Welcome to the Location Data Blog!

In the ever-evolving realm of digital marketing, our recent webinar, "Unlocking the Value of Location Data: From Insights to Impact," presented by Quadrant and Onspot Data, illuminated the transformative power of location analytics in advertising. This event highlighted how leveraging large volumes of quality location data, as opposed to relying on smaller data sets and predictive modeling, can drastically enhance marketing strategies across industries. Here, we explore the key takeaways from the webinar.  
 

Artificial Intelligence (AI) is rapidly transforming industries, from healthcare to finance, signaling a world where AI isn't just an asset, but a necessity. Yet, the backbone of this transformation —high-quality training data — is facing its own challenges. Contextually rich and representative datasets are vital; without them, even sophisticated AI can perpetuate biases, reducing effectiveness and raising ethical concerns. While broad-spectrum models like GPT-4 absorb varied data, specialized ones crave niche, context-intensive datasets. Unfortunately, many data collection methods miss the mark, leaving gaps in representation.  
 
In our latest solution brief, we dive into these challenges and introduce Quadrant’s Geolancer—a platform designed to revolutionize data collection by offering comprehensive, diverse, and high-quality data.  


 

In the modern world, Artificial Intelligence (AI) is being leveraged across various industries to tackle issues as diverse as inventory management in retail and route optimization in navigation. Due to its immense potential, AI is increasingly being used in pertinent areas such as finance, marketing, and human resources – which raises the question: will the use of AI in these (and other fields) remedy or amplify problems that lend themselves to flawed decision-making? This article will delve into the matter of ‘fairness’ in AI systems, elaborate on real-world instances of AI-based discrimination, discuss existing approaches towards mitigating AI bias, and more.  
 

In today’s rapidly evolving world, Point-of-Interest (POI) data has become the lifeblood for various sectors including retail, logistics, and travel. Whether you are a data scientist creating predictive models or an operations manager overseeing a distribution network, the quality of your POI data can make a world of difference. That’s why we took it upon ourselves to put our POI datasets to the test against two of the world’s largest mapping providers, especially focusing on the dynamic markets in Asia. What we found not only reassures our faith in our data quality but also reveals glaring gaps in some of the most trusted mapping databases today.

In an era where data is at the heart of decision-making, the property insurance industry is no exception. Assessing risk, underwriting policies, and even mitigating adjacent property risks all lean heavily on accurate and contextually relevant geospatial data. In this article, we delve into how Geolancer's (Quadrant's POI-as-a-Service) accurate, up-to-date, and customizable datasets are revolutionizing property insurance at every turn – using real-world examples from multiple successful projects.