The Analytics of Things – Why Data Analysis is Good for IoT

by | Jul 2, 2020 | Blog | 0 comments

Internet of Things (IoT) strategies are being adopted by industries world over to improve their processes and deliver a unique customer experience. From pens, lanyards, smart tags in stores and switches to air conditioners, refrigerators and traffic lights, IoT is being deployed almost everywhere.

But implementing IoT at scale requires managing millions of connected devices to gather and transmit data that is at once generated and essential for IoT devices. Naturally then powerful data analytics solutions become necessary to help businesses understand what this data is telling them. It can be a daunting task for any organization to build a functional, scalable and secure IoT network and datacenter without an experienced technology partner with the relevant expertise.

To understand the scale of data generated by these devices, we need to only count the number of IoT endpoints or a number of devices with each one capturing different parameters. This number runs into billions worldwide. Imagine the final volume of the data that is generated by these devices including sensors, actuators, tags etc. In fact, according to the Grand View Research, the global IoT analytics market size is expected to reach USD 57.3 billion by 2025 at a CAGR of 29.7%.
These staggering numbers could mean a data nightmare and raise several questions like:

  • Should all the generated data be stored and how?
  • How can we derive meaning from the data generated?
  • Is it possible to handle the high velocity of the data stream?
  • How do we secure IoT data and prevent misuse?
  • How do we enable reliable data communication between sensors, from sensors to applications, or even from sensors to a cloud?

IoT Analytics is an emerging area of data science and data analytics that can provide answers to these questions. The full capability of IoT for business use lies in connecting IoT to the data generated within the IoT ecosystem. There are various types of analytics that can be applied to IoT:

  • Streaming Analytics: Analyzes huge in-motion data sets, i.e. real-time data streams, to detect urgent situations and immediate actions and help areas like financial transactions, air fleet tracking, traffic analysis etc.
  • Spatial Analytics: Analyzes geographic patterns to determine the spatial relationship between the physical objects and helps in fields like smart parking.
  • Time Series Analytics: Basis analysis on time-based data which is analyzed to reveal trends and patterns and can be applied to weather forecasting applications and health monitoring systems.
  • Prescriptive Analytics: Combines descriptive and predictive analysis to help organizations evaluate the best action that can be taken to troubleshoot or improve a particular situation.

But do note that IoT Analytics is still an evolving field and is still dealing with complexities related to streaming and real-time data. IoT data comes in huge volumes, is highly unstructured, and differs in terms of format (text, image or videos). Moreover, while the operational technology relates to the data collected from temperature sensors, pressure sensors, tablets, smart manufacturing devices/tools, etc., the information technology it needs to be set in context to the data collected from enterprise systems, legacy systems, ERP, CRM, and finance systems.

Moreover, the total amount of data being collected may be so large that it may not be possible to move it to a central location. All IoT devices need to be connected and inter-operate. However, this requirement also raises data security issues, based on the age-old principle that a chain is only as strong as its weakest link. In IoT’s context, if the security on a specific vendor’s outdoor sensor is weak, and the sensor is connected to other devices, the likelihood of ‘indirect’ critical impact is high. Attackers can compromise the sensor and modify its data or exploit the connection to other devices to cause damage. Similarly, devices can also malfunction and transmit false readings to the system and throw IoT analytics off course.

Despite these challenges, IoT-generated data must be able to give organizations a clear picture of their desired outcomes in order to ensure that the analytics technology used to gain insight from that data is aligned with business needs. The good news is that analytics technologies have rapidly become advanced, and it is now easier for even newly generated data to be available for analysis almost instantly. IoT can be processed within a matter of seconds and can be retained efficiently and cost-effectively for a longer duration.