With the explosion in usage of mobile devices and connected sensors, geography has become inextricably linked to modern technological processes and systems. Cellphones, smart building devices, industrial systems, and car navigation systems, not to mention the fleets of autonomous vehicles slowly joining us on the streets, are all connected online. And to continue functioning properly every day, they must have what is called location intelligence. A smartphone can’t give directions unless it has a spatial awareness of its own proximity to the destination, and this is just the most simplistic example.
The growing number of connected devices has come to be referred to as the Internet of Things (IoT). The IoT is not just a random grouping of internet-enabled gadgets; it is a rapidly growing network able to capture vast amounts of data with fixed and moving sensors. Big data processing of these data – whether in real time, or on data-at-rest – is the crucial component of extracting business value from modern analytics, in private or public services. Location plays an essential part of this. Spatial analytics technology gives us the ability to tie these massive amounts of information together by placing them within the critical context of where.
The subsequent spatial analysis can provide unique insights, revealing previously hidden patterns and relationships that drive stronger decision-making for businesses. Fed by spatial analytics and real-time data, location technology’s applications are broad, ranging from optimizing supply chain management to using real-time asset tracking for logistics to customer analytics in retail.
Meeting the Demands of Supply Chain
We tend to think of the Internet of Things as a network of sensors, but it can also as easily be the barcode on a clamshell package that holds produce. This creates visibility, not just of a shipment, but down to an individual stock keeping unit (SKU). IoT data in this respect can give a company fine-grain tracking and understanding of its assets, including perishable goods.
Packaged berries are enjoyed every day by millions of people. It is taken for granted that the strawberries and raspberries are fresh and of high-quality once they reach the supermarket in their familiar plastic containers. But the process of ensuring that only the best berries make it from the field to the grocer in top condition involves multiple locations, often spanning several different countries. Because products need to go from a farm all the way to a retail location in a limited period of time, agricultural companies must track information about their product as it moves from the field to a processing center, a distribution center, and then a retail location.
This information is used to understand not only where the product went in the supply chain and how long it took to get there, but also to analyze produce quality. For instance, this process enables one agricultural producer to understand why a particular batch of strawberries was superior. The company can see where that batch came from, right down to what part of the field. They can then look at how they treated that field differently so they can repeat this success in the future. The company is essentially performing analytics on the back end to help improve the product that they deliver to their customers. This application of IoT also pays dividends when it comes to reverse logistics. If a specific product needs to be recalled, a company that knows the product’s origins down to the SKU level can perform more selective recalls and avoid waste.
Using Big Data to Make Big Predictions
The public sector is also using IoT in many ways, but one of the most useful and crucial applications is in the field of disaster response. Most recently, the National Water Center at NOAA had created a ground-breaking big data analytics model over the months before Hurricane Harvey hit to predict streamflow in streams, rivers, and other channels across the entire continental US given rainfall across the entire network. As a result, for the first time ever, they could model and predict the flow of water through the whole cycle. When Harvey hit, various organizations used spatial analytics to combine that same macro model with live IoT data from stream gauges in Houston and – in real time – to compute these large amounts of data to predict the next day’s flood impact. With this analysis, relief organizations knew where to put shelters and who needed help being relocated before the floods arrived. In this respect the IoT helps make sense of big data, using location intelligence to perform complex predictive analytics in real time to solve problems and save lives.
Insurance companies, too, need to understand very similar things as the government does in disaster areas. Data such as the location of the storm’s impacts, flood occurrence, employee location, and exposure to risk in given areas, is all connected by the question of where.
Insurance companies need to make sure they are getting to where the people who need the most help are, as soon as possible. These companies are collecting and analyzing all this data from weather services and risk management applications. But they are also collecting valuable information from IoT sources such as smartphones and stream gauges. In this respect, people are sensors, too; insurance adjusters (and relief workers) are collecting data such as spatially linked observations and photographs of conditions in the field, and providing those in real time to create a comprehensive and immediate view of the situation. Adjusters equipped with this information and an analysis of damaged areas know where they need to focus their attention. Insurance companies can tap the same information to calculate their exposure during and after an event. In the case of Harvey, the public IoT sources were stream gauges, flow networks, the modeling around stream rise, and others. The private IoT sources included the insurance companies’ client lists, the information coming in from phone calls, and their own assessments – even footage from drones. This goes to show the extent to which location-aware IoT data has proliferated. People, organizations, and businesses are creating and sharing information that can be leveraged to provide the right services, the right decisions, and the right value.
The Value of Real-Time Location Intelligence
The agricultural producer using IoT and data analytics is not just putting better products in stores. They are making their entire supply chain process more efficient, and changing the way we think about how these businesses should operate. Each box of produce is scanned at every stop of the journey from field to store. This allows the company to analyze the distribution process and remove roadblocks. Because produce is perishable, time truly is money, so being able to have location-based awareness adds directly to their bottom line. Trucking and logistics companies are also using IoT to track packages and make sure deliveries reach customers on time as well by using tracking data to save gas, reduce mileage, and ensure that their drivers don’t exceed their allotted hours for the day. And in the realm of market research, retail companies are analyzing patterns of life through data that’s collected from the apps people use on their mobile devices. This allows companies to understand not just purchasing behaviors, but travel patterns and shopping location choices as well. Location intelligence helps these retailers decide where to place their stores, where to advertise, and how to market to potential customers more effectively. That, in turn, increases in-store traffic and improves sales. And, governments as well as other emergency response organizations are using the power of spatial analytics to predict scenarios, so they can respond more effectively during a crisis when time is imperative.
IoT-based data is enabling all these outcomes and jumpstarting digital transformation for organizations around the world. But location is the key component that pulls all this data together, grounding all information, predictions, actions, and decisions in the common language of geography.