Welcome to the Location Data Blog!

Having access to large repositories of data enables businesses to optimise operations in several ways. This includes personalised advertising, greater supply chain efficiency, and more satisfying customer experiences.

However, people have grown increasingly wary of trusting businesses and governments with their data. Several high-profile data privacy breaches at LinkedIn, Alibaba, and Yahoo (to name a few) collectively impacted billions of users.

Ethically managing data and making sure no Personally Identifiable Information (PII) makes it to big data sets is a challenge we take very seriously at Quadrant.

Transportation companies need location-based intelligence to gauge demand, support operations, adapt to changing public requirements, and ensure safety. Reliable and accurate mobile location data is key to studying mobility trends that can reveal actionable insights for the betterment of infrastructure and applications in supply chain, ride sharing, public transport, traffic management, and other use cases.

Companies also need location data to assess potential for usage in growing urban spaces to improve availability and return-on-investment. More recently, due to the Covid-19 pandemic, people’s movement patterns have changed drastically. The new normal with social distancing requires tweaking existing forms of transportation and related services. All these make mobile location datasets and corresponding mobility analysis more valuable than ever, both in the private and public sector.

The location data industry is using multiple sources of data with a varying degree of quality. This means suppliers often have a significant amount of overlapping records. While this does not impact quality or validity of the data, it poses a challenge for buyers who need unique values for their analyses.

Most off-the-shelf vendors leave this unaddressed as it makes their data volumes appear larger. However, when buyers prepare the data for analysis by removing overlaps, they often see a significant drop in overall volumes. 

Earlier this year, we launched the public beta test of our proprietary POI collection and verification app, Geolancer. Aimed at providing up-to-date and manually-verified POI datasets, Geolancer is already helping ride sharing and real estate companies enrich their platforms with a hyper-accurate POI data feed. In this article, we provide a peek behind the curtain and explain how we maintain the high quality of POI data in Geolancer.

The Covid-19 pandemic and following global lockdowns were devastating for the retail industry. Activities like shopping and leisure quickly fell as people’s focus shifted to dealing with a pandemic. Essential retail like grocery, drugstores, and homeware had to adapt quickly to accommodate the needs of the hour, while non-essential retail segments of fashion, sports, beauty, luxury, etc. went into complete disarray.