Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.
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JMP Pro
Score 8.3 out of 10
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JMP Pro offers all the capabilities of JMP, plus advanced features for more sophisticated analysis including predictive modeling and cross-validation techniques.
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
JMP Pro is perfectly suited for statistical analysis but users should have some statistical knowledge before using it since there may be some terms/functions in the software that are not widely used in other fields. No prior coding experience is needed to use JMP Pro. However, most people doing data processing would prefer to code their analysis.
I think the most awesome thing about JMP Pro is the Profiler component. The Profiler component makes sharing analyses interactive on the web using interactive HTML5. The Profiler makes showing the impact of interaction effects so easy. It's the best statistical software feature I think I've ever come across.
JMP Pro does dashboards well. It is relatively straight-forward to create four pane dashboards that automatically update. I think it's nice.
I think JMP Pro graphics are a massive improvement when compared to the graphics produced by its parent company - SAS. JMP Pro's features are vast, and easy to use and understand.
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
The intuitive interface JMP Pro offers is more interactive than R studio, but since coding is not required in JMP Pro, programming analysis in R studio is more convenient in some aspects. JMP Pro is able to quickly analyze multiple variables and produce reasonable outputs and models, which is valuable for users doing data analysis.
The overall ROI for JMP Pro was positive because of the Profiler option. This makes communicating the results of regressions as easy and complex as needed. It's such an awesome feature, I wonder why it wasn't offered before.
If JMP Pro didn't have the Profiler feature, I think I would still recommend using JMP Pro, but the recommendation is not a slam dunk. The software has its own programming language, which is not well-known and requires effort to debug.
JMP Pro's dashboards are well-built, and provide a positive ROI from the perspective of creating real-time, interactive reporting. It's very nice for this feature.
