How to realise value from IoT data in the manufacturing industry

While many manufacturers have made initial forays into IoT, there have been challenges in translating their efforts into real results. One of the biggest challenges is how to extract maximum value from the terabytes of IoT data being created. If this doesn’t happen, nothing else matters. The value of IoT hinges on data, but only if that data can be leveraged to its fullest potential. By Theresa Bui, Director of Strategy, Cisco IoT.

The deluge of data highlights the need for more intelligent data management. What is required is the ability to extract, compute, move and integrate data from multiple sensors/sensor types and get that data to the right applications.

Connection Management

In a plant, goods are manufactured by various machines that leverage a range of IT applications. Applications and things also exist in a highly distributed environment, sometimes globally. The complexity of connecting, securing and managing all of these devices is huge. And a lot of data remains locked inside its sources, which are often legacy devices. All those connected things must be turned into trusted IoT devices and must connect to a secure, intent-based network.

Fog Computing

When devices are generating terabytes of data, it’s not practical or desirable to send all that data to a data centre. There is a need to increase the efficiency of how and where data is processed, automatically determining what needs to stay local, be shared on site, or delivered to the cloud. Fog computing makes it possible to process data at the edge and execute fast decisions close to the action, when and where that information is needed most. This allows manufacturers to make operational and safety decisions in the right places at the right time, working around bandwidth constraints and latencies that would otherwise delay urgent responses.

Data Delivery

Data delivery is about getting the right data to the right applications at the right time—across the edge and to private, public and multi-cloud environments.  Rules should be applied around ownership, privacy and security before the data is programmatically and reliably delivered across multiple end-points in the ecosystem to realise valuable business outcomes.

Industrial machine manufacturers like FANUC or Makino, benefit greatly from extracting data from their equipment running on factory floors. However, many factory owners are concerned about enabling a connection to a third party and what data is being collected and shared with whom. It is important to ensure that both parties – the Thing Maker and the Thing Owner – get access to only the data they are authorised to access, while protecting the connection from threats and vulnerabilities. For example, car manufacturers don’t necessarily want their competitors to know the number of layers of paint they apply to the cars they produce because that is proprietary information. So the data being collected from machines that automate the paint applying process needs to be programmatically separated into different streams with a set of rules put in place to establish what information goes where.

For too long in manufacturing, data has often remained locked inside its sources with no programmatic way to move to the right applications at the right time. And, it’s been challenging to control data privacy, security, and ownership. But manufacturers are starting to pursue a new level of sophistication in their IoT requirements.

They understand that data produced by their ‘things’ is an asset. But more manufacturers are embracing IoT so that they can capture more value from their devices. Whilst some companies have been working to try and build their own solutions – at great effort, time and cost – there are now solutions available in the market that precisely address the challenges of extracting data and making it usable. Look for a new class of platform – an IoT data fabric – to unlock and transform data into value.

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