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Fraud, BYOD and Other Reasons to Add Location Data with Enterprise Apps

Collecting location data is not new by any means, but it’s becoming more relevant now that we have better mobile technology and the processing power of Big Data solutions. Maybe it’s time to dust off your location data and look at how you can put it to use with enterprise applications. Network World recently published […]

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Loraine Lawson
Loraine Lawson
Oct 3, 2012

Collecting location data is not new by any means, but it’s becoming more relevant now that we have better mobile technology and the processing power of Big Data solutions.

Maybe it’s time to dust off your location data and look at how you can put it to use with enterprise applications.

Network World recently published a piece on this very topic, written by David Allen, the CTO of Locaid Technologies, which offers enterprise solutions for capturing and managing location data.

It’s a good piece, although it’s only offering a small glimpse at the ways you can use location data. For example, it doesn’t mention RFID or other tagging devices that are commonly used in manufacturing and retail. Since Locaid does offer technology solutions for RFID, I think Allen’s trying to focus on broadest possible use cases.

So it makes sense that he focuses on phone technologies. Besides the obvious smartphone GPS receivers, Allen points out you can also install a GPS chip into phones and, one assumes, other mobile devices. Allen calls this “handset-based technologies,” and while it’s the easiest to deploy — with approximately 100 million GPS-enabled devices available in the U.S. — there are also disadvantages.

“For users, it has the distinct disadvantage of strenuous processing and battery power drain,” Allen writes. “… to access handset-based location, the device must have an app installed or preloaded that will obtain a GPS fix using the device’s onboard hardware. This limits the reach of applications using this location method.”

Another option is network-based location data, which works with or without cellular data connectivity, an app or a GPS receiver on the phone.

He goes on to discuss when you might use one versus the other. You can probably guess that handset-based technologies work best when dealing with customers, while network-based is easier to do with internal employees, asset tracking, fleet management, logistics and so on.

Still, there are times when you might use network-based location data for customers. Allen discusses how network-based data could be used for credit card fraud detection. In this situation, customers have a good reason to opt in, and once they do, the financial institution could trace card charges to ensure the phone is near the card when the charge is made. If it’s not even in the region, that would be a red flag for possible fraud.

You could also integrate this information with sales data and CRM for location-based marketing or multi-channel sales to ensure mobile customers can receive the same price in-store as online.

Other use cases for integrating location data with enterprise systems:

  • Analytics and measurement
  • Call centers
  • Identity protection
  • Usage-based insurance
  • Supporting BYOD (bring your own device)

On top of that, since I’ve been cover B2B technology more for B2B.com, I’ve learned how valuable location-based data is becoming in manufacturing and supply chains. Of course, it has always been important for logistics, but now it’s taking on a new significance as more manufacturers deal with new regulations, sustainability goals and demand-driven business strategies that require them to monitor every aspect of the supply chain, from sourcing all the way to the retail floor.

And how, exactly, do you integrate all this data into enterprise and cloud-based ERP and CRM systems?

It’s not as hard as you think. Of course, you can always buy a proprietary system — as Allen points out — but for Web services and cloud, you can simply use an API to integrate the data into enterprise applications.

He doesn’t talk about Big Data in this piece, since it’s not relevant to his use cases, but Big Data solutions are sometimes used to process location-based data, particularly when it comes to sensors that can generate large volumes of data about utilities, smart grids, wind turbines and other large, isolated pieces of equipment.

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