What’s become of IoT
Have you ever noticed when something you appreciate gets popular, it loses some of the qualities that originally drew you to it? Think food or a style of music. I see this happening with the Internet of Things. And it’s not a bad thing.
A purist would say that commercialization dumbs things down and glosses over details to appeal to a wider market. In the case of IoT, commercialization is having quite the opposite effect. It’s moved the emphasis beyond technology to give a tangible and outcome-focused purpose to the abstraction of IoT.
You don’t need to speak the language of sensors and data — or things — to benefit from them. What are these things anyway? Aren’t we connecting everything and everyone?
So, don’t focus too much on those things. At the enterprise level, the technology is there. The biggest gap is with the business case.
Cisco says that 75 percent of IoT projects fail. Further, when describing the projects that didn’t fail, 35 percent of IT executives considered their projects a complete success. Only 15 percent of business executives agreed.
Key then is the identification of the business issue you want to solve. What’s valuable to your enterprise that IoT can bring? What’s your dream scenario or outcome?
What’s your issue?
Predictive maintenance is by far the most common IoT project I’ve seen because it solves so many issues.
For example, let’s say you’re at a theme park. Near the front of the line for your (or your child’s) favorite attraction, the ride shuts down. If you’re lucky, it may be back up after a few minutes. Do you risk waiting even longer? One sure thing, you’re not going to buy the souvenir photo of your thrilling experience as it would also remind you of the bad experience.
From the park’s perspective, the unexpected downtime drags with it customer satisfaction, revenue from souvenir sales and even customer advocacy. But what’s the long-term impact of mounting negative customer experience? How can the park bring all this back on track?
It starts with a well-defined business case. Predictive maintenance sets in motion a solution to optimize customer experience and drive greater revenue for the park.
The technology is ready
One park operator solved this issue by capturing data from the 700+ existing sensors on the rides and applying analytics and machine learning. Now, ride failures are predicted with 2-7 days’ notice for maintenance to be performed after hours. The rides have not broken down during operating hours since the implementation.
Success begets more success. Quick wins, such as the above example, embolden managers to look for more opportunities to improve their operations. They often turn to more detailed projects like fine-tuning processes or products.
What do you want to accomplish? What’s valuable to your enterprise? Focus on that first. The technology is ready.