TrendMiner review
Deep historian analysis with monitoring, anomaly detection, and machine-learning deployment.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
TrendMiner is an industrial analytics platform for operational specialists in process manufacturing and other industrial environments. It brings together time-series, event, asset, and contextual data from historians, IoT platforms, databases, and enterprise systems. Teams can explore trends, compare operating periods, create formulas and dashboards, monitor processes, investigate anomalies, collaborate on findings, and apply machine-learning models through MLHub and its notebook environment. Web, Linux, API, and self-hosted options support different operating models.
Its strongest fit is historian-heavy analysis. TrendMiner connects with OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian, and Wonderware InSQL, while also supporting PostgreSQL, Microsoft Azure, Microsoft Fabric, Microsoft ADX, and AWS. The published capabilities cover real-time stream processing, custom dashboards, pattern searches, correlations, fingerprints, calculated features, continuous monitoring, alerting, anomaly detection, predictive analytics, and model deployment. REST and connector APIs add room for custom integration, while contextualization helps relate process behavior to events and assets.
According to the vendor's pricing approach, buyers begin through contact with sales rather than selecting a published self-service tier. The platform supports SaaS, private-cloud, and on-premises deployments, so organizations should align the deployment model with their operating and integration requirements. TrendMiner is a strong choice for process manufacturers that need detailed historian exploration plus operational monitoring and machine-learning workflows. Teams seeking a broad, general-purpose analytics product outside industrial and process environments may find its focus less appropriate.
TrendMiner pros and cons
- Where it wins
- Deep time-series exploration with patterns, correlations, formulas, and fingerprints
- Broad historian, cloud, database, and enterprise integration options
- Covers monitoring, anomaly detection, predictive analytics, and model deployment
- Where it doesn't
- Pricing requires a sales conversation rather than self-service plan selection
- Its industrial process focus may not suit general-purpose analytics teams
- Deployment options can require decisions across SaaS, private cloud, and on-premises
TrendMiner fact sheet, pricing and score →
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