KNIME Analytics Platform vs. TIBCO Data Science (including Team Studio and Statistica)

[KNIME Analytics] is greatly suited for repetitive tasks one has to perform in excel as it automates these mundane tasks. [KNIME Analytics] is also well suited for creating a seamless connection with other BI tools to enable hands-free file sharing. [KNIME Analytics] has improvements to make on the overall User interface, its data visualization package and advanced level of AI-related tasks such as text mining,

Incentivized

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

• Easy to use without much knowledge of coding.
• Connection to other languages such as JS, R, Python, etc.
• Workflow is displayed as connected nodes which makes it easy to troubleshoot and visualize.
• Open-source.
• Have a decent size community that supports Q&A.

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

• It does not have proper visualization.
• Some other BI tools (QlikView) have much easier functions for data interaction.
• Some other BI tools (Tableau) can be set up much faster.
• It is not an easy tool to use for non-tech savvy staff.

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

I am happy with the product. It provides the required functionality.

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

It performs all the required functions.

Incentivized

Since it is relatively new, there has not developed a vast previously asked/frequently asked questions library that comes up when you google an issue you come across with. This will happen only in time, and as the community grows. Because of the same reason, the community is not big. Consequently, it is possible not to receive good, fast responses to asked questions in community hubs and forums.

Incentivized

• Alteryx : allows for generally "data" knowledgeable workers to easily implement and develop a data model in an automated fashion. The collaboration tools built in also make is easy for members to share work, best practices, and custom modules
• Alteryx is not cloud based solution where as KNIME Analytics has that
• Alteryx was selected due to better user interface and better in data analytics

Incentivized

I prefer Statistica's approach. It is more natural and fits the way I think. I prefer to use Statistica'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. Statistica 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

• It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
• Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.

Incentivized

• 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