How Data Analytics Plays a Crucial Role in Industrial IoT?


IoT and data analytics generate greater insights and open a window for new opportunities in businesses when adopted

By Sanjeev Verma

Data analytics is the paramount activity for today’s Industrial Internet of Things (IIoT) adopters. It is the critical component for any industrial venture. Industry 4.0 has adopted data analytics as an integral part of its operational strategy thus enriching all industries from vehicles and manufacturing to warehouses and marketing.

Due to digital and smartphone technology, the amount of data available has tremendously increased, but the IIoT has taken this data deluge to new heights. The need to review and analyse both structured and unstructured data has brought the successful IoT deployments solution.

We look at five reasons why IIoT data analytics is essential:

1. Large Chunk of Data

The IIoT comprises a massive collection of sensors, thus increasing the amount of data. This large chunk of data needs to be managed appropriately to reduce complex challenges. While ‘dealing’ with a large amount of geographically scattered data, not only collecting the data from sensors plays an important role but analysing it and decoding it to useful information plays a more critical role as well.

Businesses need to invest more in a cutting-edge analytics solution to analyse and giving real-time insights.

One of the recent studies says, “By 2020, the world’s data will amount to 44 zettabytes approx. And 10 per cent of it would be coming from the IoT.” The statistics show how challenging it would be to collect and store this large chunk of data from many connected devices. Hence, not only accommodating processes, technology and tools also play a significant role.

2. A Form of Data

It is not the collection of the huge pile of data; it is the use of collected historical data to make future business decisions. And this is where data analytics plays a significant role. IoT delivers data in diverse formats and types which becomes a challenge for different businesses to analyse and have insight.

The traditional SQL tools could no more be a consistent source for analysing the real-time data. The smart data analytical tools should be capable enough to process the diversified data type and format.

The major challenge lies in the fact that the data from different sources is unstructured.

The data gets life only when it is being analysed and interpreted in a way that it can give an insight into the future. And then only we can realise a successful IIoT implementation.

3. A Life Saviour

Because the IIoT has not only embarked its footprint in one single industry like finance or retail, IoT analytics has widespread its images in healthcare too.

Smart wearables collect and send the data of the patient to doctors in real-time. Thus, saving the life of a patient. IIoT sensors collect the real-time data from the field to make the material handling in the pharmaceutical supply chain.

With data analytics, IoT devices not only tell about the real-time information of the patient but also explains the way to cure them.

4. A Catalyst in Business

Exponential growth in revenue and business value is the core reason for adopting new technologies. IoT and data analytics generate greater insights and open a window for new opportunities in businesses when adopted. A detailed insight thereby gives more and more futuristic business plans. IoT is a reliable technology thus effectively giving businesses a crystal-clear view of the granular processes, warehouse condition, fleet locations and provides, in fact, the real-time information of the employees. The IIoT has been such a technology that not only requires where and how data is being used but also if the intent and content is known then can lead to innovations, thus creating more growth for businesses.

5. Unlocking more Opportunities

IoT data analytics provides additional data which not only gives the real-time insight which is helpful in present but also explores more opportunities in the future. The predictive analysis feature lets you know about the problems such as equipment failure so you can plan for any adverse condition way before it occurs.

Reliability, efficiency, and productivity have been the three essential pillars of businesses for IoT data analytics.


Every organisation’s business requirement will be unique, depending on its desired business objectives and particular approach to grow. But an IIoT solution is incomplete if it does not incorporate complete data analytics feature.

IoT-driven innovations such as predictive maintenance is heavily dependent on several forms of analytics such as:

  •   Operational analytics
  •   Real-time complex event processing
  •   Lightweight analytics
  •   Detailed analytical models

Sanjeev Verma is the Chief Executive Officer (CEO) of Biz4Intellia.


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