The concept of web analytics is very well understood and heavily used, mostly powered by Google Analytics. How does it translate to the Internet of Things?
There is much less work done in IoT analytics, tools to understand data created by the IoT . Web analytics can provide detailed analysis of user behaviour, enabling to quickly spot and address interaction problems towards a targeted goal. This is in line with the Lean start up from Eric Reis.
What ‘user interaction’ means in web analytics? Page views and events and there related features such as time on site, bounce rate, content, etc. An event consist in four attributes: category, action, label and value. Data is push to the cloud through protocols such as the Universal Measurement Protocol (UMP). In contrast with the web, the IoT offer is very diverse, domain specific and ‘everyone’ build their own visualisation tools with libraries like D3.js.
Mateusz and colleagues developed Pheme, a tool they use to pipe IoT data into Google Analytics. They explore 4 use cases with raw data, processed data, user engagement data and multiple devices per user. They reported limits regarding the push of historical data (should not be older than 4hrs) and not in the future (e.g. prediction data).
Paul and colleagues raise the potential of real-time analysis in the context of a Social IoT (SIoT) . What does it mean to combine all these streams of data at the same time? What could be the potential application. The authors provide example with parking lots and home energy.
Acer and colleagues  leverage WiFi to search location and state of physical objects, thus generating uniform data IoT analytics from out of a wide spread wireless technology.