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Learnings from IoT implementations outside of Commercial Property



There can be no denying that the use of Internet of Things (IoT) sensors and real-time data is increasingly gaining traction. The possibilities around the production and utilisation of data have exploded with maturing technology and we are learning how to sift through which data is meaningful for the three main use cases of IoT: operational efficiency, risk and ESG.


When it comes to the future of risk management and how we monitor ESG, both are reliant on IoT and real-time data. The insurance industry is lagging behind their customers in adoption and are at the early part of learning how best to combine the well-known static data sources (such as COPE for commercial property) alongside dynamic and real-time sources of data.


The SENSE Consortium is focused on commercial property IoT and the use of real-time data and the ‘how to’ within risk. SENSE brings together commercial property owner/ managers and the insurance industry to drive adoption of IoT and real-time data to ameliorate risk, drive down the total cost of risk and achieve ESG targets.


SENSE welcomed experts and members of the insurance sector and beyond to ask (and answer) the following question: “What can Commercial Property insurance learn from other IoT implementations?”


A superb panel of guests – including Alice deBiasio (Vice President and General Manager of Sensitech and Lead Digital Solutions with Carrier Refrigeration), Ian Storey (Founder and President at I.B. Storey Inc.) and Dan Fiehn (Chief Operating Officer at Incited) – came together to explore solutions that are already proven to be a gamechanger for insurance and yet with low levels of adoption.


Beyond commercial property, the panellists gave their expertise as to how is IoT being used and what are the impacts:


Supply chain and the transportation of goods: The pandemic shined a spotlight on the supply chains within organisations’ own business and operations, which accelerated the adoption of connected technology. The ability to monitor the condition and location of goods during transportation has enabled significant operational benefits to be realised alongside fewer lost or perished goods.


Entertainment & sporting venues: The multi-faceted benefits of IoT within the context of the National Hockey League of Canada stadia (NHL) include enhancing the safety and operability of the facility, in particular to monitor and mitigate refrigerant leakage (a significant risk in light of the toxicity of ammonia). IoT is also being used to assess athlete performance via the use of body sensors (a literal chip on the shoulder!).


An emerging area for IoT for entertainment/ sporting venues is assuring the safety of those attending sporting events and making people feel more comfortable regarding indoor air quality – a new risk tied to the pandemic.


Motor fleet: Within a large commercial fleet encompassing hundreds or thousands of vehicles, being able to visualise individual driver behaviour and react to the quality through real-time feedback is invaluable. This type of data and associated behavioural change should enable the insurance industry to build more intelligent products and services. With the use of IoT, insurance is no longer just about product, it’s about product and service.


For example, the behavioural shift was further enabled via the use of revised incentivisation schemes and regular updates (nudges, health and safety reminders), which had a knock-on impact on running costs like petrol usage as well as a reduction in incidents.


A new added value service, focused on risk for the betterment of all stakeholders.


Whilst the panellists were clear on their use cases and learnings, the insurance industry still has a long way to go to even match where their client base is in their advancement of IoT and the use of real-time data. Education is absolutely critical to encourage both the use of IoT and the understanding of what the data yielded can do for organisations across operations, risk and ESG.


With the multiplicity of data sources, one major concern that arises around IoT adoption is that of ‘data deluge’: with so much data, how do insurers and organisations decide what’s valuable and meaningful?


Lacking the necessary historic references, more work is needed to effectively analyse these ‘new’ and improved and real-time risk profiles to better understand the changing dynamics across the total cost of risk.


What big barriers/hurdles has the panel experienced in deploying IoT and how are these challenges solved?


When utilising technology that analyses behaviour, one clear hurdle (outside of the GDPR concerns) is the ‘people barrier’. Specifically, convincing employees that the technology used is beneficial rather than intrusive. Here, traditional change management techniques can play a crucial role in bringing people together around issues and involving them in how solutions are deployed. Showcasing the benefits of behavioural change for example through reduced incidents and injury is powerfully impactful.


It is also critical that innovation matches up with the demands of customers and is effectively adopted to realise any benefits. Again, no different to any other organisation initiative. However, IoT and the use of real-time data is more than simply developing the technology required to meet the goals and expectations.


“With new technology and capabilities that can be deployed so quickly – this landscape is changing every three to six months in what we’re able to do – there’s a level of training that makes sure our customers stay up to date with the capabilities. We cannot underestimate the hands-on and virtual training that needs to be provided because the technology is maturing so fast.”


Alice deBiasio, Vice President and General Manager of Sensitech and Lead Digital Solutions with Carrier Refrigeration


With the evolution of IoT, we are getting much broader access to a range of data sources from everywhere. What are the biggest challenges in leveraging this data to produce solutions that make sense and separating out the non-value add?


Whilst there is a clear need to simplify and prioritise high value data, the reality is that what initially appears to be insignificant could be valuable with the help of predictive modelling.


This can come down to the building of intelligent models designed to better sift through the data, though it must culminate in the better understanding of customer challenges and how to seamlessly address them.


Given that this is difficult to clearly define, it’s imperative then, that the insurance industry sets the tone.



“The insurance industry needs to start making demands on what it needs for the future rather than all these sets of data being pushed on it by the tech industry,”





Dan Fiehn (Chief Operating Officer at Incited)


There is a need for industry standards to underline the gathering and utilisation of data and we need these standards sooner rather than later.


The use and adoption of IoT sensors, the gathering of real-time data and its use and interpretation is slowly gaining momentum There are many lessons to be learned from those that have started to tread this path and are already gaining the significant benefits across operations, risk and ESG. These are real and tangible solutions to lower the total cost of risk, respond to ESG targets and overall improve efficiency. Why is the insurance industry so slow in adoption and given that their client base is out-pacing and out-innovating them?



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