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Optimize your business through AI & Analytics


The Insights feature of the platform enables deeper knowledge of your data, so you can optimize your processes



The insights features can generally be organized into 3 main elements:

Visualize - data from your assets in time series explorer or dashboards. Bring your own visualization tools.

Analyze - data based on advanced analytics tools from Azure Data Explorer including ML features, or use your preferred analysis software

Improve - your process by training ML models and deploy them at the edge


Take a further look at the insights features below




Visualize your data through time series database or use your own visualization tools:

Time series
Visualize time series both historically and in real-time directly from your assets.

Build your own dashboards using dozens of graphical objects for easy overview and analysis.

Customer applications
Use virtually any visualization tool and integrate into existing business intelligence solutions. Visualize your data using tools like Power BI, Grafana, Tableau, Excel and many more.


Analyze huge amount of data through advanced built-in analysis tools based on Azure Data Explorer or use your own toolkit.

Query your data using Kusto (KQL) with Azure Data Explorer for deeper insights, comparison and analysis.

Azure Data Explorer uses KQL that has anomaly detection and forecasting functions to check for anomalous behavior.

3rd party tools
Store historical information in a data lake and get access to the information from 3rd party tools including analytic platforms such as Databricks, Dataiku, Jupyter Notebooks, Apache Spark, Power Apps and many more.


Enable closed loop analytics by using edge modules to do process optimization, re-train and control the process:

Process optimization
Improve your process optimization efforts by detailed insights through data.

Train model
Optimize your ML algorithms by automated re-training and deployment and integrate your MLOps process with Tricloud Nexus fast and easy.

Make your own optimization algorithms based on data and rules of thumb, and deploy on edge for guidance or direct control of the process.