Compare Darwin vs. TIBCO Spotfire Data Science

Darwin is ranked 8th in Data Science Platforms with 8 reviews while TIBCO Spotfire Data Science is ranked 17th in Data Science Platforms with 2 reviews. Darwin is rated 8.4, while TIBCO Spotfire Data Science is rated 7.6. The top reviewer of Darwin writes "Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows". On the other hand, the top reviewer of TIBCO Spotfire Data Science writes "A straightforward initial setup and good reporting but needs better documentation". Darwin is most compared with RapidMiner, whereas TIBCO Spotfire Data Science is most compared with TIBCO Statistica, Dataiku Data Science Studio and Amazon SageMaker. See our Darwin vs. TIBCO Spotfire Data Science report.
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Most Helpful Review
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Find out what your peers are saying about Darwin vs. TIBCO Spotfire Data Science and other solutions. Updated: January 2020.
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
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.

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We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost.The idea that you don't have to generate reports each day but they are sent automatically is great.

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Cons
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.

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In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues.Additional templates would help to get things moving more quickly in terms of getting the reports out.

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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.

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Ranking
8th
Views
257
Comparisons
34
Reviews
7
Average Words per Review
1,581
Avg. Rating
8.4
17th
Views
527
Comparisons
398
Reviews
2
Average Words per Review
838
Avg. Rating
7.5
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Also Known As
Alpine Data Chorus
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SparkCognition
TIBCO
Overview

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.

TIBCO Spotfire Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.

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Find out what your peers are saying about Darwin vs. TIBCO Spotfire Data Science and other solutions. Updated: January 2020.
397,983 professionals have used our research since 2012.
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