Welcome to the Location Data Blog!

Geospatial data have the potential to uncover valuable insights about the physical world. It can be used by governments to save lives and influence public welfare. Businesses can use it to drive consumer acquisition by capturing the attention of the right people at the right time. Anonymized location data has a lot of value to offer without breaching people’s privacy.

High-quality Point-of-Interest (POI) data is the backbone of all location-based businesses and applications. Your customers rely on you to find day-to-day services such as transportation, food delivery, online shopping, and more. The speed, accuracy, and reliability in providing these services can be a determining factor in gaining and retaining customers.

But how do you ensure that the data powering your business are of good quality and up to date?

There are numerous data providers that claim to offer vast, accurate, and ideal POI datasets, but how do you correctly assess them? In this post, we will discuss a few factors to consider before buying POI data. Use them as a checklist for your purchase decision while selecting a data vendor!

A few weeks ago, we discussed the seven most important parameters for data buyers to consider before purchasing location data. One of those factors is the health and quality of data. Is the data complete, accurate, and usable? Does it need additional preparation? How many unique values does the dataset actually contain?

Quadrant, a global leader in location data and intelligence solutions, is delighted to announce its new Point-of-Interest (POI) Data-as-a-Service.

In today's increasingly complex digital economy, many modern applications and systems depend on POI data. From our engagements with customers across multiple industries, we have found that accurate POI data is a critical defining factor of customer satisfaction and operational efficiency.

Companies in the location data industry like to highlight the numerous uses and benefits of their data. However, the topic of how to select a data vendor remains largely undiscussed. As the old adage goes, ‘garbage in, garbage out’: low quality data leads to unsubstantiated, weak insights, and ultimately to wrong decisions. To ensure that the data you acquire helps you solve your business challenges, you need to evaluate data quality.