IBM SPSS Modeler

Score9.4 out of 10

44 Reviews and Ratings

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

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.

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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.

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

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

#1 most frequent

Finance and Insurance

22.9%

78 installations of 341

#2 most frequent

Educational Services

12.6%

43 installations of 341

#3 most frequent

Professional, Scientific, and Technical Services

11.4%

39 installations of 341

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

George DeMaagd, PharmD.,BCPS View 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

Ben Holmes View 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

Tim Daciuk View 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

Char Paul View 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

SAS Viya

RapidMiner

KNIME Analytics Platform

IBM SPSS Modeler

What is IBM SPSS Modeler?

Categories & Use Cases

Media

IBM SPSS Modeler Key Features

Top Performing Features

Automatic Data Format Detection

MDM Integration

Visualization

Areas for Improvement

Security, Governance, and Cost Controls

Connect to Multiple Data Sources

Extend Existing Data Sources

Most Frequent Users

Finance and Insurance

Educational Services

Professional, Scientific, and Technical Services

IBM SPSS Modeler Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Pros

Cons

Return on Investment

Other Software Used

Pros

Cons

Most Important Features

Return on Investment

Alternatives Considered

Other Software Used

Pros

Cons

Return on Investment

IBM SPSS Modeler Alternatives