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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM SPSS Modeler

    Score9.5 out of 10
    N/AIBM 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

    SAS Enterprise Guide

    Score9.4 out of 10
    N/ASAS Enterprise Guide is a menu-driven, Windows GUI tool for SAS.N/A
    Pricing
    IBM SPSS ModelerSAS Enterprise Guide
    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 ModelerSAS Enterprise Guide
    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 ModelerSAS Enterprise Guide
    Considered Both Products
    IBM
    No answer on this topic
    SAS
    Chose SAS Enterprise Guide
    Python-based platforms like Pandas or Spark are very good too at displaying data and do exploratory analysis. I definitely prefer them to SAS EG. It's just too slow, and doesn't let you peek into the data very easily. Lots of clicking, and I'd rather just write some code, …
    Incentivized
    Key User Insights
    Would buy again
    100%
    Would buy again
    5 Answers
    No answers on this topic
    Delivers good value for the price
    No answers on this topic
    No answers on this topic
    Happy with the feature set
    100%
    Happy with the feature set
    5 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    No answers on this topic
    No answers on this topic
    Implementation went as expected
    No answers on this topic
    No answers on this topic
    Features
    IBM SPSS ModelerSAS Enterprise Guide
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM SPSS Modeler and SAS Enterprise Guide
    Feature
    IBM SPSS Modeler
    8.9
    2 Ratings
    6% above category average
    SAS Enterprise Guide
    -
    Ratings
    Connect to Multiple Data Sources8.82 Ratings00 Ratings
    Extend Existing Data Sources8.82 Ratings00 Ratings
    Automatic Data Format Detection9.01 Ratings00 Ratings
    MDM Integration9.01 Ratings00 Ratings
    Data Exploration
    Comparison of Data Exploration features of IBM SPSS Modeler and SAS Enterprise Guide
    Feature
    IBM SPSS Modeler
    9.0
    1 Ratings
    6% above category average
    SAS Enterprise Guide
    -
    Ratings
    Visualization9.01 Ratings00 Ratings
    Interactive Data Analysis9.01 Ratings00 Ratings
    Data Preparation
    Comparison of Data Preparation features of IBM SPSS Modeler and SAS Enterprise Guide
    Feature
    IBM SPSS Modeler
    9.0
    1 Ratings
    9% above category average
    SAS Enterprise Guide
    -
    Ratings
    Interactive Data Cleaning and Enrichment9.01 Ratings00 Ratings
    Data Transformations9.01 Ratings00 Ratings
    Data Encryption9.01 Ratings00 Ratings
    Built-in Processors9.01 Ratings00 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of IBM SPSS Modeler and SAS Enterprise Guide
    Feature
    IBM SPSS Modeler
    9.0
    1 Ratings
    6% above category average
    SAS Enterprise Guide
    -
    Ratings
    Multiple Model Development Languages and Tools9.01 Ratings00 Ratings
    Automated Machine Learning9.01 Ratings00 Ratings
    Single platform for multiple model development9.01 Ratings00 Ratings
    Self-Service Model Delivery9.01 Ratings00 Ratings
    Model Deployment
    Comparison of Model Deployment features of IBM SPSS Modeler and SAS Enterprise Guide
    Feature
    IBM SPSS Modeler
    9.0
    1 Ratings
    6% above category average
    SAS Enterprise Guide
    -
    Ratings
    Flexible Model Publishing Options9.01 Ratings00 Ratings
    Security, Governance, and Cost Controls9.01 Ratings00 Ratings
    Best Alternatives
    IBM SPSS ModelerSAS Enterprise Guide
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    No answers on this topic
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    No answers on this topic
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    JMP Pro
    Score6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM SPSS ModelerSAS Enterprise Guide
    Likelihood to Recommend
    9.7
    (8 ratings)
    5.3
    (8 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    8.0
    (1 ratings)
    Usability
    8.9
    (2 ratings)
    5.0
    (2 ratings)
    Support Rating
    10.0
    (1 ratings)
    5.3
    (5 ratings)
    Implementation Rating
    -
    (0 ratings)
    7.0
    (1 ratings)
    User Testimonials
    IBM SPSS ModelerSAS Enterprise Guide
    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.
    Incentivized
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    SAS
    SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.
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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.
    Incentivized
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    SAS
    • Ability to load an AutoExec when opening a session ensuring everyone has the same global variables.
    • Formatting with Ctrl I. If you're reading someone else's code and it's not formatted correctly you can highlight the area and hit Ctrl I.
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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.
    Incentivized
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    SAS
    • Process time of data is a bit long. It depends on the size of your data and complexity of your project tree.
    • There is not enough online free training videos.
    • While working with the project tree sometimes the links between the modules are broken or the order for running the modules get mixed up. You should know your project tree by heart.
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    Likelihood to Renew
    IBM
    No answers on this topic
    SAS
    On account of current user experience and the organization-wide acceptance.
    Read full review
    Usability
    IBM
    The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources
    Incentivized
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    SAS
    It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.
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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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    SAS
    Although I use SAS support for information on functions, these are SAS related and haven't really come across anything that is specifically for SAS EG.
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    Implementation Rating
    IBM
    No answers on this topic
    SAS
    I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.
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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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    SAS
    Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.
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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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    SAS
    • Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore.
    • Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power.
    • Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required.
    Read full review
    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.