IBM Machine Learning for z/OS vs. IBM SPSS Modeler

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM Machine Learning for z/OS
Score 9.9 out of 10
N/A
IBM Machine Learning for z/OS® brings AI to transactional applications on IBM zSystems. It can embed machine learning and deep learning models to deliver real-time insight, or inference every transaction with minimal impact to operational SLAs.N/A
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
Pricing
IBM Machine Learning for z/OSIBM SPSS Modeler
Editions & Modules
No answers on this topic
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
Offerings
Pricing Offerings
IBM Machine Learning for z/OSIBM SPSS Modeler
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
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 Machine Learning for z/OSIBM SPSS Modeler
Considered Both Products
IBM Machine Learning for z/OS
Chose IBM Machine Learning for z/OS
We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core …
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.
Features
IBM Machine Learning for z/OSIBM SPSS Modeler
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM Machine Learning for z/OS
-
Ratings
IBM SPSS Modeler
7.0
Ratings
18% below category average
Connect to Multiple Data Sources00 Ratings7.00 Ratings
Extend Existing Data Sources00 Ratings7.00 Ratings
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IBM Machine Learning for z/OSIBM SPSS Modeler
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User Ratings
IBM Machine Learning for z/OSIBM SPSS Modeler
Likelihood to Recommend
10.0
(0 ratings)
7.0
(0 ratings)
Usability
-
(0 ratings)
8.0
(0 ratings)
Support Rating
4.0
(0 ratings)
10.0
(0 ratings)
User Testimonials
IBM Machine Learning for z/OSIBM SPSS Modeler
Likelihood to Recommend
IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.
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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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Pros
  • Good machine learning tool
  • Easy integration
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  • 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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Cons
  • Proper usage of REST API documentation is missing.
  • Not localization friendly, cannot support regional or local language documents.
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  • 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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Usability
No answers on this topic
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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Support Rating
IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.
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The online support board is helpful and the free add ons are incredibly appreciated.
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Alternatives Considered
We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.
Read full review
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.
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Return on Investment
  • Create secure business environment.
  • Save upto 90% of manual labor.
  • Improve my sales and marketing ROI.
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  • 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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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.