IBM SPSS Modeler vs. SAP Predictive Analytics

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM SPSS Modeler
Score 7.8 out of 10
N/A
IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
$499
per month
SAP Predictive Analytics
Score 7.0 out of 10
N/A
SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.N/A
Pricing
IBM SPSS ModelerSAP Predictive Analytics
Editions & Modules
IBM SPSS Modeler Personal
4,670
per year
IBM SPSS Modeler Professional
7,000
per year
IBM SPSS Modeler Premium
11,600
per year
IBM SPSS Modeler Gold
contact IBM
per year
No answers on this topic
Offerings
Pricing Offerings
IBM SPSS ModelerSAP Predictive Analytics
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeOptionalNo setup fee
Additional DetailsIBM SPSS Modeler Personal enables users to design and build predictive models right from the desktop. IBM SPSS Modeler Professional extends SPSS Modeler Personal with enterprise-scale in-database mining, SQL pushback, collaboration and deployment, champion/challenger, A/B testing, and more. IBM SPSS Modeler Premium extends SPSS Modeler Professional by including unstructured data analysis with integrated, natural language text and entity and social network analytics. IBM SPSS Modeler Gold extends SPSS Modeler Premium with the ability to build and deploy predictive models directly into the business process to aid in decision making. This is achieved with Decision Management which combines predictive analytics with rules, scoring, and optimization to deliver recommended actions at the point of impact.
More Pricing Information
Community Pulse
IBM SPSS ModelerSAP Predictive Analytics
Considered Both Products
IBM SPSS Modeler

No answer on this topic

SAP Predictive Analytics
Chose SAP Predictive Analytics
Each of them has a special use and we used SAP Predictive Analytics here because, in addition to the appropriate speed, it also had convenient and appropriate compatibility with other SAP products, which makes the work easier for our team.
Top Pros
Top Cons
Best Alternatives
IBM SPSS ModelerSAP Predictive Analytics
Small Businesses
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.2 out of 10
Alteryx
Alteryx
Score 9.0 out of 10
Enterprises
Posit
Posit
Score 9.1 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM SPSS ModelerSAP Predictive Analytics
Likelihood to Recommend
10.0
(6 ratings)
9.0
(3 ratings)
Usability
-
(0 ratings)
6.0
(1 ratings)
Support Rating
10.0
(1 ratings)
7.0
(1 ratings)
User Testimonials
IBM SPSS ModelerSAP Predictive Analytics
Likelihood to Recommend
IBM
Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.
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SAP
It's a great tool to merge actual data analysis (which Lumira doesn't do that well) with visualization (which Lumira does well) - so it can be seen as Lumira for data analysts. However, a lot of the 'predictive' side is hidden/black box which can be frustrating for those analysts, so you could argue it is too complex for casual users, but too 'black box' for analysts.
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Pros
IBM
  • Combine text and data
  • Provide facilities for all phases of the data mining process.
  • Use a node and stream paradigm to easily and quickly create models.
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SAP
  • It doesn't require you to have a Ph.D. to build models!
  • You can use it to address a very large and wide dataset without worrying about sampling.
  • Automation is in the product DNA. You can prepare your data, ingest it into the "Kernel", then get insights about what was found, decide to publish it and schedule scoring tasks or model refresh in the same product.
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Cons
IBM
  • Has very old style graphs, with lots of limitations.
  • Some advanced statistical functions cannot be done through the menu.
  • The data connectivity is not that extensive.
  • It's an expensive tool.
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SAP
  • Working with this software is very simple and enjoyable for me as [an] IT consultant and expert, but it is a bit complicated for novice users.
  • Some big data takes more time‌to load, which I think could be faster
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Usability
IBM
No answers on this topic
SAP
the UI is a bit dated and available as a desktop tool mostly.
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Support Rating
IBM
The online support board is helpful and the free add ons are incredibly appreciated.
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SAP
The documentation provides an explanation about what features are available but not necessarily what's happening behind the scenes. On the other side, the "community" has grown since the acquisition and most questions are properly addressed by SAP folks. Since the "product maintenance" mode announcement was made, there wasn't much new content published except on the Smart Predict side (which is built by the SAP Predictive Analytics team)
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Alternatives Considered
IBM
When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.
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SAP
We have typically used Spotfire for data analysis but decided to move to SAP Business Objects due to its innate connection with SAP. I found Lumira to be good for visualizations but it is not meant for data analysis. Therefore, we have introduced Predictive Analytics to see if it can fill that gap. So far, it's been far less intuitive than Spotfire to get started, and as far as I am aware so far, it does not bring many additional capabilities. I do, however, like that it utilizes the Lumira look/feel and integrates very well.
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Return on Investment
IBM
  • Positive - Ease of decision making and reduction in product life cycle time.
  • Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
  • Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.
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SAP
  • Proper forecasting increases our credibility with partners and customers
  • Forecasting determines the amount of investment in each sector and reduces the cost of additional costs
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ScreenShots

IBM SPSS Modeler Screenshots

Screenshot of Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.Screenshot of Explore geographic data, such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.Screenshot of Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails and social media comments.Screenshot of Use R, Python, Spark, Hadoop and other open source technologies to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you keep control.