Most Helpful Review
Automated modelling, classification, or clustering are very useful. Customer support is hard to contact.
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Automated modelling, classification, or clustering are very useful.
A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly.
It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful.
It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler.
New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data.
We use analytics with the visual modeling capability to leverage productivity improvements.
It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it
The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that.
This open-source product can compete with category leaders in ELT software.
This solution is easy to use and especially good at data preparation and wrapping.
It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.
Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis.
Clear view of the data at every step of ETL process enables changing the flow as needed.
We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.
The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine.
Easy to connect with every database: We use queries from SQL, Redshift, Oracle.
Customer support is hard to contact.
It is not integrated with Qlik, Tableau, and Power BI.
Expensive to deploy solutions. You need to buy an extra deployment unit.
I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it.
The standard package (personal) is not supported for database connection.
Unstructured data is not appropriate for SPSS Modeler.
Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization.
I think mapping for geographic data would also be a really great thing to be able to use.
The ability to handle large amounts of data and performance in processing need to be improved.
It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end.
They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.
The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).
The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily.
The data visualization part is the area most in need of improvement.
The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best.
Data visualization needs improvement.
Pricing and Cost Advice
When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount.
It got us a good amount of money with quick and efficient modeling.
The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool.
It is a huge increase to time savings.
KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.
Answers from the Community
out of 17 in Data Mining
Average Words per Review
out of 17 in Data Mining
Average Words per Review
Compared 18% of the time.
Compared 15% of the time.
Compared 12% of the time.
Compared 42% of the time.
Compared 13% of the time.
Compared 7% of the time.
Also Known As
|SPSS Modeler||KNIME Analytics Platform|
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.
|KNIME is the leading open platform for data-driven innovation helping organizations to stay ahead of change. Use our open-source, enterprise-grade analytics platform to discover the potential hidden in your data, mine for fresh insights or predict new futures.|
Learn more about IBM SPSS Modeler
Learn more about KNIME
|Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey||Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG|
Financial Services Firm24%
Software R&D Company22%
Comms Service Provider13%
Financial Services Firm11%
Software R&D Company25%
Comms Service Provider17%
Financial Services Firm9%