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The Emerging Marketplace for RFID Data Analytics, or Finding a Needle in a Haystack
Solution vendors should plan for changes in the economics of reselling hardware. Those who find ways to incorporate data analysis into their offerings will enjoy higher margins and a competitive advantage.
According to Armstrong, that specialization in the analysis of retail data will create a third-party marketplace for data analysis. "We've teamed up with a company that is looking at that market," he says. "Our value is that we have massive amounts of data. We get value from their labor and skill [in applying specific analysis techniques]."
Cofacet is Senitron's analytics partner. McCoy, who earned a Ph.D. degree from the California Institute of Technology in computing and mathematical sciences, says: "Cofacet helps retailers understand how people are interacting with physical objects in the real world… such as what areas and what products are seeing activity." He adds that "activity is a leading indicator" of sales, while sales is a lagging indicator of past decisions. His service will help customers get ahead of sales.
Advanced mathematics is required to interpret the data, McCoy says—it is not simply a matter of counting reads. "The data tends to be very noisy, and fluctuates a lot during the day. How do you know when something has actually been picked up?" He employs statistical methods such as "change-point detection" to know when a change has actually occurred.
Machine learning and automation is part of the solution, McCoy says. For example, when helpful customers re-hang items on the wrong rack, the system can automatically notify employees where to look and what to look for. A learning system doesn't have to be told where that item belongs. It can infer the planogram from the tag data streaming in. Machine learning is an important element in the larger Internet of Things, of which RFID is a subset. University of Washington professor and MacArthur Fellow Shwetak Patel says that in the sensor systems he studies, "It was previously all a device play. The problem is, simple devices or sensors don't do much [by themselves]." Machine learning helps sensor systems (including RFID) establish what Patel calls a "baseline of the observables," which is necessary before changes can be detected.
McCoy's company is now developing algorithms or systems to take advantage of fixed infrastructure data, just ahead of demand. "The market doesn't exist yet, because there's little fixed infrastructure yet," he explains. "But Cofacet is building systems for what's coming. It's less than two years off."
Diana Hage, CEO of RFID Global Solution (RFIDGS), says other verticals will follow retail, though some will take longer to adopt due to data security concerns. RFIDGS' customers include government clients. "The majority of them are not comfortable with data going off-premises due to security and firewall issues," Hage says. RFIDGS has been successful getting its Visi-Trac real-time location system (RTLS) platform adopted broadly in government organizations, such as at Department of Homeland Security (DHS) offices, but regulations like the International Traffic in Arms Regulations (ITAR) restrict access to the data, so "It's all based on on-premises servers."
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