Research suggests that by 2025, the Internet of Things (IoT) will have a potential economic impact of up to US$11 trillion per year driven by improved system interoperability and data use optimisation — among other factors.
With so much to gain financially, GIS technology suppliers — like Esri — are helping customers design, implement and operate complex systems that can collect data from the physical world, analyse it based on location and deliver actionable insights in a timely manner.
But the collection of data is only half the story, because while system integration and interoperability play a significant role in tapping into the potential economic impact of IoT, making use of the vast amounts of data being collected is equally important.
The Internet of Things (IoT) has completely changed how we architect modern enterprise geospatial systems.
Currently, IoT sensors are collecting massive amounts of data but, for example, an offshore oil rig fitted with more than 30,000 sensors makes use of only a small percentage of the data generated — and mostly for real-time monitoring or anomaly detection purposes only.
Alternatively, an optimised data utilisation strategy would see the deployment of more sophisticated IoT applications using location-based performance data to predict maintenance needs or to analyse workflows to optimise operating efficiency.
Not only can sensor data help monitor physical assets and detect faults in real-time; but adding a location element gives insight into the environmental or physical factors affecting them and allows decision makers to improve asset performance, extend asset life or even redesign the assets to do more.
The ability to electronically monitor and manage objects in the physical world supports data-driven decision making and opens up the potential to optimise systems and processes, save time and costs, as well as improve the quality of operations.
Here are some examples of how various industries can leverage IoT with their GIS platforms to improve their workflow efficiency and decision-making capabilities.
Insurance | Situation: Natural disaster
When natural disaster strikes, it’s important to understand where the damage has occurred and what it has affected. Analysing IoT data such as real-time weather and social media stream services using in the context of location allows insurers to map out where their clients are in relation to the damage and make smarter, more informed decisions about how to respond.
By combining IoT with a well-architected geospatial system, insurers can detect fraud, automatically process claims and drive customer satisfaction or improve customer retention.
Local Government | Situation: Smart cities
Smart cities are data-driven and by nature, rely on collaboration and the sharing of real-time awareness. Collecting data from thousands of IoT sensors and analysing it using an enterprise geospatial system creates a visualisation of the data on a map for immediate actionable insights.
These insights can be used track the delivery of city services and highlight areas where local council services need improvement. Location-based analysis of IoT data has also been used by local government to develop smarter parks, improve safety and drive innovation.
Federal & State Government: Defence, Policing and Emergency Services |
Situation: Terrorist attack
In the event of a terror attack in a public venue it’s critical for first responders to have a visual understanding of the situation to plan their initial response.
Location-based analysis of data from stream services — such as social media, vehicles and weather — provides real-time situational awareness that can be fed into crisis coordination centres for planning for an initial response.
For this, police and emergency service providers need to integrate IoT with geospatial systems which can provide notifications, analytics and a common operating picture to all agencies, allowing first responders to collaborate and save lives.
These are just a few examples of how IoT can be integrated with an enterprise GIS to create a whole new understanding of data and support a better-informed decision-making process.
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