Domino Enterprise MLOps Platform vs. Spotfire Data Science

If you have an analytics department, Data Science is perfect for making analyses quicker. Data Science works well for web querying, automating analyses, sharing advanced analyses with others, and performing lots of other advanced analytical processes. Data Science is not a good fit if the analytics you do is stuff that Excel can do. The software is powerful, with lots of features, and unless you actually plan on using those features, it's not worth paying for.

Incentivized

• It has a great user interface, easy to navigate and learn on the fly.
• There are lots of great options for data organization and analysis! Makes it a handy tool for presentations as well.
• A collaborative ability is highly valued for my company where we often work from home or on site. Being able to share the data with those in the office so multiple people can look at it is a great tool!

Incentivized

• Unfortunately, some functionality is hidden per upgrade to other versions. Feel data mining functionality would be useful, but not budget for software. At the current price point, would have expected more (such as Mathematica breadth of functionality for one price).
• It is light on optimization capability.
• Slow when considering very large datasets, performing things such as distribution identification

Incentivized

The company is hesitant to spend this much on software. They are primarily an engineering firm, and they don't understand the use of analytical software for environmental professionals.

Incentivized

I prefer Spotfire Data Science's approach. It is more natural and fits the way I think. I prefer to use Spotfire Data Science's VB for writing macros. It is real code, meaning that I do not need to trick the software to do what I need and there are no implied loops over solving simple problems. The graphs are publication quality and can be edited by hand or using a macro if I am building hundreds of them. Spotfire Data Science had a user-friendly approach to building lengthy data processing streams (in its workspaces). It is just so fast for analyzing a dataset that you have never seen before and efficient for ongoing work on the same data.

• Our company has had the program for less than 1 year. We don't expected a positive return this year. The goal is for Data Science to led to defined projects by the end of the end of the year and implementation in the following two. Overall, we are planning on 4 years to fully recoup the cost of the software and the cost of implementing identified projects.

Incentivized