Spotfire: Robust, Shareable Data Visualization
March 08, 2019
Spotfire: Robust, Shareable Data Visualization
Score 8 out of 10
Vetted Review
Verified User
Software Version
TIBCO Cloud Spotfire Enterprise
Overall Satisfaction with Spotfire
My experience with Spotfire occurred within an engineering context. Spotfire was used across the entire Medtronic Tempe campus. Spotfire is instrumental in rapidly sharing and analyzing data in a centralized, portable way. Its robust visualization tools allow engineers to rapidly identify trends which indicate either problems to be addressed or improvements which should continue. The automated nature of Spotfire allows analyses to be run on new data sets without any additional hassle.
- Data visualization: Spotfire quickly reveals trends through its robust visualization tools. Many different graphical representations of the data are immediately available and can be used effectively with either default settings or can be tweaked to the specific needs of an audience.
- Extensibility: Spotfire allows for the implementation of just about any data analysis you can think if. If the analysis that you want to run is not already built in, Spotfire includes the option to write R or IronPython code to extend its capabilities.
- Shareability: When you develop a tool or visualization in Spotfire, you can publish it in such a manner that your audience only needs a link to view it: they don't even need to have Spotfire installed on their own machine. Web publishing maintains the interactivity of your visualizations and allows users to download data sets for their own use.
- Feature parity between web publishing and the Spotfire client: some features of Spotfire that work flawlessly in the Spotfire client break when published to the web. In particular, custom JavaScript inserted into the analysis tends not to render well on the web even if it renders perfectly in the client.
- Lack of thorough documentation: particularly when comparing Spotfire documentation to the documentation for Python, JMP, or Matlab, Spotfire's documentation is lacking. In particular, I remember finding documentation on custom SQL queries being very sparse. Most often I would base my Spotfire solutions on examples found on forums rather than on official Spotfire advice.
- The execution time of custom R data functions: Spotfire uses its own implementation of the R runtime (called TERR, Spotfire Enterprise Runtime for R) which I noticed to be significantly slower than the open-source runtime for certain tasks.
- Error reporting: error reporting by both TERR and IronPython is very opaque and makes debugging very difficult.
- Long-term solutions: the data analysis tool that I built using Spotfire about a year ago is still in use for similar data sets at my company.
I am not aware of integrations with any other Spotfire products being used at my company.
JMP is much better suited for deep dives into one specific data set. Its ability to drill down outpaces Spotfire. JMP is also easier to learn and has more and clearer documentation than Spotfire. However, I selected Spotfire because Spotfire has much superior visualization tools, much easier sharing capabilities, and allows one to build much more intuitive user interfaces.
Spotfire Feature Ratings
Spotfire Training
- In-Person Training
- No Training
For the most part, I leveraged existing expertise within the company to successfully take advantage of all of Spotfire's features and abilities. Thus, the training provided to my company reached me in an indirect manner, but based on the wealth of knowledge held by some in the company I can only conclude that Spotfire's training programs are effective.