where is knime machine learning's success story

Hello everyone

We are making a proposal to pharmaceutical company for a new data analysis project.
We prepare to use knime machine learning to finish the proposal.
Would you please tell me where is knime machine learning’s success story, or anything help to write the proposal document.

Thanks in advance


Hi @laughsmile -

Maybe our innovation notes would be useful for you? These are use cases which describe how KNIME has been deployed across several different technical areas. They are 1-2 page PDFs and come with a supporting workflow and dataset. You can find them on the KNIME home page - just scroll down a bit - or you find links to them here.

Maybe in your case the Disease Tagging example would be a good place to start?

If you were looking for other material entirely, let us know.


Hello @ScottF

Thank you very much for your help.

In these document , there are lot of qualitative information, it will be appreciated if you could tell me about whether there is some quantitative information.
For example:
customer A : cuts troubleshooting time from 2 days to 1 hour
customer B : the number of breakdown reduce by half.
customer C: find 3 factors affecting quality from 150 production factors

Thanks in advance.


It can be a little hard to find specifics like this, since businesses are often keen on confidentiality when it comes to results. Still, here are a few more resources that may be helpful for your proposal:

Selected company use cases as presented at KNIME Summit

Recent videos about how our partners are using KNIME


Hello @ScottF

That is what I want.
Thank you again.


1 Like

@laughsmile: I would like to add two more aspects. Besides specific business cases and success stories there are two aspects of KNIME that seem especially relevant to me.

One is what I would call the ‘Citizen Data Scientist’ approach. Spreading KNIME within a company and see how it grows in various departments driven by a vibrant community (and maybe a core expert group). These tow examples might illustrate what I mean by that.

Five Takeaways from the First KNIME Meetup@Siemens

Empower Your Own Experts! Continental Wins the Digital Leader Award

The second aspect is the scalability. You could use KNIME on your laptop but you also could use it to connect to Big Data clusters and handle the analytic there. This example that runs on a local machine does also run on a large Cloudera cluster just by switching out the connectors.

Bring the latest H2O.ai models to an enterprise big data cluster (speaking of the latest machine learning …)

I recently used KNIME to teach people the usage of Hive (and partitions) on Big Data systems. It is all there in a KNIME workflow but could just like that also work on Big Data with the same nodes.

And then a further note. KNIME has an especially strong community in the Chem- and BioInformatics sector (which I know almost nothing of). That might also be relevant for your client:

I am always impressed by the workflow landscapes of @jcmozzic (which underlying science I do not understand :slight_smile: )


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