The Importance of Location Data
People are beginning to understand the value of spatial data. This blog post illustrates how various stakeholders use spatial data in order to gain insights and make better decisions.
Data science
In the last few years, a new field has emerged: “data science”. Its practitioners are occupied with extracting insights from large data volumes. Now that data can be produced quickly for at low cost, there’s no lack of data volumes waiting to be analyzed by data gurus.
Before raw data can be turned into information, it needs to be processed. Here, data scientists refer to “data cleaning”, a process where “dirty” data is “cleaned” so that it is fit for use. This process can be time-consuming as data sets can be quite large and may require manual intervention before they can be analyzed through an automated process. Longtime users of spatial data claim they have been doing data cleaning for years and that the problems in data science resemble their own spatial data issues.
Business Intelligence
Business intelligence is when businesses use data science tools to gain insights into sales and customers. Even before the value of location data was fully realised, businesses were interested in the location of their clients and their behavior.
Now businesses use business intelligence tools in order to drive sales or get highly detailed info about customer behavior. For example, shops collect consumer sales data that is used to predict future sales very accurately and enables the comparison of sales in different markets or areas. These often form the basis for location-specific sales campaigns and marketing efforts. Also, location data makes it possible to define the catchment area of a physical shop or show where a certain financial product or service is used.
Internet of Things (IoT) and Artificial Intelligence (AI)
The revolution of intelligent electronic devices called IoT has seen a great uptake in the last few years. Consumers are busy making their houses intelligent, whereas self-driving cars know how to navigate traffic without accidents through smart cities that consist of interconnected networks of devices that manage themselves. The next step in this process is adding artificial intelligence, so that devices are aware of itself and its surroundings and can even learn from “mistakes” to become smarter so that they no longer need to be operated by humans, such as self-driving cars or autonomous drones.
Any autonomous car requires a map of its surroundings as a point of orientation. The mapping industry is busy developing real-time mapping systems that enable self-driving cars to “read” their environment and adapt to it almost instantly. The same is happening in the drone industry so that drones can locate themselves and objects around them when a pilot is no longer able to see a drone and operate it. This makes it possible to operate drones over long distances while avoiding obstacles. The technology that is developed to make drones, cars and devices location-aware is called SLAM (Simultaneous Location and Mapping), where different positioning systems can be applied at the same time. These help a drone create a 3D map of its environment, that is underpinned by location data.
The integration of location data
Drones, phones, cars, you name it. Soon most devices will become reliant on some form of real-time location data. Whether to facilitate maintenance, improve safety, or to heighten the user experience. location data is becoming increasingly more important.
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