IoT Explained

12 March 2025

Reading Time: 4 mins

Harnessing the Power of IoT and Big Data Analytics: Transforming Connectivity into Insights

Eseye

IoT Hardware and Connectivity Specialists

As the number of IoT devices online approaches the tens of billions, it is increasingly clear that the real value of this vast network of things is in the data generated.

Evolutions in connectivity are driving new use cases in IoT, which in turn is generating more data at a faster rate than ever before. According to a study by Juniper Research, the global amount of data generated by roaming IoT connections will increase from 86 petabytes in 2022 to 1,100 petabytes by 2027; representing enough data to stream 165 million hours of 4K video. The focus now is on how enterprises and organizations make sense of all that data.

The intersection of IoT and big data analytics

Big data analytics is essential to making sense of the billions or trillions of historical and real-time data points generated by IoT devices. Modern big data tools include AWS, Google BigQuery, Apache, and Microsoft Power BI. Connectivity enhancements have enabled big data analytics platforms to take unstructured data generated by IoT devices and organize that information into digestible datasets that can be used to power various analytical applications.

What is big data analytics?

Big data is a term that has already been around for decades and primarily refers to data sets that are too large to be dealt with by traditional data-processing applications. As both compute power and the volume of data created have increased significantly over the years, what is classed as ‘big data’ today is significantly ‘bigger’ than a decade ago.

Ultimately however, the greater the volume (more data entries or rows) or the complexity (more properties or columns) of the data, the harder it is to process and extract insights from it in a timely and cost effective manner.

Originally, big data was defined by ‘the three Vs’:

Volume: The amount of unstructured data. In the case of IoT this is influenced by the number of devices in the network generating data points.

Velocity: The rate at which data is received and processed. For IoT this could be a real-time stream or occasional batch delivery.
Variety: This refers to the type of data, such as text, audio, video, metadata and more. IoT devices can produce many different data types and the processing of different types comes with its own requirements.

In recent years, two more ‘Vs’ have been added:

Veracity: This refers to the quality of the data, how reliable it is and essentially how ‘truthful’ it is. For simple IoT applications such as a meter reading a single data point may be reliable for the intended application. But for other applications a more complex dataset may need to be processed to reach a point of ‘truth’, especially in use cases like manufacturing or connected smart cars where lives may be at stake. This is tied to the next point.

Value: It is well recognized that data has intrinsic value. But that value often has to be unlocked through some kind of processing of the data. Value could be internal, such as optimization of operational processes, or it could be used externally to drive customer engagement. Value can take many different forms.

The development of open source frameworks such as Apache Hadoop and Apache Spark helped manage the growth of big data because they made it easier to store large amounts of unstructured data in a data lake or data warehouse. These developments also made data easier to work with and cheaper to store, which is just as well as the volume continued to grow.

For IoT initiatives it is the data generated, not the network of devices, that is the capital. Big data analytics is the commercial enabler for the Internet of Things and could manifest as product or service improvements to generate more revenue or cost savings from business or process optimizations. For IoT commercially, big data can mean big business.

Key benefits of IoT and big data analytics

Big data analytics has applications for IoT in all sectors, including telecom, retail, banking, manufacturing, public transport, government services and more to help solve generic and specific problems.

Making use of the massive data collected from IoT devices can assist many industries in many ways:

Predictive maintenance

While predictive maintenance isn’t necessarily a revenue generator, IoT-driven data insights can help enterprises to avoid unplanned downtime that could affect revenues. Example industries include manufacturing, pharmaceutical, utilities and transportation.

Enhanced customer experience

Big data analytics and IoT can help improve real-world experiences from theme parks, events, and hospitality, to public transportation through crowd management and monitoring, managing access to venues, areas and rooms, and providing payment services.

In energy and utilities, big data can help consumers better understand and manage their consumption of resources.

Operational efficiency

The use cases for operational efficiency in IoT are broad and varied and big data analytics expands them further still. Use cases exist in agriculture and smart farming, including monitoring of field and farming conditions via IoT devices and drones.

Supply chain management benefits include cellular trackers so users can monitor a roaming shipment’s location and condition in real-time, as well as logistics management, including route optimisation and planning, and fuel reduction.

For retail big data can help with inventory consumption and planning.

Real-time decision making

The ability to extract insights from large volumes of data in close to real-time make IoT and big data perfect partners for large scale initiatives like smart cities. Smart city ‘brains’ can help manage traffic congestion, footfall, lighting and even monitor and respond to safety issues dynamically.

For services like public transportation, big data can assist in optimization, diagnostics, and route management.

Eseye’s role in IoT and big data analytics

With seamless global coverage and near 100% device uptime, Eseye’s IoT solutions provide reliable, real-time data collection from connected devices, no matter where they are deployed.

By combining our robust connectivity with advanced big data analytics, businesses can gain deeper insights, optimize performance, and drive innovation. From predictive maintenance to operational efficiency improvements, Eseye enables organizations to transform raw IoT data into actionable intelligence, ensuring smarter business outcomes and long-term success.