Act on IoT insights in an instant
Empower people—such as operations managers—to turn IoT data into actionable insights using self-service IoT analytics. Understand, predict and act on the powerful insights revealed in your IoT data using integrated real-time streaming and predictive analytics capabilities. Imagine what you can do with a holistic view of your IoT data with IoT analytics from Software AG.
Our customers—
truly connected enterprises
Meet our IoT analytics products
Get faster ROI with self-service IoT analytics
Analyze your data using a straightforward drag-and-drop UI based on the concept of reusing flexible building blocks. Analytics on real-time data can be that simple! Business users and operational experts can build sophisticated analytics on their own without writing code or needing IT support. Developers also have a powerful coding environment to create and deploy advanced analytics apps in the cloud, at the edge and as a stand-alone solution. Build analytics for a wide range of use cases, from alarms analytics to condition monitoring to prediction of failure, maintenance and utilization.
Analyze & act on data in real time
Put the world’s #1 streaming analytics engine to work for your business. Apply analytics to large volumes of fast-moving data. Look for patterns and process incoming data in real time. Identify problems and respond immediately via alarms and notifications. Using a drag-and-drop, no-code environment, anyone can design advanced rules to monitor and act on any number of event streams and high-velocity event data of any kind—without the help of IT. Streaming analytics is ideal for condition monitoring in real time as well as more complex pattern matching and analysis.
Predict & prevent problems
Act precisely and so quickly that no one even noticed there was an issue. Use AI, deep learning, machine learning and predictive analytics to predict when a problem is likely to occur. Leverage machine learning models to score device data to understand areas to investigate. Operationalize AI and other advanced IoT data analytics models without the time and cost of expensive custom coding. Machine learning is ideal for anomaly detection and predictive maintenance.
Make decisions on the shop floor
Make front-line operators self-sufficient. Empower process engineers, plant managers and non-data scientists to optimize operations anytime, using self-service industrial analytics. No data science expertise required to find out what’s causing production bottlenecks or quality issues or how to increase overall equipment effectiveness.
Learn from past experience
Store data in a cost-efficient way for archival, analytical and machine learning model training purposes. Schedule offloads of operational data to local stores, a local-based file or a cloud-based data lake of your choice. Then access this data directly for deeper analysis and machine learning using your preferred business intelligence and machine learning tools. Scale up processing as data volumes grow.
Take intelligence to the edge
Extend advanced analytics and machine learning to devices, wherever they
are, to make the most of the data they generate. Edge analytics—close to
or on equipment—collects and processes data without having to send that
data back to the cloud for analysis. Make time-critical decisions when it
matters most. Reduce network bandwidth as well as load on back-end
servers to lower costs.
View & share IoT data in real time
Use business intelligence and visual IoT data analytics to view and share your IoT data and performance in real time. Anyone can log onto dashboards to get a complete and up-to-date view of the current state of their processes and devices. Take stock of important KPIs, detect patterns in high-volume data, and see where to act when it matters most.
Advanced tooling for data experts
We’ve got technology experts covered too with a full analytics development
environment when you need to build something uniquely complex.
Create advanced analytics apps and deploy in the cloud, at the edge or as
a stand-alone solution.
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