For location analytics

For location analytics, only the best, most current data will do

For location analytics, only the best, most current data will do

In geospatial tech, data has always been the lost stepchild. Or, it’s the “rocket fuel” of geographic information systems (GIS). Take your pick. It depends on who you work for or what you need. The bottom line? For location analytics, only the best, most current data will do.

Today, data is the new bacon. It sizzles; it’s crispy; it’s essential to the complete buffet of geographic information systems (GIS). There are no eggs without bacon. Likewise, you can’t run GIS or advanced location analytics without data.

In today’s mobile society, there is more location-based data than ever before being collected and repackaged for sale. Twitter, Facebook, and other social media apps collect data from their users and offer it to advertisers. Anonymously, of course. But cell phones are constantly broadcasting location-based data – how else would you play PokemonGo? And, standard fare that includes population demographics are “meat and potatoes” for today’s hungry marketing managers.

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Yet, the challenge for many users is an inability to find this so-called smorgasbord of data. And if found, how to consume it. Pitney Bowes offers a data catalog replete with an abundance of data. It’s digestible since it has been divided into discrete categories: streets, points of interest, boundaries and demographics. To this menu has been added industry data bundles for retail, insurance, telecommunications and others. The coverage is global, the content rich and the file formats for use are extensive.

While GIS is familiar to those educated about location technology, others are new to location analytics – especially the millions of users of business intelligence (BI) solutions that are starting to include basic mapping.  In the BI domain, maps are the background against which analysts and line of business users display data and analytics. For example, heat maps showing the concentrations of customers, facilities or even IoT devices offer a unique proximity of spatial relationships not afforded by spreadsheets. Taking in data from in-house and external sources is essential to deriving the insights that impact business outcomes.

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