Edge Analytics

An important use of machine data is to calculate different KPIs to measure the performance and behaviour of machines or processes. Edge analytics is a perfect tool both for collecting the relevant data and calculating

Siemens Mindsphere has taken the lead as the open operating system for Industrial IoT, according to Forrester. The cloud-based platform is built to connect and integrate the entire manufacturing process, to give manufacturers the capability to implement a wide array of solutions

OSIsoft PI System is one of the most widely deployed solutions for capturing and storing machine data within manufacturing, energy, utilities, pharma, transportation and facility management. 

The use of machine learning (ML) models is becoming more and more common in industrial IoT applications, especially for use cases such as anomaly detection and predictive maintenance. ML models are typically trained in the cloud using large stored datasets and iterative processing on powerful servers.

Extracting valuable insights out of data collected from machine sensors can be hard, often requiring analyzing data from many sensors in parallel.
Due to the complexity, machine learning (ML) methods are becoming more and more popular to analyze these datasets.

If you are in manufacturing, process industry, oil & gas or other asset-rich industries with many machines & equipment there is a good chance you are thinking about how to leverage this data in a better way. Either to gain insights to improve your processes or for optimizing your maintenance for an improved uptime.

Based on customer requests we have implemented support for running Crosser Edge Streaming Analytics on Azure IoT Edge. Now you can innovate much faster and without the cost, risk and developer dependencies that come with writing code.

Your data scientists are among your most valuable assets. But if you are not making full use of edge analytics then you could be wasting their time.  

To be locked in or not to be locked in. That is the question facing many IoT project owners when it comes to the technology stack. But there is a smart way of solving the problem.

Contrary to what you might think, you don’t need cloud providers or platforms to run your enterprise IoT project.