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IBM Machine Learning for z/OS vs. SAS Enterprise Miner

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM Machine Learning for z/OS

    Score10 out of 10
    N/AIBM 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

    SAS Enterprise Miner

    Score9 out of 10
    N/ASAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.N/A
    Pricing
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Features
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM Machine Learning for z/OS and SAS Enterprise Miner
    Feature
    IBM Machine Learning for z/OS
    -
    Ratings
    SAS Enterprise Miner
    8.8
    4 Ratings
    5% above category average
    Connect to Multiple Data Sources00 Ratings8.14 Ratings
    Extend Existing Data Sources00 Ratings9.04 Ratings
    Automatic Data Format Detection00 Ratings9.34 Ratings
    MDM Integration00 Ratings9.02 Ratings
    Data Exploration
    Comparison of Data Exploration features of IBM Machine Learning for z/OS and SAS Enterprise Miner
    Feature
    IBM Machine Learning for z/OS
    -
    Ratings
    SAS Enterprise Miner
    8.1
    4 Ratings
    4% below category average
    Visualization00 Ratings7.14 Ratings
    Interactive Data Analysis00 Ratings9.14 Ratings
    Data Preparation
    Comparison of Data Preparation features of IBM Machine Learning for z/OS and SAS Enterprise Miner
    Feature
    IBM Machine Learning for z/OS
    -
    Ratings
    SAS Enterprise Miner
    8.0
    4 Ratings
    2% below category average
    Interactive Data Cleaning and Enrichment00 Ratings7.84 Ratings
    Data Transformations00 Ratings8.24 Ratings
    Data Encryption00 Ratings8.12 Ratings
    Built-in Processors00 Ratings8.12 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of IBM Machine Learning for z/OS and SAS Enterprise Miner
    Feature
    IBM Machine Learning for z/OS
    -
    Ratings
    SAS Enterprise Miner
    8.8
    4 Ratings
    4% above category average
    Multiple Model Development Languages and Tools00 Ratings7.54 Ratings
    Automated Machine Learning00 Ratings9.82 Ratings
    Single platform for multiple model development00 Ratings8.54 Ratings
    Self-Service Model Delivery00 Ratings9.23 Ratings
    Model Deployment
    Comparison of Model Deployment features of IBM Machine Learning for z/OS and SAS Enterprise Miner
    Feature
    IBM Machine Learning for z/OS
    -
    Ratings
    SAS Enterprise Miner
    7.8
    4 Ratings
    9% below category average
    Flexible Model Publishing Options00 Ratings7.04 Ratings
    Security, Governance, and Cost Controls00 Ratings8.54 Ratings
    Best Alternatives
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Small Businesses
    TensorFlow
    Score7.6 out of 10
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    Score8.9 out of 10
    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    Google Cloud AI
    Score8.7 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Likelihood to Recommend
    10.0
    (2 ratings)
    9.9
    (4 ratings)
    Support Rating
    4.0
    (1 ratings)
    10.0
    (2 ratings)
    User Testimonials
    IBM Machine Learning for z/OSSAS Enterprise Miner
    Likelihood to Recommend
    IBM
    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.
    Incentivized
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    SAS
    SAS Enterprise Miner is world-class software for individuals interested in developing reproducible models in a reasonable amount of time. Perhaps the most useful part of SAS Enterprise Miner is the ability to compare models with other models without writing code. The ensemble modeling capabilities is the easiest way to do ensemble modeling I have come across. SAS Enterprise Miner is well-suited for beginning to advanced analysts who know something about advanced analytics. The software is not well-suited for analysts or companies that have little interest in advanced modeling.
    Incentivized
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    Pros
    IBM
    • Good machine learning tool
    • Easy integration
    Incentivized
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    SAS
    • Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
    • The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
    • The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
    • Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
    Incentivized
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    Cons
    IBM
    • Proper usage of REST API documentation is missing.
    • Not localization friendly, cannot support regional or local language documents.
    Incentivized
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    SAS
    • SAS is not as user friendly as other stats software.
    Incentivized
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    Support Rating
    IBM
    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.
    Incentivized
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    SAS
    SAS' customer support used to be non-existent many years ago. Today, contacting SAS customer support is great. They are responsible, knowledgable, and seem to have an interest in getting the results right the first time. With that said, Enterprise Miner's online support is weak, probably because the user base is much smaller than other tools.
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    Alternatives Considered
    IBM
    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.
    Incentivized
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    SAS
    SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.
    Incentivized
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    Return on Investment
    IBM
    • Create secure business environment.
    • Save upto 90% of manual labor.
    • Improve my sales and marketing ROI.
    Incentivized
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    SAS
    • In our organization, users were using SAS already so the learning curve was really low. Within a few weeks after the implementation, the users were already delivering models developed with SAS Enterprise Miner. It is difficult to talk about ROI as models were already being developed before. It was mostly a change of technology and it was a smooth transition.
    • Going with Enterprise Miner came with migration from desktop use of SAS to a server use of SAS. This created a new role of SAS administrator. This was obviously a cost but as the use of SAS increased greatly, it was expected.
    • From a methodology standpoint, Enterprise Miner helped greatly in the documentation of the model development which was a requirement in a few groups such as the risk groups. Having a visual "GUI-like" approach to development, the flowchart or diagram of the project in Miner was able to give users a good understanding of the approach the analyst took to develop the model.
    Incentivized
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    ScreenShots