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How to integrate SCADA into scalable data analytics

Traditional operational systems can benefit from the use of sensors and modern cloud technology to become the new Industrial Internet of Things

Over the last five decades, Operational Technology (OT) such as SCADA has been widely used to manage, monitor and control physical devices and facility-based processes within industrial operations. While OTs continue to help industries such as mining, manufacturing, construction and utilities, the next step is to integrate these systems with sensors and cloud technology, transforming traditional SCADA into the new Industrial Internet of Things (IIoT).

Traditionally, SCADA is supported by blunt condition-based maintenance tools within centralised systems, which has limitations such as technology fragmentation, lack of connectivity and unsynchronised data sets. With recent Industry 4.0 advances and the decentralised nature of the Internet of Things, the role of existing systems like SCADA is being disrupted. Traditional systems that collect and store data can benefit from modern, more economical predictive processes and automated actions for increased efficiency and reduced operational costs.

The good news is that it is possible to implement IIoT solutions with most SCADA systems without interrupting the existing infrastructure. Integrating OTs with sensors and advanced data analytics is quicker and easier than most organisations think.

“For a water utility customer, SpiralData ingested data from a SCADA system (Ignition) into a scalable data analytics platform in under a day. Integration between OT and IIoT is fast and no longer a bottleneck”, says Chris Jansz, CTO of SpiralData.

Any SCADA tag is able to be transmitted to the cloud securely using store and forward. Data throughput is optimised and therefore fast, generating a highly cost-efficient data storage. In the cloud, data lakes can be implemented easily for future proofing, catering to analytics requirements with minimal changes to the existing implementation. Finally, machine learning workbenches that handle model training and deployment can be scaled up and down depending on needs. Two-way communication is possible, enabling local machine learning inferences to be deployed at the edge for low-latency and low-cost predictive analytics.

Lifting and shifting SCADA datasets to your analytics platform is no longer the barrier to data-based decisions it once was. It is an opportunity to optimise the network and its assets, resulting in new predictive processes and automated tasks for reduced costs and improved efficiency.

SpiralData is a full service data analytics agency providing data strategy, enterprise technology, data science and custom AI solutions for organisations of all sizes looking to make data-based decisions. Learn more about how SpiralData helps create smarter organisations using data analytics.

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