What is Internet of Things (IoT)
Before knowing about role of analytics in Internet of Things (IoT), it is necessary to know that IoT does not only include internet-connected, automated TVs or Ovens. Internet of Things can have network of vast array of applications ranging from agriculture to space exploration or from baby monitoring to logistics optimization for companies or even driverless cars!
Analytics in IoT
Having said that IoT systems have very less or no human involvement, it becomes very important to have a strong analytics structure at back to support it for sustained accuracy, else it can cause fatal and unprecedented blunders. This highlights the ever-increasing need of practice of analytics in IoT.
IoT Analytics involves:
– Capturing data events using protocols
– Storing this ‘Big’ IoT data
– Adding new events using push messages from IoT device or registering data directly
– Transferring data to Analytics Algorithm/ System
– Calculated metrics along with new raw data are again fed back to processing systems
as a part of analytics algorithm for IoT devices
– Processed output
Applications of IoT Analytics
The output can be used for final decision making actions by IoT device or next step of iterations for further complex data capture and data driven AI activities. This is the back-end analytics process for IoT explained in simplest terms. Involvement of various protocols and databases for each decision-making makes IoT analytics a multi-layered complex structure.
IoT thus opens new doors of innovation and sets new bar for efficiency. To harness this, companies need to have a centralized system for their big data capture, process and analytics. On that front, basically there lacks a common framework for modes of data processing across all IoT devices. IoT being in its infant stage, companies face barriers of ease of accessing it. This ultimately leads to lot of fragmentation of data from IoT. To overcome this, like traditional analytics, there is now a wave to differentiate IoT Analysis from mainstream companies and form a new dedicated specialized system just for IoT analysis. These companies can gather data from devices in real-time and provide more reliable, insightful and actionable results from well-established algorithms and their expertise.
Conclusion
Though privacy issues bring in skepticism, IoT Analytics firms need to establish themselves as reliable contributors in this decision making process through encrypted cloud-based processes.
It has been proven from examples of companies which centrally capture IoT data and utilize them further, that IoT analytics has helped them to reduce operation costs and improve efficiency manifold than expected. Thus to conclude, in this evolution of internet, IoT Analytics is the transformation required for companies to be fittest and survive in long-term!