Most Helpful Review
Automated modelling, classification, or clustering are very useful. Customer support is hard to contact.
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Automated modelling, classification, or clustering are very useful.
A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly.
It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful.
It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler.
New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data.
We use analytics with the visual modeling capability to leverage productivity improvements.
It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it
The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that.
The most valuable feature is the ability to handle large data sets.
Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features.
It has facilitated timely analysis results with quality work and meaningful output.
I use it to replicate our entire financial system to verify/duplicate calculations.
I use SAS daily to analyze data, produce reports, and other outputs.
SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems.
It is able to connect to all major platforms, and all the smaller platforms that I have come across.
It has also been around for an extremely long time, has a strong history, and good market penetration.
Customer support is hard to contact.
It is not integrated with Qlik, Tableau, and Power BI.
Expensive to deploy solutions. You need to buy an extra deployment unit.
I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it.
The standard package (personal) is not supported for database connection.
Unstructured data is not appropriate for SPSS Modeler.
Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization.
I think mapping for geographic data would also be a really great thing to be able to use.
This solution should be made more user-friendly.
Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots.
Support at universities used to be limited, but I hear this is changing.
The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled.
I would like to see their interface to R added to either Base SAS or SAS Analytics.
Pricing and Cost Advice
When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount.
It got us a good amount of money with quick and efficient modeling.
The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool.
It is a huge increase to time savings.
The cost for SAS Business Intelligence can prove to be a little prohibitive.
Setup costs were quite reasonable.
Prices were comparable with alternative solutions.
Licensing was rather straightforward.
It is relatively expensive. It is not an easy software to afford.
out of 16 in Data Mining
Average Words per Review
out of 16 in Data Mining
Average Words per Review
Compared 18% of the time.
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Also Known As
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.
|SAS was founded in 1976 and actually began as a project at North Carolina State University to analyze agriculture research. It has since become a global company that is recognized for its innovation in data analytics and business intelligence. SAS is redefining what's possible with data analytics through greater efficiency, strong information value chains, effective collaboration tools, and state-of-the-art visualization software. SAS Analytics is designed for use in a variety of industries including government, manufacturing, higher education, defense & security, banking, automotive, communications, and much more. SAS Analytics is a business intelligence (BI) solution that has the ability to reveal patterns and anomalies in data, identify relationships and different variables, and predict future outcomes. Users of SAS Analytics will benefit from making more sound, better informed business decisions based on company data and market trends. Data mining, data visualization, text analytics, forecasting, statistical analysis, and more are all available through SAS Analytics. Staples, which boasts $27 billion in sales across the globe, has a business philosophy that prioritizes customer loyalty and satisfaction. In order to better engage their customers, Staples utilizes SAS Analytics to plan finely tuned marketing campaigns. Through forecasting and advanced analytics, Staples has been able to rely on fewer contractors, and cut their marketing budget, while improving their customer retention rate.|
Learn more about IBM SPSS Modeler
Learn more about SAS Analytics
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Financial Services Firm25%
Software R&D Company23%
Financial Services Firm13%
Comms Service Provider9%
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