Most Helpful Review
Use IBM SPSS Statistics? Share your opinion.
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.
The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful.
In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions.
The most valuable feature is the user interface because you don't need to write code.
It has the ability to easily change any variable in our research.
They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do.
The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS.
You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use.
The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important.
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.
The solution needs more planning tools and capabilities.
Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them.
This solution is not suitable for use with Big Data.
The design of the experience can be improved.
The product should provide more ways to import data and export results that are user-friendly for high-level executives.
One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated.
Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance.
The statistics should be more self-explanatory with detailed automated reports.
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.
The price of this solution is a little bit high, which was a problem for my company.
We think that IBM SPSS is expensive for this function.
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.
Compared 45% of the time.
Compared 27% of the time.
Compared 13% of the time.
Compared 11% of the time.
Also Known As
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.
|Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.|
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Software R&D Company4%
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K 12 Educational Company Or School18%
Comms Service Provider12%
Financial Services Firm8%