Most Helpful Review
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate.
In terms of streamlining a lot of the low-level data science work, it does a few things there.
I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable.
The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science.
Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision.
I find it quite simple to use. Once you are trained on the model, you can use it anyway you want.
The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.
The thing that I find most valuable is the ability to clean the data.
This solution is easy to use and especially good at data preparation and wrapping.
The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes.
This open-source product can compete with category leaders in ELT software.
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.
An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data.
The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition.
There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets.
There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.
The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin.
The analyze function takes a lot of time.
Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model.
Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin.
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.
I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script.
The ability to handle large amounts of data and performance in processing need to be improved.
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.
Pricing and Cost Advice
I believe our cost is $1,000 per month.
The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well.
In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos.
As far as I understand, my company is not paying anything to use the product.
KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website.
KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.
out of 33 in Data Science Platforms
Average Words per Review
out of 33 in Data Science Platforms
Average Words per Review
Compared 55% of the time.
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Compared 13% of the time.
Compared 7% of the time.
Also Known As
|KNIME Analytics Platform|
SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.
|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.|
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|Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG|
Comms Service Provider64%
Financial Services Firm10%
Software R&D Company4%
Comms Service Provider29%
Software R&D Company25%
Comms Service Provider14%
Financial Services Firm8%
K 12 Educational Company Or School6%