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IBM SPSS Modeler

Score9.3 out of 10

43 Reviews and Ratings

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What is IBM SPSS Modeler?

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.

Categories & Use Cases

Media

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.
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.
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.
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.

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Key Features

Learn about the best (and worst!) features IBM SPSS Modeler has to offer, as determined by TrustRadius' reviewers.
Based on 43 ratings of IBM SPSS Modeler's features
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Top Performing Features

  • Automatic Data Format Detection

    Automatic detection of data formats and schemas

    Category average: 9.2

  • MDM Integration

    Integration with MDM and metadata dictionaries

    Category average: 7.8

  • Visualization

    The product’s support and tooling for analysis and visualization of data.

    Category average: 8.2

Areas for Improvement

  • Security, Governance, and Cost Controls

    Built-in controls to mitigate compliance and audit risk with user activity tracking

    Category average: 8.5

  • Connect to Multiple Data Sources

    Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

    Category average: 8.7

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

Reviews

What users are saying about IBM SPSS Modeler.
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SPSS Modeler Review

Incentivized
Jim McDonough
President in Corporate at DataHub (11-50 employees employees)

Use Cases and Deployment Scope

IBM SPSS Modeler provides us with a no-code data science and predictive analytics platform for developing and deploying machine learning models. It give us the ability to model out a idea to test the hypotheses.

Pros

  • Predictive Modeling
  • Data Preparation
  • Text Mining

Cons

  • User Interface could improve
  • More statt capablities

Return on Investment

  • User Interface
  • No Code / Low Code

Usability

Alternatives Considered

SAS Viya

Other Software Used

Red Hat OpenShift, IBM Cloud Pak for Data

IBM SPSS Modeler a Statistical Tool for the New Researcher

Incentivized
George DeMaagd, PharmD.,BCPSView profile
Professor of Pharmacy and Associate Dean of Academic Administration College of Pharmacy at UUCOP College of Pharmacy (201-500 employees employees)

Use Cases and Deployment Scope

IBM SPSS Modeler is used mainly in the world of social sciences and psychology and other research. This product is user friendly and a new user can use for statistical analyses without much practice. This tool provides a range of statistical techniques (descriptive statistics, regression and other tests) and can be used for a variety of data sets with good outcomes. You can also use with Microsoft Excel which makes it very practical. There are good support links available to assist in the use of this tool. The only negative aspects of this tool are cost, and it may not be as flexible as some programs.

Pros

  • Wide range of statistical techniques
  • Good tables and charts
  • Integrates with Excel

Cons

  • Appears slow with very large data sets
  • Lack of frequent updates
  • cost reduction

Return on Investment

  • Good results for shared research project data with others

Usability

Alternatives Considered

Python IDLE

SPSS Modeler is a great addition to Data Science toolkit, faster results than coding

Incentivized
Ben HolmesView profile
Senior Data Scientist in Information Technology at Arkansas Blue Cross Blue Shield (1001-5000 employees employees)

Pros

  • Fast code builder.
  • No need to maintain software versioning.
  • Visual code, easy to see what's happening.

Cons

  • Details of models and nodes requires some "digging", "clicking".
  • Customization of versions is hard to implement.
  • Latest open source code packages take time to integrate into the application.

Return on Investment

  • Several projects completed quickly and without substantial coding.
  • Saves some time by allowing younger experienced employees to jump right into data science.
  • Comes with the package for Cloud Pak for Data, so its available that way as a toolkit, saves money.

Other Software Used

Jupyter Notebook, TIBCO Data Science (including Team Studio and Statistica), RStudio

IBM SPSS Modeler is My Choice

Incentivized
Tim DaciukView profile
Director, Advanced Analytics in Product Management at SmartSimple Software Inc (51-200 employees employees)

Pros

  • 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.

Cons

  • The graphics are weak.

Most Important Features

  • Range of algorithms
  • Easy development environment
  • Ability to customize

Return on Investment

  • Quick development.
  • Robust environment to handle the largest dat challenges.
  • Ability to incorporate freeform text.

Alternatives Considered

RapidMiner Studio and Dataminr Pulse

Other Software Used

RapidMiner Studio, Weka.IO, Python IDLE

Appreciation of Stats: Recommendation for Consumers and Producers

Incentivized
Char PaulView profile
Psych n Stats Tutor in Professional Services at Self-employed (1-10 employees employees)

Pros

  • Inputting data
  • Test selection
  • Data manipulation
  • Providing sandbox data sets
  • Two week trial for students considering purchase
  • Reasonable rent prices

Cons

  • Including MCAR in basic packages
  • Standardise language across dialogue windows
  • Enable 'undo' after sorting cases
  • Include Bayesian options

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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