1. Introduction: Unlocking the value of “where?”
The global digital age has had a profound impact on consumer access to maps, which are becoming an intrinsic part of everyday life. Many are familiar with web-based mapping applications like Google Maps, Yahoo Maps or Microsoft Windows Live Local, as well as 3D maps such as Google Earth or Microsoft Virtual Earth. Along with GPS (global positioning system) devices for leisure, in-car navigation and the emergence of mobile phones and handheld devices that offer GPS services, we are all being exposed to a wealth of location based information. The influx of these webbased mapping services and pervasive GPS data is bringing location to the masses and also stimulating the corporate appetite for exploiting location technology.
Location is relevant to all businesses – it is a common dimension of almost all business information and an important element for many business decisions. Where are my best performing stores? Where are my most profitable customers? Where are my competitors or suppliers? What is the potential revenue opportunity compared to investment costs necessary to enter a new market? Today, businesses are generating volumes of data, almost all of which have a geographic dimension, captured and processed through a myriad of business applications such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. This, combined with the growing use of Radio Frequency Identification (RFID) tagging and GPS technologies to track business assets and events in real time, means that understanding the value and impact of location on business performance has never been more important.
Harnessing business data to measure and analyse business performance has long been the mainstay of business intelligence applications. However, all too often, the location dimension of business data is overlooked, meaning its potential impact and influence on business operations is left unexposed. Understanding the relevance of location is fundamental when deciding on store locations, developing marketing campaigns, tracking and managing assets or designating sales or delivery boundaries. This is the foundation of location intelligence.
Through combining the geographic dimension of business data together with external geographic data such as road networks, place names and attributes, demographic information, or other geographically dispersed data such as hurricane impact areas, location intelligence produces a visualisation in the form of a map that can be explored, manipulated and analysed to unlock the value of “where”. Whether it is best performing stores or sales people, capacity of utility networks, customer and market characteristics, or high crime areas, all of this information can be placed on a map to understand how it relates to other spatial information such as competitors, consumer segmentation information, daytime population patterns or delivery boundaries. Businesses that are able to exploit the context of this location information can gain valuable insights into business performance – not only where they are today but where they should be in the future.
The aim of this document is to outline the opportunity of location intelligence for the 21st century enterprise and how it can enhance decision making and business insight through leveraging existing investments in business intelligence tools.
2. Definitions
Before discussing the evolution of location intelligence, it helps to define some terms that are commonly used with reference to analysing location enabled data.
- Geospatial: Geospatial data represents objects that can be referenced to a location – either a specific point or a relevant area on the Earth’s surface. Spatial data, in contrast, represents the location of objects relative to each other in space with no Earthly context. However, the terms are often used interchangeably. Geospatial data can be about either natural or constructed features; examples include data representing roads, rivers and property boundaries. Geospatial data can also be derived – the probability of flooding zones, most profitable sales territories per capita, or the relative efficacy of special business development zones. Geospatial data is usually stored as single or multiple pairs of coordinates (longitude, latitude and often height above sea level), enabling it to be visualised on a 2- or 3-dimensional map as points, lines or areas. This forms the basis of geospatial analysis which enables a user to visualise, analyse and manipulate geospatial data.
