, detection algorithms, and cleaning steps.
- Thomas Dhollander says TimeSeer maps data/metadata across diverse stores and runs incremental checks at scale.
Prioritize By Scoring And Summarizing Incidents
- Summarize and score issues into actionable incidents so users avoid alarm floods.
- Thomas Dhollander recommends using AI for detection and language models for summarization to guide stewardship efforts.