IBM SPSS Modeler vs. SAP Predictive Analytics

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM SPSS Modeler
Score 7.1 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.
$4,670
per year
SAP Predictive Analytics
Score 9.0 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
NoNo
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
Chose IBM SPSS Modeler
Python requires knowledge of programming, higher learning curve vs IBM SPSS Modeler
Chose IBM SPSS Modeler
IBM SPSS Modeler is considerably easier to use. It allows for very rapid development and the ability to get to a goal quickly. There is no need to learn a new programming language so the analyst has the ability to focus on the problem rather than the pedantics of managing …
Chose IBM SPSS Modeler
We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and …
Chose IBM SPSS Modeler
SPSS has a great set of analytical models, but SPSS is especially strong (compared to other tools) in complex statistical modeling and predictive analytics/statistics. However, the data connectivity features of SPSS are not the best, as the data sources SPSS can work with are …
Chose IBM SPSS Modeler
The field of data analytics has important value for each organization. IBM SPSS Modeler is one of the leaders in this highly competitive vertical. IBM SPSS is very intuitive compared with others, and has reduced the complexity. This software has various good functionalities …
Chose IBM SPSS Modeler
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.
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.
Chose SAP Predictive Analytics
(Couldn't pick R from the list nor Python packages)

Actually, I don't see SAP Predictive Analytics stacking up against other tools, but rather complementing them. On one side why would we use something "more complex" to solve a "business as usual" problem, when you can use tools …
Chose SAP Predictive Analytics
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 …
Features
IBM SPSS ModelerSAP Predictive Analytics
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM SPSS Modeler
7.0
Ratings
18% below category average
SAP Predictive Analytics
-
Ratings
Connect to Multiple Data Sources7.00 Ratings00 Ratings
Extend Existing Data Sources7.00 Ratings00 Ratings
Best Alternatives
IBM SPSS ModelerSAP Predictive Analytics
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
IBM SPSS Statistics
IBM SPSS Statistics
Score 7.8 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM SPSS ModelerSAP Predictive Analytics
Likelihood to Recommend
7.0
(0 ratings)
9.0
(0 ratings)
Usability
8.0
(0 ratings)
6.0
(0 ratings)
Support Rating
10.0
(0 ratings)
7.0
(0 ratings)
User Testimonials
IBM SPSS ModelerSAP Predictive Analytics
Likelihood to Recommend
Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.
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When you don't have much data science skills or when you need to build something quick, you can get started really fast with SAP Predictive Analytics. There are scenarios, like the ones covered by a neural network, where SAP Predictive Analytics is not well suited, but the big question is usually: "do you really need a neural network to address this use case?"
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Pros
  • A very nice and easy to use interface.
  • A great variety of analytics, from statistical calculation to data validation and predictive statistics.
  • Has a steep learning curve.
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  • Ability to use built-in algorithms or expand using R. This means that (with training) casual users can take advantage but also data analysts can do their thing!
  • Integration and consistency with Lumira. Even Lumira on its own has a quick 'predict' functionality (although limited/black box)
  • Ability to do the analysis and then present visually using the Lumira visualization capabilities
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Cons
  • Some Analyses aren't there out of the box but can be added through open languages like R and Python.
  • Graphs could be better.
  • Unable to read data stored in OLAP databases
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  • The "User Experience" is sometimes lacking some clear basic things. Maybe a migration to a cloud-based environment will help bridge that gap.
  • API is probably the next item on my list. The existing one is not easy to access or use which limits the integration capabilities.
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Usability
The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources
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the UI is a bit dated and available as a desktop tool mostly.
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Support Rating
The online support board is helpful and the free add ons are incredibly appreciated.
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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
We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.
Read full review
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
  • I am able to study and work from home sustainably
  • I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
  • Market research for my business
  • Help other small businesses to create viable and high quality products and services
  • Contribute to research projects: ethical, high quality data analyses and interpretation
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  • 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.