We define location intelligence as the ability to analyze location data for enhanced and actionable business insights.
Geography has historically been seen as a specialist data set - confined to the GIS specialists for very specific applications.
Now, the Internet of Things (IoT) and devices such as mobile phones are providing valuable location data that is being applied across a range of industries and for a variety of purposes.
Common applications for location intelligence
- Increasingly, smart retailers are recognising the correlation between their customers' movements and when and where they shop. Analytics can inform store locations, drive personalised marketing strategies, and even inform product strategies per store.
- Risk management is another common use case. Insurance companies can reduce underwriting bills, for example, by assessing the insured location to other risk indicators and demographics.
- Infrastructure planning. Telecommunications operators are rolling out new 5G or fibre networks, for example, and need to understand where to prioritise investment in order to maximise returns. Similarly, other services providers, such as municipalities or utility providers are leveraging location with related data to inform decisions on the delivery of services.
- Finally, logistics and supply chain companies can reduce delivery costs by building a better understanding of route planning through location and traffic analytics.
In each case, location intelligence is providing the link between the companies products or services, and the people that consume these.
Considerations for selecting an enterprise solution
Rolling location intelligence out into the enterprise is not easy.
One problem is access. Companies are democratising analytics and must plan to include geography in this process. The challenge is to move from being "map-centric" to being "data-centric" by adding geography data into commonly used analytics data sets.
Another common challenge is data hygiene. A recent survey shows that the main challenges of GIS include data accuracy and effective data management. This problem is exacerbated for multinational firms, that must consolidate or source location datasets for multiple geographies, but is a real problem for many.
Last, but certainly not least, large organisations need reliable, scalable and secure solutions that can connect to enterprise systems and enrich enterprise data sets without impacting operational performance, and without compromising privacy.
4 Questions to ask yourself:
Data-driven executives should be asking themselves the following three questions:
- Where does location intelligence fit within your into your data strategy?
- How does location enable your customer journey?
- How do you ensure the integrity of the data you are considering?
- How can you integrate location into your systems and processes?