What do Google Home, Amazon Echo, Google Next, Amazon Echo Show, Vivint Smart Hub, Ecobee, Samsung SmartThings Home Monitoring Kit, and Wink Hub have in common? Like hundreds of other smart IoT (Internet of Things) devices with actuators and sensors, they all have the ability to collect, store, and transmit measurable usage data and time stamps. This form of peripheral analytics is quickly emerging to become a dominant source of truth for organizations looking to glean a deeper insight from individual interactions with their smart devices.
The collection of this form of psychographic data is of immense value to organizations, or at the very least, should be. After all, it’s through the psychographic data center that key stakeholders can better understand how users optimally fit their business model. More importantly this type of data helps organizations to discover how their business models should be optimized to fit consumer behavior and their needs through engagement.
Peripheral analytics goes well beyond the utilitarian functionality of edge analytics whereby continuous variable monitoring leading to real-time scaling and the automated triggering of on-demand actions/events occurrences. For instance, with edge computing capabilities, if a device with a temperature sensor in a cooler at a flower store detects a critical thermal increase that could cause damage to floral inventory within moments, the real-time measured data could be used to programmatically trigger a text alert to be instantiated on employees’ smart phones so that immediate action could be taken to resolve the issue.
With peripheral analytics, one could measure the types of alerts that are instantiated, the frequency of those alerts, a time-parted view of the frequency by alert type, or even the types of resolutions applied. On the flip side, peripheral analytics enables an evaluation of contextualized events or user-actions.
For instance, think of an organization that needs contextual insight on which of their streaming audio clips is most popular or most engaging, in which geographic region, at which times of day and from which smart home devices coupled to start-of-stream, mid-stream or end-of-stream falloff points. Such critical insights could enable stakeholders to identify monetizable pre, post, and interstitial marketing strategy opportunities, which topics have an influence on increased play requests, or which topics could be refined with nGram curation through a view of which key terms are most relevant and related.
Edge computing technology may be more specialized, however peripheral analytics is more contextualized. Both are becoming increasingly and significantly useful for organizations that are focused on building out their spatial presence across organizations, urban communities, and home smart devices.
Starting Your Data Analytics Career
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