Connecting your things, be they heavy metal or your fridge, to the internet is one thing, but what about unlocking the value of their location as well? Combining intelligence between people, places and things to understand and minimise risk.
The concept of the “internet of things” (IOT) is not a new one. It first started to gain some popularity in the Northern Hemisphere in the summer of 2010. The initial craze was when information leaked that Google's StreetView service had not only made 360-degree pictures, but had also stored tons of data of people's WiFi networks.
The internet was on fire with speculation as to whether this was the start of a Google strategy to not only index the internet but also index the physical world. Later that same year, the Chinese government announced it was going to make IOT one of its strategic priorities in its five-year plan.
No self-respecting tech commentator would be able to look themselves in the mirror if they didn’t mention the Gartner Hype Cycle for emerging technologies (which incidentally was invented in 2011). It included IOT as a new emerging phenomenon on its list.
Now 10 years on from those early IOT beginnings, and the understanding of the relationship between people, places and things is a growing, innovative market – a market that, says analyst group Research and Markets, will be worth $71.6 billion by 2025 and is growing at a CAGR of over 34%.
The firm refers to it as the ‘location of things’ and defines it as “an emerging sub-category of the IOT concept that enables connected devices to monitor and communicate their geographic location. Enabled by IOT sensors and location technologies embedded into various connected devices allows organisations and service providers to collect a variety of data over the network.”
Understanding the ‘where’
IOT provides us with the ‘standard’ sensor data that we typically collect about our things: temperature, status, fuel levels, critical errors, current state, last action, next action, software version, operating system, etc.
LOT is unlocking the ‘where’. Where is the device in time and space? What’s its altitude? What’s its speed and direction?
The location data now gives us the opportunity to lock these sensor data points to a specific space and time. Now the relation between these data points becomes relevant as well. The distance between sensors can point to trends, patterns and risks that have been previously invisible, as the location of the sensors was somewhat obscured in spreadsheets or business intelligence that could not expose its physical location.
Effectively, we are talking about a move from just sensing, to doing, and this becomes incredibly powerful in any business.
Effectively, we are talking about a move from just sensing, to doing, and this becomes incredibly powerful in any business. By understanding your where, it provides context for action at the edge. This edge of ‘where’, therefore, can unlock value in a multitude of use cases.
The where of retail
In retail, LOT allows a level of personalisation that is arguably the Holy Grail of the retail industry.
‘Where’ is not something new to the retail industry; since the inception of retail, stores were strategically placed to coincide with where demand is high. Even more now, retailers can:
- Enhance their inventory management by stocking the products most popular in a particular location, or seasonal based on weather patterns specific to the area.
- They can avoid the risk of “cannibalisation”, in which their own stores remove the business from other nearby stores.
- Analyse the movement patterns within the store to place relevant products in the high traffic areas.
- Analyse the drive time to their physical locations and ensure the correct marketing mix that has an influence on traffic to their stores.
The where of insurance
‘Where’ is not just confined to the data that machine sensors can pick up. The exponential value is in understanding how seemingly disparate datasets come together when visualised.
Using location data of potentially insured properties and overlaying the relevant weather, satellite, topographical and floodplain data over that allows insurers and underwriters to benefit from more accurate risk modelling based on a far more granular understanding of geographic location.
Add to that the recent South African riots, where insurers could get a better understanding of their risk based on the propagation of the riots and could forewarn and prepare their clients with an early warning system if the riots were seen to be moving or gaining a groundswell of support in their area.
Again, this is achieved through the combination and tracking of a disparate set of data that all relates back to the location of that dataset being the single most relevant unifying factor.
As more organisations begin to truly grasp the value of weaving together different data points to create insight, the location of things will exponentially transform interactions between consumers and businesses.
Generating a greater understanding of the relationships that exist between people, places and things adds an invaluable layer of insight to organisations.
Armed with this location-aware insight, businesses are primed to boost engagements with their customers, making them extremely personalised and relevant. Through this, they can improve their strategic planning and operations.
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