SAS Analytics for IoT Features List

Streamlined, extensible ETL

  • Automatically transforms and loads key data fields into the sensor-based data model.
  • Lets you rapidly load IoT data, whether you have three fields (sensor ID, value and date time) or hundreds.
  • Includes sensor attributes, device attributes, hierarchies, measures and events.
  • Enables integration of additional field and production quality data with your sensor data, using comprehensive ETL capabilities.
  • Connects directly to SAS Event Stream Processing to integrate real-time information with historical records.

Flexible, sensor-focused data model

  • Provides a standardized, extensible sensor-based data model.
  • Integrates real-time and historical data, hierarchies and other relationships right out of the box.
  • Organizes large volumes of diverse IoT data for efficient analysis.
  • Provides a single version of the data for diverse users across the organization.

Unified, intuitive business-focused data selection user interface

  • Lets nontechnical users quickly select data for analysis without knowledge of the underlying technology and data structure.
  • Enables users to access available variables and attributes in their own business terminology.
  • Uses smart filters, predefined date windows and other shortcuts to increase efficiency and reduce errors.
  • Supports individual user needs by letting them select data for any combination of devices, sensors, measures and events.
  • Allows you to save, copy, reuse and share data selections across the organization.

Data profiles & explorations

  • Summarizes huge volumes of high-frequency data to understand where data are being collected and what is available for analysis.  
  • Reduces millions of sensor and event records to a manageable size while maintaining relationships and patterns within the data.
  • Visualizes IoT data to see time-series relationships among devices, events and sensor readings.


  • Enables users to easily prepare and transform data for analysis in SAS or third-party tools.
  • Transposes data from an efficient storage format to an analytics-ready format.
  • Interpolates missing values in the data.
  • Applies a fixed periodicity to reduce data size or commonize across sensors.
  • Lets users open the data in SAS Visual Analytics, SAS Visual Data Mining and Machine Learning, and SAS Studio, as well as third-party and open source applications.

Advanced analytics & machine learning

  • Combines data exploration, feature engineering and modern statistical, data mining and machine learning techniques in a single, scalable in-memory processing environment.
  • Lets users analyze data without writing code, using a drag-and-drop interactive interface.
  • Uses best practice templates (basic, intermediate or advanced) to get you started quickly with machine learning tasks.
  • Applies diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines.
  • Compares results of multiple machine learning algorithms with standardized tests to automatically identify champion models.

Streaming model execution

  • Analyzes and filters streaming data (data in motion) in real time.
  • Lets you create, deploy and manage advanced analytics models running on streaming data.
  • Scores data in real time and applies learning models that combine scoring and training.
  • Reduces downstream processing by cleansing, standardizing and filtering livestream data before it’s stored.

Public APIs

  • Allows external systems to access data in a way that optimizes IoT investments across the enterprise.
  • Lets you integrate SAS or third-party solutions into your IoT ecosystem.
  • Automatically populates external dashboards and reports with the latest data or lists of data selections.