What is IoT analytics?
IoT analytics is the application of data analysis methods to examine a wide range of data collected from any number of IoT devices. IoT analytics is used to derive understanding from the massive amount of data for actionable insights.
Analytics can be categorized in three categories:
- Historical analytics, which help you understand what has happened
- Real-time or streaming analytics, which help you understand what is happening now
- Predictive analytics, which help you understand what is likely to happen in the future
Why is IoT data analytics important?
The true value in IoT does not come from the generation, collection and storage of data on its own, but in the accurate, relevant and timely insights.
Analytics generates that value. IoT analytics applies context to IoT data to reveal useful information, which allows you to make impactful decisions.
For example, according to a Forrester report on the economic impact of IoT, advanced analytics produce recommendations that can extend the lifetime of equipment by as much as 200% while reducing electricity costs by up to 20%.
What can IoT analytics do?
IoT analytics gives understanding to your IoT data to allow you to predict and act on the powerful insights, for example, by using integrated real-time streaming and predictive analytics capabilities.
Learn from the past
Using historical data, which can provide insights into why an incident happened and how often it occurs, helps to improve efficiencies in production processes. Analytics software uses historical data to indicate possible root causes, so similar behavior can be avoided in the future.
An IoT data lake is a way for you to store your IoT data over time. Later, you can access your IoT data for historical analytics. Learn from the past and discover trends by combining historical with real-time IoT analytics to make smarter decisions.
Analyze data in real time
Streaming analytics, often a starting point for using IoT data to gain business value, is the processing and analysis of fast-moving live data from a variety of sources, including IoT devices, to raise automated, real-time actions or alerts and ensure equipment is running smoothly. This allows you to identify patterns, as well as filter and aggregate data. Streaming analytics lets you act in the moment with alerts and notifications.
Prepare for the future
By combining real-time streaming with historical IoT data, companies can create, train and execute machine learning models for predictive maintenance, anomaly detection and demand forecasting.
Machine learning, which is powered by IoT data, generates insight by using past behavior to identify patterns and builds models that help predict future behavior and events.
IoT analytics use case
Dürr, one of the world’s leading mechanical and plant engineering firms, wanted a way to allow customers to securely monitor the real-time production data from their robot painting stations. To do this, Dürr looked to add streaming analytics to its already highly automated, robotic paint finishing lines.
Dürr utilized Cumulocity IoT to tackle this project, a low code platform for powerful real-time anomaly detection. The Cumulocity IoT system monitors and sends up to 10,000 signals from robots, each equipped with a hundred or more sensors.
With Cumulocity’s IoT analytics capabilities, self-service predictive maintenance and anomaly detection are utilized, and air nozzles are now cleaned only when needed to prevent contamination. Additionally, the company has eliminated defective paint runs due to contaminated air nozzles—because the new system detects such faults immediately.
Now, Dürr’s customers get instant alerts when air bubbles get into the painting system, which prevents cars from receiving inconsistent amounts of paint. And when algorithms automatically detect a dropout, the robots stop, and technicians are sent descriptive error messages and suggestions for a fix.
Key considerations for IoT analytics
Organizations frequently start an IoT project without a clear idea of what the primary goal should be, or even whether substantial value lies ahead. In fact, Beecham Research estimates that nearly three quarters of IoT projects won't be considered successful.
What companies often overlook is that the value of IoT is not in the connected devices and sensors, but in the data they collect and the way it helps customers achieve their goals. You need accurate, relevant data, processed in the correct way, to drive every business decision and reach targets such as greater uptime, efficiency, and a shfit to new business models.
However, these insights need to be accessible, configurable and sophisticated. That’s where a powerful IoT analytics tool can help.
IoT analytics tools
What if you could collect, analyze and monitor data from any “thing”? Use self-service, real-time streaming analytics to visualize IoT data?
When machines talk, they empower your colleagues, customers and partners to capitalize on IoT data.
Discover what you can do with a holistic view of your IoT data with IoT analytics tools from Software AG.
Empower people—such as operations managers—to turn IoT data into actionable insights using a self-service IoT analytics platform. Real-time streaming analytics, historical IoT data analytics and machine learning/predictive analytics come together in one IoT analytics platform—integrated and available on the cloud and/or at the edge. Find out what the Cumulocity IoT platform can do for you today.
Empower process and asset experts with advanced self-service industrial analytics to analyze, monitor and predict the operational performance of manufacturing processes. TrendMiner is Software AG’s self-service industrial analytics software for smart factories and Industry 4.0 operations. If you’re on a quest to continuously improve your production processes, take a look at TrendMiner. Made by engineers for engineers, TrendMiner is based on a high-performance analytics engine for sensor-generated time-series data. Process engineers and operators can easily search for trends and question process data directly—on their own, without the help of a data scientist.