Welcome to the Location Data Blog!

Good quality data is crucial for the operational efficiency of location-based businesses. Location-based analyses used in critical decision-making processes rely on the quality and accuracy of data powering them.

However, location data supply chains are characterized by a lack of transparency and authenticity, because data vendors prioritize volume over quality. To ensure that our customers make the most of their data purchase, we have made significant strides in identifying and erasing fraudulent data within our datasets. One way we achieve this is by examining our own data sources and constantly monitoring activity within our mobility and POI data feeds. We use data visualisation and proprietary AI models to spot instances of low-quality or fraudulent data and remove them before reaching customers. 

A few weeks ago, we announced that Quadrant had collected 250,000 POIs through Geolancer. Today, we are excited to share that we have doubled the size of our database to over half a million POIs!  

The increasing demand for Geolancer's manually collected, recent, and attribute-rich data has encouraged our rapid expansion. Besides hitting the half-million mark, we are happy to report that three of the world's top 5 mapping companies are now leveraging Geolancer's industry-leading POI data.

Besides rapid growth, we also have other exciting news to share.

The ride-hailing industry is characterized by stiff competition and new businesses are entering the fray every year. By 2026, the market size for ridesharing services is expected to more than double compared to 2021. For companies to thrive in this fiercely competitive landscape, they must optimize their routing systems to expedite pick-ups, match demand, and deliver more rides per driver for maximum profitability.  

Ridesharing services need accurate and up-to-date POI data to function. However, the underlying maps that enable their operations are outdated, and changes in the physical world are not reflected nearly as quickly as needed. A lack of current and contextual information can cost rideshare companies in terms of lost driver productivity, rider churn, and overall operational inefficiency. 

In the insurance business, many decision-making processes hinge on location intelligence and real-world geospatial information. Point-of-Interest (POI) data in particular is used for building contextual profiles of neighborhoods and determining precise risk scores for commercial and residential properties - which enables the creation of better policies. 

Many insurance businesses use satellite and aerial imagery to guide risk assessment and underwriting. However, neither of these tools can offer the comprehensive insights that can be unlocked through ground level geospatial data.  

Releasing a new consumer-facing physical product is difficult. In today’s hyper-competitive landscape, only companies that execute a data-driven marketing strategy during a product’s pre-launch, launch, and post-launch phases can expect to hit or even exceed – their goals. In this article, we elaborate on how businesses can leverage mobile location and Point-of-Interest (POI) data to make marketing initiatives across these three critical timeframes more effective.