SPSS® Modeler can read data from flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop. You can extend the capabilities of SPSS Modeler to the Analytic Server with our perpetual license.
Easy model deployment
From Scikit-learn and Tensorflow to SPSS Modeler, save and deploy models from the most popular machine learningframeworks using the tools of your choice: including notebooks and Modeler Flows in Watson Studio Desktopor any IDE used for Python.
Automatic data preparation
SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes.
Powerful graphics engine
Leverage Watson Studio Desktop’s powerful graphics engine to bring your insights to life. The smart chart recommender finds the perfect chart for your data from among dozens of options, so you can share your insights quickly and easily using compelling visualizations.
Visual analysis streams
SPSS modeler provides an intuitive graphical interface to help visualize each step in the data mining process as part of a stream. Now analysts and business users can easily add expertise and business knowledge to the process.
SPSS Modeler can test multiple modeling methods, compare results and select which model to deploy in a single run. This enables you to quickly choose the best performing algorithm based on model performance.
A range of algorithmic methods
SPSS Modeler offers multiple machine learning techniques — including classification, segmentation and association algorithms including out-of-the-box algorithms that leverage Python and Spark. Users can now employ languages such as R and Python to extend modeling capabilities.
SPSS Modeler captures key concepts, themes, sentiments and trends by analyzing unstructured text data. Now you can uncover valuable insights in blog content, customer feedback, emails and social media comments.
Explore geographic data such as latitude and longitude, postal codes and addresses using SPSS Modeler. By combining that information with current and historical data you can generate better insights and improve predictive accuracy.
Support for open-source technologies
SPSS Modeler enables the use of R, Python, Spark and Hadoop to amplify the power of analytics. You can also extend and complement these technologies for more advanced analytics while maintaining control. SPSS Modeler Gold includes access to Watson Studio Desktop, which allows you to extend your Modeler streams with Jupyter Notebooks, enabling line of business users and data scientists to collaborate on the same platform.
Machine learning methods and algorithms
SPSS Modeler supports decision trees, neural networks and regression models. Now you can take advantage of ARMA, ARIMA and exponential smoothing; transfer functions with predictors and outlier detection; benefit from ensemble and hierarchical models; support vector machine and temporal causal modeling; and employ time series and spatial AR for spatiotemporal prediction. Generative adversarial networks (GANs) and reinforcement also enable deep learning.
Multiple deployment methods
IBM SPSS Modeler is also available as part of IBM Watson Studio, as well as the perpetual offering. Using Modeler Gold, data scientists can schedule jobs to run at desired times. IT administrators can integrate deployment into existing systems for batch, real-time or streaming.
Use case: Optimize logistics and prevent failures
Error-prone, manual processes lead to equipment failures
Duplicate processes and operational waste are too common
Business continuity and security concerns are not being met
Predicting potential maintenance issues or optimizing routes have never been easier with this visual drag-and-drop data science tool.
Use case: Build new offers and innovating business models
Understanding how customers are reacting to and acting on information is difficult
Creating the right offers for the right channels is challenging
Spending too much time wrangling data and scripting the information flow inhibits efficiency and innovation
From data preparation to applying machine learning algorithms, SPSS Modeler enables new ways of exploiting information. Now you can confidently create new offers, drive channel performance and optimize business processes for optimum team productivity.
Use case: Operational efficiency and forecast accuracy
Working capital is scarce and warehouse costs are eating into the budget
There is a need to shrink stock without risking stockouts or impacting customer service
Inaccurate forecasts lead to poor planning and inability to meet predicted demand
SPSS Modeler drives the forecasting process in IBM Planning Analytics, enabling supply chain leaders to cut down the margin for error in the forecasting and planning process. This approach optimizes stock levels and increases the return on working capital available to the business, improving operational efficiency throughout the enterprise.