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Managing Big (RFID) Data

Retailers tracking unique items via radio frequency identification are concerned about how to take advantage of all the data they collect. A recent MIT-sponsored event began to address this issue.
By Stephen Miles
How might companies begin to analyze Big Data in omnichannel retail and mobile retail commerce settings, despite the challenge of processing huge volumes of information in real time? As was demonstrated at the Auto-ID Labs Big Data Conference, companies utilizing GS1 structured identifiers for product labeling may have the opportunity to relate data captured when a customer scans a bar code using a smartphone with legacy applications employing those same identifiers. During the retail panel at the Auto-ID Labs Big Data conference, Venk Reddy, Walgreens' senior director for connected health, described the success of his company's mobile application, whereby customers scan their prescriptions' bar codes to order refills—a system that now accounts for 40 percent of all refills.

A second example provided by Abhi Dhar, Walgreens' chief technology officer for e-commerce, speaks to the opportunity of opening application programming interfaces (API's) to legacy enterprise systems. By creating a set of APIs to its photo-printing store systems for independent software developers, the company now supports more than 17,000 applications, providing the option to print an image at a Walgreens store nearby.

These two examples from Walgreens' e-commerce business show how a company, using off-the-shelf smartphone data-acquisition capabilities (bar-code or RFID scanning software) in conjunction with a customer-loyalty program and APIs to a shared infrastructure (photo processing, in this case), can link information from the Big Data world to enterprise applications using GS1 identifiers. However, as was pointed out repeatedly during the course of the conference, provisioning and managing these services, which reside in the cloud, remains a challenge.

During his summary remarks from the conference, Sanjay Sarma, a cofounder of the Auto-ID Center and the research director of the Auto-ID Labs, proposed to expand this approach to actually building models of things we are familiar with in the cloud. A "Cloud of Things" concept builds on several ongoing projects at the Labs to connect objects and their operations, such as vehicles and buildings, to the cloud. This initiative is open to companies, nonprofits and individuals interested in using Big Data resources to promote the development, adoption and commercial success of a Cloud of Things.

A Cloud of Things approach is important, because neither the Internet of Things nor conventional machine-to-machine (M2M) approaches will likely be able scale across multiple domains to represent networked systems with any communality. By building models of legacy systems infrastructure, along with APIs that can be accessed via the cloud, we can ease the task of moving information up and down the demand chain. This is likely to be more efficient and scalable than point-to-point linkages between intermittently connected systems. Businesses interested in prototyping such models—including smart-grid, smart-transportation and smart-manufacturing networks—are invited to join the Auto-ID Labs Cloud of Things initiative. To learn more, visit the Auto-ID Labs at MIT Web site.

A complete agenda and presentations can be downloaded from the conference Web site. Stephen Miles can be reached directly at s_miles@mit.edu.

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