I would offer you my own take on the the matter. The three main points are
KNIME is a platform . Sounds simple but it means it will happily interact with all sorts of data sources and systems like Java, R, Python etc. as well as SQL-databases and Big Data
It is highly scalable: it can be used by beginners as well as experts and they all can interact with the business side (all the hippos coming together). It is low-code
it has a great community and support network. And the initial price tag is just great
@DataSapiens welcome to the KNIME forum.
It is difficult to state in a few lines what KNIME is and can do. I think you have mentioned some important functions that KNIME can provide. And there is more.
At a base it is a data analytics platform with a graphic workflow interface. Platform meaning that it is not restricted to a certain set of functions but will happily connect to other programs, data bases and languages/systems - namely R and Python.
For me it is mostly about the scalability and…
Then you could check out Reviews about KNIME and KNIME-Sever at Gartner:
https://www.gartner.com/reviews/market/multipersona-data-science-and-machine-learning-platforms/vendor/knime/product/knime-analytics-platform
https://www.gartner.com/reviews/market/multipersona-data-science-and-machine-learning-platforms/vendor/knime/product/knime-server
Then I like to reference these examples from leading German companies about their use of KNIME and the building of a data science community:
Expanding the Field: The Data Solutions Space for Self-Service Business Users (Continental)
Sparking Data Literacy with KNIME and Making Better Decisions (Continental)
Five Takeaways from the First KNIME Meetup@Siemens
Last Thursday, we at Data Visions organized together with Robotic Land our first Siemens-wide KNIME Meetup in Munich. Around 70 colleagues from various organizations within Siemens found their way to the Werk 1, the trendy hot spot for startups and...
Reading time: 5 min read
Driving a Citizen Data Scientist Approach (Siemens)
The demand for automating day-to-day procedures is growing daily. On top of that, billions of bytes of data, multiple data sources, and hours of manual work put into sorting it all out, make these procedures hugely complex and time consuming.
Manufacturers Bosch, ZF, and Fraunhofer Discuss Key Considerations for Building Data Science Teams
9 Likes
