Data intelligence – is edge analytics the saviour of IoT?

With IoT, businesses were meant to be able to leverage local data that would drive local decisions and improve local performance. What has actually happened is the creation of a huge data source that, at best, has added to the depth of overall business intelligence. The sheer scale is proving overwhelming and is starting to echo the disappointment that ‘big data’ created. And, if businesses believe the arrival of 5G will enhance this process – think again. Bandwidth will continue to come under extreme pressure, filling up quickly with the movement of voice and audio.

Decisions at the edge

It is now time that everyone stops, takes a giant step back and ask themselves: ‘what value is the business wanting to get from IoT?’ The outcome should be to facilitate better, faster and more informed local decision making — both automated through machine learning and to equip individuals with the insight they need to be able to make instant decisions on the front line.

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The concept of edge computing is starting to gain momentum. The goal is to better manage IoT data where it is created and only transmit the most relevant data to the centre for analysis in order to address bandwidth issues. But what is the value of simply managing data at the edge? Yes, it tackles the data transfer challenges but how does it support any of the much needed real-time decision making that is required to achieve tangible value from the, often substantial, IoT investment?

It is the ability to analyse data at the edge — effectively on site — that will unlock meaningful new opportunities for businesses. Just consider the value of providing individual supermarket store managers with insight into the operational performance and temperatures of their fridges in real- or near real-time. They will be better equipped to take immediate action if necessary to help prevent food wastage. Combine this with predictive analytics of historical performance data that shows potential points of failure and a tangible data set is created. The ability to collect and analyse data at the edge undoubtedly changes the way IoT can be leveraged – and will mean that the return on the investment in IoT can finally be realised.

But does the value that businesses can get from the data created from IoT, when deployed the right way, need to end there? Are they missing a significant trick when it comes to gaining even more value from the data? What about monetising the data?

Additional value

Loading every piece of IoT sensor data into a cloud based database and mixing it together is not enough: data without context essentially has no meaning. So where does the context exist? Where is the additional data source, or sources, that when combined can produce a golden nugget of insight?

Whilst sales and stock data can give a store the insight it needs to improve what is happening on the shop floor, with edge computing, linking external data sources and/or voice and video can further enhance store experience and performance through improved shopper interactions leading to increased sales. Edge analytics also allows for the collection and analysis of machine performance data that can also be extremely valuable to the refrigeration unit manufacturer, particularly when it is combined with contextual information about different locations and operations.

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With such insight, manufacturers could incorporate this information into their design process to enhance efficiency and be able to identify and address any issues within specific operational areas. The same can be applied to insurance companies who could discover significant value and an in-depth understanding of the market from reviewing consolidated information from IoT sensors monitoring hazardous environments. Moreover, if companies are able to demonstrate increased safety and regulatory compliance, they too may benefit from potentially lower premiums. A win, win all round.

The possibilities available as a result of greater insight derived from the data collected from IoT is tantalising but there can be some considerations for businesses – such as who owns the data?

The company that deploys the sensors has ownership of its own operational data but can the manufacturer and vendor of the refrigeration unit also claim to own the product’s performance data?

Additionally, how is this data being presented that provides value to a specific third party sector and securely? How is the additional data context to be added – and by whom?

Layering data sources over the IoT delivered insight is vital in being able to truly monetise IoT – the whole is definitely greater than the sum of the parts when it comes to data – but achieving that requires a clearly defined business model and full understanding of issues, from ownership to security and data delivery.

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When it comes to achieving a return on their investment from IoT, businesses really need rethink how they are deploying it so that they are able to use the data to make better real time decisions and also be able to monetise it. However, for both to happen, and for IoT to not end up like big data, businesses need to embrace more effective data models, be able to add context and securely share the data with the right people.

Written by Peter Ruffley, chairman, Zizo
Written by Peter Ruffley, chairman, Zizo

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