Spotfire Data Science is a tool that finally feels like it was written by a professional analyst
January 12, 2020

Spotfire Data Science is a tool that finally feels like it was written by a professional analyst

Thomas Young | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Spotfire Data Science

Spotfire Data Science is used by our organization to build advanced analytical forecasting and other predictive models and for simple to understand visualizations. The software is used by the professional analysts exclusively, no other group of professionals employs the software. Spotfire Data Science does data science in an easy-to-use, visually appealing style.

Pros

  • Spotfire Data Science does the point-and-click setup of data science better than anyone.
  • Spotfire Data Science does a good job at making suggestions on modeling framework.

Cons

  • Spotfire Data Science is for the advanced user. A beginning analyst may find the capabilities of the software overwhelming.
  • Spotfire Data Science's default analytics models are not always the best.
  • It saves time on many advanced analyses.
  • Improved time-series forecasting.
I think Spotfire Data Science does two things better than these other software tools. First, Spotfire Data Science is perfect for exploratory analyses. It's suggested models are great, and the visualizations provide a nice look at the data before any modeling is done. Second, Spotfire Data Science works quite well with other software tools. The software seems like it is built for the advanced modeler.
Spotfire Data Science is world-class at building exploratory analyses and for creative analytical tasks. The software does these two things quite well. On the other hand, Spotfire Data Science is not well suited for creating real-time advanced analytical models that are used in a production environment. Other open-source models work better for such productions.

Spotfire Feature Ratings

9.8
Connect to Multiple Data Sources
10
Extend Existing Data Sources
10
Automatic Data Format Detection
10
MDM Integration
9
9.5
Visualization
9
Interactive Data Analysis
10
8.5
Interactive Data Cleaning and Enrichment
8
Data Transformations
8
Data Encryption
9
Built-in Processors
9
9.3
Multiple Model Development Languages and Tools
9
Automated Machine Learning
9
Single platform for multiple model development
10
Self-Service Model Delivery
9
9.0
Flexible Model Publishing Options
9
Security, Governance, and Cost Controls
9

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