TrendMiner
Predictive analytics software
Time series intelligence software
Manufacturing intelligence software
IoT analytics platforms
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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- Manufacturing
- Energy and utilities
- Agriculture, fishing, and forestry
What is TrendMiner
TrendMiner is an industrial analytics application used to search, visualize, and analyze time-series data from historians and operational technology (OT) systems. It is commonly used by process engineers, reliability teams, and operations analysts to investigate events, monitor asset and process behavior, and support troubleshooting and continuous improvement. The product emphasizes self-service time-series exploration, contextualization of signals, and sharing of analyses for operational decision-making in manufacturing environments.
Purpose-built for time-series OT
TrendMiner is designed around high-frequency sensor and historian data rather than primarily around business reporting tables. It supports workflows such as event investigation, pattern search, and comparing operating periods that are common in industrial operations. This focus can reduce the need to adapt general BI tooling for OT-specific analysis.
Self-service analysis for engineers
The product targets day-to-day users in plants who need to explore signals without building complex data models. It provides interactive visual analysis and repeatable investigations that can be shared with other stakeholders. This can shorten the cycle from anomaly detection to root-cause hypothesis compared with analyst-led reporting pipelines.
Operational context and collaboration
TrendMiner supports adding context to time-series analysis (for example, identifying relevant time windows, conditions, and operational states) and reusing those definitions across analyses. It also supports sharing results so teams can align on what happened and when. This helps standardize investigations across shifts and sites.
Less suited for BI reporting
TrendMiner is not primarily a general-purpose BI platform for enterprise dashboards, semantic models, and broad business data blending. Organizations often still need separate tools for financial, sales, or cross-domain analytics and governed reporting. This can increase the number of platforms in the analytics stack.
Integration depends on data sources
Value depends on reliable connectivity to historians, OT data infrastructure, and consistent tag/asset naming practices. If data is fragmented across sites or lacks standard context, onboarding and scaling can require additional data engineering and governance work. Some advanced use cases may require complementary integration or data platform components.
Advanced ML may require add-ons
While the product supports analytical workflows on time-series data, organizations seeking extensive automated model management, large-scale feature engineering, or enterprise MLOps may need additional tooling. Teams with strong data science requirements may find limitations compared with platforms centered on cloud data warehousing and ML pipelines. This can affect fit for predictive programs that extend beyond plant-level analytics.
Seller details
Software AG
Darmstadt, Germany
1969
Private
https://www.softwareag.com/
https://x.com/SoftwareAG
https://www.linkedin.com/company/softwareag/
