Welcome to the Location Data Blog!

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.  
 

Point-of-Interest (POI) data has become a key component in driving innovation, efficiency, and personalized user experiences across diverse industries. From enabling ride-sharing services to identify pick-up and drop-off locations and ensuring timely last-mile deliveries, to enriching real estate insights and supporting state-of-the-art AI models and Large Language Models (LLMs), POI data is reshaping how businesses operate.  

Point-of-Interest (POI) data is critical for executing operations in a host of industries. Even companies that do not directly rely on POI data utilise it for deriving actionable intelligence to improve performance.

The challenge most of these businesses have is that off-the-shelf and web-scraped POI datasets are riddled with outdated and inaccurate information. Most POI data providers offer one-size-fits all datasets and fail to provide data that is relevant to different types of use cases and applications.

We've put together a solution brief discussing these challenges and solving them using Geolancer, Quadrant's very own POI Data-as-a-Service platform.

Point-of-Interest (POI) data is instrumental in facilitating the operations of businesses such as food and last-mile delivery companies, those that develop mapping and navigation software, ridesharing companies, and many others. In other industries, such data is harnessed to derive insights to inform decision-making around site selection and supply chain optimisation.  

However, procuring good quality POI data is a significant challenge.