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Advanced Analytics in TIBCO Spotfire®

Last updated:
6:46pm May 18, 2022

Not already using Spotfire? 


Advanced Analytics is a term often used for Predictive and Prescriptive Analytics.  Spotfire has built-in capabilities for statistical analysis and modeling available directly from the user interface.  These include calculated columns, expressions in visualizations, visualization features such as boxplot comparison circles and line chart forecasting and Tools menu data relationships, clustering and modeling.  These built-in capabilities can be quickly extended to incorporate custom calculations of any complexity using TIBCO® Enterprise Runtime for R and other advanced analytics engines.  

Watch a short demo of Spotfire advanced analytics:

Out-of-the-Box Advanced Analytics 

You can create predictive models and apply advanced techniques from the Spotfire user interface.

  • Calculations: Aggregations on Visualizations, Expressions - multiple Quick Reference topics
  • Statistical Features: Clustering, Box plots & comparison circles, Relationships between categorical variables (Chi-square) - multiple Quick Reference topics
  • Data Relationships Tool - The Data Relationships tool is used for investigating the relationships between many column pairs. The Linear regression and the Spearman R options allow you to compare numerical columns, the Anova option will help you determine how well category columns categorize values in (numerical) value columns, the Kruskal-Wallis option is used to compare sortable columns to categorical columns, and the Chi-square option helps you to compare categorical columns.
  • Predictive Analytics:  Regression Models, Holt-Winters Forecast - multiple Quick Reference topics
  • Clustering: Data clustering is the process of grouping things together based on similarities between the things in the group. See how Spotfire makes it easy to perform clustering

Data Functions

You can use R models and run them within Spotfire using the in-built TIBCO® Enterprise Runtime for R  engine, as well as leveraging advanced analytics from Python, Statistica, SAS, MATLAB, KNIME, S+, Spark, H2O, MapReduce,  Fuzzy Logix and databases.

Other Resources


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