SPSS Modeler Benefits and Insights

Why use SPSS Modeler?

Key differentiators & advantages of SPSS Modeler

Empower Data-Based Decision-Making: Automate high-volume decision-making by integrating predictive models and scoring into business systems. Get insights on the most likely outcomes and identify key metrics to make strategically aligned decisions.
Supply Chain Management: Boost team productivity by optimizing business processes and address issues before they escalate with predictive maintenance. Monitor stock levels without risking stockouts or impacting customer service.
Gain Customer Insights: Turn business opportunities into profit by nurturing profitable customers, attracting new leads, detecting fraud and reducing risk. Drive channel performance by redefining customer retention and lead generation strategies.
Multiple Source Connectivity: Reads data from flat files, all major RDBMSs, spreadsheets, IBM Planning Analytics and Hadoop. Supports open-source technologies such as R, Python and Spark and scales by connecting to the Analytics server.
Flexible Deployment: Deploy anywhere – on-premise, in the cloud or go hybrid through embedded services. Deploy models created with tools such as Scikit-learn and Tensorflow. Include notebooks and workflows in Watson Studio Desktop or any IDE used for Python.
Ease of Use: Reduce time to value by creating interactive visualizations through intuitive drag-and-drop interfaces. Create accurate models quickly without needing specialized skills or IT help. Choose the best-performing model for scoring or further analysis by running multiple-model comparisons.
Data Preparation: Seamlessly analyze key metrics by transforming data in the best format for accurate predictive modeling. Monitors and reports all changes to data; generates recommendations to not use a data set if there is too much missing information.

Industry Expertise

The vendor provides data modeling to clients in diverse industries across the world. These include customer analytics and relationship management (CRM), insurance, healthcare, sales, inventory management, law enforcement, education, telecommunications, entertainment and more.

Key Features

Integrations: Connects to Oracle, IBM Cognos, SPSS Statistics, R and Python. Collaborate with data science teams on the same platform by extending its functionality with Jupyter Notebooks.
Machine Learning: Get answers based on realistically generated data by including data probability distributions through Monte Carlo simulation. Generate forecasts for one or more series over time and leverage classification, segmentation and association algorithms for statistical modeling.
- Text Analytics: Glean priceless business insights from customer feedback, emails, social media comments and blog content. Analyze text data to capture key themes, sentiments and trends through advanced linguistic technologies and natural language processing (NLP).
CRISP-DM: Organize project streams, outputs and annotations by phase for each data mining project. Toggle between the CRISP-DM view and the standard Classes view to see specific streams or output organized by type or phases. Create custom tooltips for each phase and take notes on the conclusions drawn from a particular model. Generate HTML reports to share insights with the project team.
Graphics Engine: Display data insights through the powerful graphics engine of Watson Studio Desktop. Visualize data by choosing the perfect chart from multiple options with the smart chart recommender.
Entity Analytics:: Build accurate models based on in-context enterprise data by resolving identity conflicts in disparate records. Improve data coherence and consistency by identifying relationships between disparate data sets.
In-Database Processing: Reduces latency and minimizes data movement by providing a number of capabilities.
- SQL Pushback: Boosts analytical performance by enabling in-database transformation and preparation — no need to write SQL.
- Scoring: Allows in-database scoring via custom adapters for IBM DB2, PureData System for Analytics and Teradata.
- In-database Mining: Build, score and store models from inside the workbench through integration with IBM InfoSphere Warehouse, Oracle Data Miner, Microsoft Analysis Server and more.

Limitations

At the time of this review, these are the limitations according to user feedback:

Source nodes with an output data model containing a list type aren't supported in streams.
A new model, when generated, isn't connected to any nodes.
If a flow contains Text Analytics nodes, teams can't run it on a Watson Machine Learning Server; they have to execute it locally.

Suite Support

Before reaching out to support, go through the knowledge center, product documentation and community forum for answers to queries and self-paced troubleshooting of issues. Go to Fix Central on the vendor’s website to find fixes for the software, hardware and OS.

Support for free trial subscribers is available through Stack Overflow.

Email: Not specified.

Phone: 1-800-426-7378. Or, connect to a chatbot on the support page.

Training: The vendor offers instructor-led and self-paced courses for data experts and analysts. Many third-party websites also provide training for the product.

Tickets: Submit a ticket by signing in to the vendor’s website.

Pricing & Cost Details

Includes Price/User and Minimum Commitment Terms for SPSS Modeler

SPSS Modeler Reviews & Pricing

What is SPSS Modeler?

#207