Data at Work

The world of data and its many applications. This blog will help you learn how visionary companies are utilising external data to enhance business operations.

How Quadrant Assesses Location Data Feeds

Businesses that want to leverage location data must procure high-quality datasets because erroneous data will result in false insights, and therefore, poor decision making. However, not all market participants are transparent about their data practices. In this article, we share background information about how the team at Quadrant analyses the quality of location data we provide our buyers – some of the steps we take to ensure it is of the highest quality possible for their particular use cases. 

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How Quadrant satisfies the highest standards for handling user data

From the onset, Quadrant’s corporate ethos has been predicated on the principle of transparency. As proponents of the laws and principles enshrined in data-privacy regulations, we ethically procure and process data to make sure we are operating within the boundaries established by these regulations. 

Early on, our Consent Management Platform (QCMP) was registered as an IAB TCF-compliant CMP – which made us a valuable partner for publishers who wanted to monetize their apps by supplying consented and anonymized user data. 

Recently, we became a IAB TCF’s v2.0 registered vendor - an accreditation wider than just QCMP, assuring our customers that we collect, store, and process data in accordance with the legal requirements of the GDPR. 

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Performing Extrapolation on Location Data to Derive Relevant Insights

Location data is collected from multiple sources of varying quality GPS signals from mobile devices, beacons, and WIFI connections, the notorious Bidstream, and more. In most cases, even genuine location data cannot represent the entire population of the region. This discrepancy can be attributed to smartphone penetration in the country, app-specific demographic variations, hardware inconsistencies, and sources of location data.

To perform meaningful analysis that accounts for mobility patterns and other trends in a larger region, data scientists use projection models to make an accurate estimation of a region’s population and normalise data counts to fit the use case. This is called data extrapolation.

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How Quadrant Evaluates Geolocation Data

In the spirit of transparency, we are going to share some background info on how we at Quadrant analyse the quality of location data we provide to our buyers – some of the steps we take to ensure it is of the highest quality possible for their particular use case.

 

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Introduction to Quadrant's Data Quality Dashboard

For data professionals, choosing a location data feed that is fit for purpose for your project can be challenging. The problem is that it’s not always clear what the quality of a given data feed is, but with Quadrant’s Data Quality Dashboard we’re changing that. 

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