In a recent blog post on Information Week (“IoT is about Analysis, Not Things”) Chris Taylor wrote: “The challenge for business is to shift the focus away from things. Yes, you need to invest in new hardware and software in an IoT deployment, but without excellent analysis, a host of sensors won’t give a competitive edge.”
IoT delivers data, and lots of it. Many of the acclaimed IoT applications do nothing more than collect terabytes of diagnostic data from equipment in the field. OEMs are collecting so much data they’re having trouble utilizing it; they’re literally swimming in an ocean of error codes. (Some OEMs complain that they’re processing less than 1% of the data they collect.) To benefit from the Internet of Things, manufacturers need applications that can evaluate all that data and deliver the most relevant bits in a useful way. It’s one thing to download data off a machine; it’s another to know what to do with it. Collecting more data has no value if customers, service technicians and engineers can’t make better decisions about how to operate, repair and design equipment.
For product support, one of the benefits of collecting diagnostic data is to recognize clues about a machine’s performance and failure characteristics. For example, product engineers can research various fault codes; for example, has the error code occurred before, and if so, where, when, why and how often? Pairing the right diagnostic tools with IoT helps OEMs and operators to discern data sequences that indicate a potential product or component failure; furthermore, when data is collected from more than one machine (i.e., a group or fleet of machines) it can reveal emerging failure trends that require escalation and corrective action.
With proper analysis, companies can glean insights from diagnostic data to increase equipment performance and improve product design. Similarly, diagnostic data can provide important clues that helps product support and service technicians make faster, more accurate decisions about equipment maintenance and repairs. However, as IOT starts to be viewed as a panacea for the challenges of product support, more and more sensors are being added to machines so that the amount of diagnostic data is expanding exponentially.
Some OEMs are discovering that much of the data collected is irrelevant to understanding equipment problems, and often all those error codes make it harder to correctly identify the cause of the defect. Manufacturers do need a complete view of the diagnostic data, so that engineers and technicians can discern what is most relevant for troubleshooting performance and reliability problems. Furthermore, IoT is an industry transformation that is here to stay. However, for IOT to be deemed a success it must deliver on its promises: better product performance, higher customer satisfaction, lower operating and support costs—for the equipment manufacturer and the operator.