SIDE-BY-SIDE COMPARISON

CompareTrendMinervsSeeq Analytics

Review features, pricing signals, strengths, and trade-offs before choosing.

Generated from current catalog profiles Catalog profile signals Use-case comparison

Quick decision guide

Choose based on your workflow

TrendMiner may fit better if...

  • Pattern Search
  • Root Cause
  • Asset Dashboards

Seeq Analytics may fit better if...

  • Calculation Engine
  • Operational Dashboards
  • Engineer Collaboration

Overview

How each tool is described

TrendMiner

TrendMiner is a Time series search for process plants.

It helps industrial operations and analytics teams handle repeated work with a clearer process, so the tool is easier to judge even for non-technical buyers. Instead of looking only at feature lists, focus on whether it improves the everyday tasks your team already repeats: planning work, creating outputs, reviewing quality, and keeping information organized. For most teams, the right test is simple: try it with real workflows, real data, and real team expectations before treating the score as the final decision.

  • Best fit: Process engineers searching historian data daily.
  • Check first: connectors, data ownership, plant readiness, dashboards, and security requirements.

Bottom line: TrendMiner is most useful when its core strengths match the work your team repeats often.

View full TrendMiner profile

Seeq Analytics

Seeq Analytics is a Self service analytics for process data.

It helps industrial operations and analytics teams handle repeated work with a clearer process, so the tool is easier to judge even for non-technical buyers. Instead of looking only at feature lists, focus on whether it improves the everyday tasks your team already repeats: planning work, creating outputs, reviewing quality, and keeping information organized. For most teams, the right test is simple: try it with real workflows, real data, and real team expectations before treating the score as the final decision.

  • Best fit: Process engineers using historian data.
  • Check first: connectors, data ownership, plant readiness, dashboards, and security requirements.

Bottom line: Seeq Analytics is most useful when its core strengths match the work your team repeats often.

View full Seeq Analytics profile

Side-by-side

Key differences

Criteria
Industrial AI AnalyticsTrendMiner
Industrial AI AnalyticsSeeq Analytics
Best for
Industrial AI Analytics
Industrial AI Analytics
Score
8.5/10
8.7/10
Pricing
Contact sales
Contact sales
Category / audience
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
  • industrial analytics
  • process analytics
  • trendminer
+2 more
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
  • seeq
  • process analytics
  • time series analytics
+2 more

OVERLAP

Where TrendMiner and Seeq Analytics are similar

Both tools cover similar catalog signals. The deciding factor is usually workflow fit, implementation needs, and ecosystem fit.

1 capabilities1 workflows

Shared capabilities

Capability overlap

  • Historian ConnectorsReads PI Aspen and other historian sources

Shared workflows

Workflow overlap

  • Standardize process analytics across plant teamsUse same methods across plants and process units

Feature check

Side-by-side feature check

Feature
TrendMiner
Seeq Analytics
Pattern SearchFinds similar operating periods across history
-
Root CauseCompares signals during normal and abnormal operations
-
Asset DashboardsLive views of critical process asset behavior
-
Self ServiceBuilt for process engineers not data scientists
-
Process ContextAdds asset context to historian time series
-
Time Series SearchFinds patterns across historian data quickly
-
11 capabilities compared.10 differentiating rows are shown first.

Use cases

Who they're built for

TrendMiner

  • Search historian patterns quickly across yearsFind similar operating periods across long historian records
  • Investigate abnormal process behavior across plantsCompare normal and abnormal periods to find root causes
  • Build asset monitoring dashboards for engineersCreate live views of critical process equipment behavior
View full TrendMiner profile

Seeq Analytics

  • Investigate process plant events and anomaliesSearch historian data to understand specific abnormal events
  • Build asset performance dashboards for engineersCreate live views of critical process equipment behavior
  • Support reliability engineering analysis and decisionsHelp reliability teams analyze recurring failures and patterns
View full Seeq Analytics profile

The trade-offs

Pros & cons of each tool

Trade-offs

TrendMiner

Pros
  • Strong pattern search across long historian records
  • Built specifically for process engineers not data scientists
  • Strong root cause analysis on historian data
Cons
  • Best value for plants with rich historians
  • Adoption depends on engineer training and champions
  • Ownership has changed across recent years already
Trade-offs

Seeq Analytics

Pros
  • Powerful time series analytics for process engineers
  • Connects to many industrial historians and sources
  • Encourages collaboration across reliability and process teams
Cons
  • Needs strong historian data and engineering capacity
  • Less suited for plants without process data history
  • Pricing scales with users and connected sources

Final verdict

Best fit depends on your workflow

Catalog verdict · low confidence

Current catalog data shows meaningful overlap between TrendMiner and Seeq Analytics. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.

Shared catalog overlap

TrendMiner and Seeq Analytics share 2 catalog signals, so the decision should focus on fit rather than broad capability alone.

Differentiators available

TrendMiner has 4 visible decision signals and Seeq Analytics has 4.

Score signal

Seeq Analytics has the higher SoftFinders Score in the current catalog data.

TRY THEM YOURSELF

See which one fits your workflow

Both tools have their strengths, the best way to decide is to spend a few minutes inside each.