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Finding the RFID Signal in the Noise

Use insights based on Nate Silver's The Signal and the Noise to make better use of RFID data analytics to improve processes.
By Kurt Hozak and Olajumoke Awe
Jan 10, 2018

Realizing the full potential of RFID often requires going beyond using it simply as a means of fast data collection in otherwise unchanged processes (a "paving the cowpaths" approach). Higher return on investment is possible if RFID is used to facilitate enhanced or entirely new processes (see RFID Research Supports Real-World Experimentation).

In some cases, RFID directly integrates into how an activity is performed, but it's also possible to use data analytics with RFID to indirectly make operations and marketing improvements. Using insights gleaned from the data, predictions can be made about anything from customer and employee behavior to supply chain events—which, in turn, can lead to changed processes (see The Emerging Marketplace for RFID Data Analytics, or Finding a Needle in a Haystack and A Guide to RFID Analytics Software for Retail and CPG Companies).

It has been said that compared to data-collection alternatives, RFID's unique capabilities and low variable costs for each additional read create a "fire hose" of big data. While one might infer that better decisions will naturally follow from having more data, taking advantage of it is not always as easy as one might hope. The award-winning and best-selling book The Signal and the Noise, by Nate Silver, makes many points that are relevant to RFID, though he does not specifically discuss it as a data source. He observes that while the amount of information available to us is greater than ever, we are not very good at making predictions, and "we face danger whenever information growth outpaces our understanding of how to process it" (p. 7). Silver's insights can be applied to RFID to avoid prediction pitfalls and, in turn, improve processes.

As RFID infrastructure develops, information sharing across supply chains becomes more common, and data analytics software and data aggregators become more capable, companies will have many more variables and data points that can be incorporated into their analyses and subsequent predictions. While that sounds like a good thing, Silver observes that when people have more information, they are prone to cherry-pick data that fits their biases and desired outcomes. Arguments that sound convincing can be made for any side of most issues, especially to those predisposed to believing a particular point of view. This is not always done consciously, so organizations and analysts need to work hard to prevent and detect it.

First-hand experience can be used to help verify that correlations in the data are causally related and not just statistical coincidences that are ever more likely to be observed simply because of the huge and growing amount of data collected. An organizational culture that takes advantage of bottom-up engagement may help identify opportunities for which RFID data analytics can truly make a difference.

In his article Free the People, RFID Journal editor Mark Roberti suggests letting workers who can benefit from RFID play significant roles in driving how it is used. This is compatible with lean principles that suggest that frontline workers often have the best vantage point from which to suggest operational improvements (see Lean and Six Sigma Create Valuable Synergies for RFID Adopters).

The lowest-level workers may not have the perspective to see insights from RFID data spanning outside their immediate purview (e.g., across departments and supply chains), but the point is that data collection and analytics should be organically driven from those who are in a position to know. If data analytics can help find needles in haystacks, but there are a multitude of haystacks in the barn of big data, companies should take a bottom-up approach to identify which haystacks are most likely to have the needles.

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