Data intelligence — is edge analytics the saviour of IoT?

As it stands, the existing IoT model isn’t working. The concept of transferring vast amounts data that’s collected from an army of sensors to a central repository is neither sustainable or affordable

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.

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.

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.