Internet of Things (IOT) Analytics

Considering the exponentially increasing development of technology and computational power of Artificial Intelligence, Internet of Things (IoT) is not as far as it seems now. With saturation being achieved in most of the traditional technologies, IoT as compared is much unexplored and a promising domain. Before knowing about role of analytics in IoT, it is necessary to know that it does not only include automated TVs or Ovens. Internet of Things can have vast array of applications ranging from agriculture to space exploration or from baby monitoring to logistics optimization for companies or even driverless cars!

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

?This 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. 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.

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. 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.

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