The rate at which “things” are Internet-connected is growing, coupled with the availability of a low-cost and ready-made storage and computational abilities and the chance of generating the actual consumer insights from data at their point of origination has meant a strong case for analytics.
There is a need to understand the major differences that exist between the Internet of Things (IoT) analytics and traditional analytics. In traditional analytics, data is centralized before being analyzed, whereas, in IoT analytics, analysis of data is done at the point of origination (i.e at the device level) and at the centralized level which depends on the type of analytics and application.
There is now the ability to monetize insights at all levels both internally and externally in the ecosystem hierarchy. This may mean an opportunity to save costs and generate extra revenue. In order to maximize IoT’s potential, an understanding of the reasons for using IoT analytics and other infrastructure activities is essential. Also, there is a need to identify the information needs before the process of converting proliferating data from the raw state to a refined state where is gives meaningful insight and foresight.
In this case study, we’ll review how a combination of IoT, Cloud, Big Data, and Analytics helps Hydroponics Farming.
The IoT is an intelligent-based network. Its network is interconnected and instrumented with specific identities which use the embedded processing and communication technologies/capabilities. It has the special ability to sense, communicate and interact with each other about their states and environment. The insights generated by such network can be analyzes and applied to provide for data-driven decisions.
The major difference between traditional analytics and IoT analytics is in their data processing mode. While the former can only analyze distributed data at a central location, while the latter has the ability to generate insights across (IP) networks. This offers a specialized ability to generate insight and provide a genuinely distributed decision-making strategy.
IoT Analysis is differentiated from the traditional analysis due to its computational ability of distributed “things” within the edge network, apply the use the insights/outputs of the analytics at the local level and ability to communicate/collaborate with each other. In IoT, bringing data to the central location is not required as this is eliminated by the intelligence and computational ability built inside the lowest levels of the network.
In our next post we’ll share how we used hybrid analytics for hydroponics