The convergence of IoT and big data is a key pillar of Industry 4.0. By analysing vast volumes of data, businesses can improve production processes and enable smart manufacturing.

There is a natural synergy between IoT and big data. IoT devices generate large quantities of raw data; big data tools filter, sort, and analyse this data to extract actionable value.
This convergence is now central to various domains. In smart cities, for instance, data collected by distributed sensors provides insights into traffic flow, parking availability, and more. In factories, connected devices send data about both their environment and internal operation, enabling everything from worker monitoring to real-time machine diagnostics.

This circular, continuous process can scale—from the individual production line to entire organisations and customer ecosystems. When applied within a single process, it helps monitor and refine operations. When extended to different departments, it facilitates internal integration. And when products send data back from customer environments, companies gain insights into performance, maintenance needs, and usage trends.

One of the most valuable applications of this convergence is predictive maintenance—servicing machines before failures occur, whether those machines are in a factory or deployed at a customer’s site.

The ability to control or configure devices remotely also becomes a major advantage, especially in situations where the asset is inaccessible, hazardous, or remote.

Thanks to the ongoing miniaturisation of sensors and wireless communication modules, it is now possible to embed smart technology into nearly any object. These “embedded systems” collect environmental data and transmit it over the network. They are supported by a growing range of wireless networks designed to connect objects and machines reliably and securely.

IoT data varies widely depending on the object or system involved. It may include:

• Environmental data (e.g., temperature, humidity, air quality)
  
• Worker monitoring data (e.g., location, use of PPE, compliance with safety protocols)
  
• Operational data (e.g., equipment performance, diagnostics, predictive maintenance)
  
• Contextual information for smart environments (e.g., traffic, mobility, utilities)
• Personal health data (e.g., from wearables used in telemedicine or sports)

IoT data can be stored and analysed in various ways depending on the use case, data volume, and infrastructure constraints. Data can be processed:

• On-premises
  
• In the cloud
  
• In hybrid architectures
  

The method depends on:

• The type and volume of data
  
• Power and connectivity limitations
• Whether the goal is storage, real-time analysis, or batch processing

Key Analysis Methods:

Let us examine some methodologies of big data analytics.

• Distributed analysis – Best for large-scale datasets distributed across systems. Often implemented with platforms like Hadoop or Spark.
• Real-time analysis – Used when timely response is critical (e.g., monitoring live data streams).
• Edge analytics – Performs lightweight analysis directly on IoT devices or gateways to reduce latency and pre-process data before cloud transmission.
• Hybrid analysis – Combines edge processing with cloud-based analytics for efficiency and scalability.
• Descriptive analysis – Provides a real-time or historical snapshot of performance through dashboards and reports.
• Diagnostic analysis – Identifies the root causes of events or issues.
• Predictive analysis – Forecasts potential equipment failures, enabling preventive maintenance.