The shift to data-driven business intelligence is nowadays one of the main characteristics of the on-going digital transformation of business enterprises. Companies leverage data from many different sources to improve the effectiveness of their business processes and to boost their managerial decision making. Most of the data that they analyze have a temporal dimension i.e., they evolve over time. However, there is also information and data that have a spatial dimension as well, such as sales and customer information that are tied to specific locations. The wide availability of such data gives rise to a new wave of business analytics that leverage geographical information about business assets towards extracting location-aware insights. This type of business analytics is conveniently called location analytics and enables a range of interesting applications. In principle, location analytics is all about adding geolocation information to assets and using this information to offer convenience, boost business efficiency and achieve economies of scale. Most location analytics applications are able to superimpose business assets and location information in a geolocation canvas like a map. Accordingly, they analyze this information to optimize business processes and to offer value-added services to their customers.
The idea of location-aware computing and location-based services is not new. It has been around since the advent of mobile headsets and wireless computing, which facilitated the acquisition of location information about people and things. Nevertheless, in recent years, the interest in location analytics is growing as a result of the proliferation of mobile devices and positioning technologies like GPS (Global Positioning System). Likewise, enterprises have access to ubiquitous and high bandwidth infrastructures, such as the 4G/LTE (Long Term Evolution) infrastructures and the emerging 5G networks. Furthermore, there is also IoT technology that can facilitate the capturing of accurate location information in indoor environments. This is the case for LPWAN (Low Power Wide Area Network) technologies (e.g., LoRaWAN and SigFox), RFID (Radio Frequency Identification), and Bluetooth beacons. Also, end-users are increasingly using location information in their applications e.g., they post location information as part of their social media posts. Also, companies can nowadays deploy location intelligence software, which implements location analytics functionalities and integrates them within business intelligence solutions.
In this context, business enterprises are provided with unprecedented opportunities for developing location analytics applications and for using them to improve their business results. In this direction, they need to understand how location can enhance the intelligence, agility, and speed of their business processes. For instance, using geolocation information it is possible to provide enhanced insights about the spatial distribution of datasets such as customers, purchases and service requests. In this case, location information adds a very useful feature for business analytics such as retail and sales analytics by means of machine learning and artificial intelligence algorithms. This location feature might play a role in clustering customers, sales orders, products, and other entities. It also helps extracting location-related trends such as behavioral patterns for customers that reside in a certain geographic location.
Location analytics add a very important contextual dimension to business data, namely the “where” dimension. The placement of business data in a real-world context requires the acquisition of contextual information across five complementary dimensions i.e. When, Why, Who, Where, What(?), which are sometimes characterized as W5 context. Location analytics applications ensure that the spatial dimension of the context is adequately taken into account. Another benefit of location analytics lies in their ability to produce actionable insights for specific areas or territories. Specifically, leveraging location analytics companies can make evidence-based decisions about how to operate in specific countries or regions.
As already outlined, location intelligence functionalities boost many different types of enterprise applications. Some of the most prominent examples are:
Overall location analytics opens up a wide array of innovation opportunities for modern business enterprises. Hence, companies must explore the best possible ways for deploying location intelligence as part of their wider business intelligence solutions. Fortunately, state of the art location software platforms facilitate the implementation of location intelligence in areas like sales, marketing, production, and supply chain management. It is therefore time enterprises start integrating location analytics in their enterprise applications such as the applications outlined earlier in this post.
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