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Home Sponsored Editorial

The challenges of data collection in infrastructure

by April Shepherd
November 7, 2022
in Big Data, Smart Infrastructure, Sponsored Editorial, Technology
Reading Time: 3 mins read
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With over 30 years’ experience providing resilient industrial technology solutions, Madison Technologies is familiar with many of the challenges the industrial sector faces as it embraces digital transformation.

The buzz around data grows louder every day. By the time you finish reading this article, an estimated 16 billion connected IoT devices around the world will transmit over 300 trillion bytes of data.

All this data has the power to create value through greater understanding – providing analytics and insights that can deliver sustainable change and positive impact for all organisations. However, before data can be used to better understand and manage resources, improve safety and increase efficiencies, it needs to be collected.

For industrial operating environments, collection of data starts at the sensing and edge layer, where the initial data acquisition takes place. As technology improvements are made, the data sources we can measure continue to grow. In 2022, it is possible to measure almost anything, and data sensors can be placed (almost) anywhere.

Once the data has been captured, it needs to be transferred through a connectivity layer to an on-premise or cloud platform. This could be through traditional wired communication networks or wireless communications.

Where high availability and reliability in critical applications is required, network redundancy or mesh networks allow for the constant connectivity of static or mobile assets, so they stay connected to the network. Finally, private and mobile network communications enable connectivity in remote and difficult to reach areas.

For some processes, real-time decision-making is required, and the advancement of edge computing, machine learning and artificial intelligence means data can be captured, processed and turned into an action at or near the physical location where it’s created.

Without the need to transfer the data to the on-premise or cloud platform, this real-time analysis of data decreases the time to insight from minutes to micro-seconds, powering quick decision-making for solutions like smart traffic control systems.

For processes that don’t require real-time decisionmaking, the transferred data is now ready to be presented to a platform or application to be analysed, visualised and contextualised. It’s at this layer that the value hidden in the collection of data can start to be unlocked.

This might be to identify a process that needs attention or improvement, clarify factors that influence human behaviour, provide insight to anticipate a future condition, or identify key sources of a problem that needs to be addressed.

The final step in this process is to mobilise and present the data to operational stakeholders across an organisation, so that informed decisionmaking can take place. Armed with this data, organisations can now identify trends and influence change.

Across this entire process, consideration must be given to cybersecurity, and the need to keep the data secure. Preventing manipulation of the data so that it can be trusted enables more accurate data driven decision-making.

Cybersecurity technologies built specifically for industrial environments, in alignment with IEC 62443 cybersecurity requirements, can proactively identify and mitigate cyberthreats in IT/OT environments and ensure business sustainability.

Consider how data is currently collected in your operations. Is it set up to be insightful, secure, and is the right data going to the right people?

This sponsored editorial is brought to you by Madison Technologies. For further information, please visit www.madison.tech or phone 1800 72 79 79.

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