The internet of things is an approach that characterizes the interconnected world. The application of IoT in various sectors like the automotive, manufacturing, heavy industrial, and energy sectors has transformed the industries akin to the first industrial revolution.
The precise and timely data provided by low-cost connected IoT sensors has given rise to data-driven artificial intelligence based cyber-physical systems. These systems can anticipate inadequacy before they impact performance, thus helping improve the bottom-line.
However, this data deluge poses a key challenge to the industries, which is to maintain and preserve the latent value of sensor data. This real-time streaming of data is likely to create more problems than it solves if the organization is not prepared to optimally handle and utilize it, resulting in Digital Exhaust.
To avoid IIoT digital exhaust and maximizing the value extraction from the sensor data, enterprises need to develop robust and future-proof data retention and governance policies.
So how can one avoid discarding data that might provide valuable insight or be monetizable, while still not storing everything?
Dean Hamilton, GM IoT at Accelerite, in his article Avoiding industrial IoT digital exhaust with machine learning, discusses how a machine learning based predictive analytics approach helps the entire system to react quickly and get smarter over time by avoiding IIoT digital exhaust.
Accelerite offers SCAIDATM (Supervisory Control with Artificial Intelligence Data Analytics) solution, built on top of Concert- our IoT service creation and Enrichment platform. Concert SCAIDATM makes it possible for production line subject matter experts to make their own JIT process and AI-assisted analysis innovations without software developer resources. Know more about how Concert SCAIDA evolves factories towards the smart factory vision.