Man vs Machine: being behavioral in the Internet of Things

In contrary to the traditional web that has been a part of our lives for quite a few years, internet enabled appliances or devices, ie. Internet of Things (IoT),  has been drawing a lot of attention as a concept lately.

However, is it possible to leverage the same technology to get insights from the IoT in the same way we can from an online business? Well, yes and no.

When it comes to investigating data generated on the Web, we can categorize the types of things we collect by:

  1. Entities– these are things like users, visitors, sessions, pages, cookies, IPs, etc.
  2. Measures– KPIs that companies like to use in order to understand their online activity, such as monthly active users, daily active users, frequency, and more.
  3. Devices– whether it be desktop, mobile (iOS/ Android/ etc.), or other internet enabled devices.

The data generated from these three categories can also be enriched. This has become increasingly popular as mobile and device specific data, like how users might connect (ex: 3g/4g/Wifi/nfc/Bluetooth) can provide additional usage patterns that are important to understanding user behavior.

When it comes to analytics, there are some similarities and differences between the way we can track data from the traditional web and the IoT:

Data – in cases of periodical data or fixed format like a sensor sending a report every minute on the temperature and voltage, as opposed to web, where there can be long periods of time with no interactions.

Enrichment- is relevant for IoT and web. External data can clarify and add perspective to the whole picture. In IoT we can see that an air conditioning unit stopped working at an x temperature. In the traditional web, we can monitor ad spending and how it affects the funnel.

Session – The Sessionization process lets you detect how users/devices behave; each session stores endless information on either user or device trends or patterns. Important business questions can be answered through data collection in session mode and is applicable to both.
Ultimately, there are similarities in the way we can gain insights from typical human internet users and internet enabled devices. The traditional web has evolved enough to ask more sophisticated questions about how users behave, what they consume, or even how they move within a defined site. In IoT, however, we are still beginning to understand how data collection is managed and what insights we can gain from it. Overall, when it comes to behavioral analytics, the fact that a human or a machine produced the data will change what we view, but not how we handle the data.

 

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