IoT devices don’t just appear and disappear on their own. They get ordered on the web or in a store; they go through a supply chain; get assembled, tested, shipped, received, and unboxed. The user has to charge them and recharge them, put them on a network, connect up a mobile app, commission them in a network, log into a web app, or access their data via an API. They access a subset of features, occasionally running into failures. On cold winter days, devices may run out of power early, or just fall offline for a few minutes intermittently. Eventually the devices fail and get replaced. Or else the user gets confused and fails to install it, or loses interest and stops using it.
With a traditional app, the sales funnel and usage statistics are well defined. But the equivalent for an IoT device is quite different, and a lot more data intensive. Adding data analytics to the stack is challenging for IoT companies, who are already spread thin between hardware, firmware, mobile, and web apps. I will discuss tools that make the latter less difficult and make enormous contributions to the IoT company’s survival, fitness, and success.
I’ll share my experiences and tools for collecting data throughout the entire IoT lifecycle–early prototypes and beta testing (Streetline), manufacturing (Streetline/Gener8), eCommerce, distribution, and RMA (Pebble), user behavior (TouchTunes), software loading (Light Blue Bean), app developer networks (Pebble), and user-defined analytics (Electric Imp, Tessel).
Caroline and her team at Streetline were a bunch of hardware and cloud weirdos building the Internet of Parking Spaces back where there was no hardware or IoT in SoMa, San Francisco — only seven years ago. She’s lived through many a thrill of victory and agony of defeat in the IoT landscape, and is eager to share her stories. She’s currently building the IoT practice at data analytics company Keen IO. She is a graduate of Massachusetts Institute of Technology.
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