Have you ever considered the amount of investment and effort companies put into the research, design, development, marketing, launch and anticipation around new products? Who does because it’s just expected. If a company is going to create a new product it will take a lot of investment in resources and time to get it right the first time. One of the biggest challenges when launching a new product is anticipation of how the product will do in a customer’s environment after it performed so well in a very controlled test lab or small test market.
Most companies do not plan for a significant support demand after a product launch which is why key resources will already be working on the next new product or new enhancements. Resources are usually reallocated to the next new product or version to meet customer demands for improvements. These key resources and product milestones are seriously affected when customer issues occur and the support team struggles to find the problem.
There have been many studies finding a bug through the SDLC and the costliest bugs are those that occur after product launch when operating in customer environment. Products are still launched and supported this way and that just seems ‘old school’. Today most products stay connected and there is really no reason these devices could not be used to give companies with technical details from the real operating environment to show when everything is good but most importantly when the first signs of failure. Privacy is always a concern and there are ways to support privacy and offer better diagnostics.
Over the course of my career I’ve been involved with a number of products that provided this type of ability. They watch end-point-devices, collect a consistent set of predetermined key diagnostics information and upload that data routinely using a small encrypted payload. The insight I am able to learn from this method is a game changer and can actually find issues before a customer even knows there’s an issue. This means we can find and resolve issues more efficiently than current methods, e.g., trying to reproduce an event, gather log files that show the event, create a fix and hope it actually fixes the issue.
The IoT will be even more challenging and end-point-device monitoring shows the real issue and this is especially important when there are so many devices and services responsible for delivering customer expectations. How do you figure out where the issue really is? I think there is an answer and it’s monitoring the real devices and learn from them.
If your company is in big data analytics or your company develops connected devices and maybe struggling with support issues, contact me. This is a movement I’m passionate about.