The proliferation of connected devices - such as cameras, lights, and sensors - is enabling organisations to capture vast amounts of data on their physical operations in real-time. Collectively known as the Internet of Things (IoT), these devices bridge the gap between an organisation’s physical and digital infrastructure, making it possible to remotely monitor and control operations at scale.

While the concept of an ‘Internet of Things’ first emerged in the 1980s, it’s only recently that IoT has begun to live up to the promise of automation and augmentation at-scale thanks to a transition towards cloud-native infrastructure. Fundamental in enabling organisations to deploy and monitor thousands of connected devices in real time, this new cloud ecosystem presents exciting opportunities for enhancing the efficiency and reliability of operations, while simplifying the processes of audit and assurance.

Introducing IoT Analytics

Collecting the vast amounts of data generated by IoT devices is a crucial foundation, however organisations need to be able to analyse it in real-time to extract actionable insights, and be ready to respond rapidly to those opportunities.

That’s where IoT analytics comes in. By combining real-time and historical data feeds with state-of-the-art analytics and machine learning techniques, organisations can leverage the power of the cloud to drive tangible improvements to their operational processes. There are three major areas where IoT can deliver significant performance improvements:

Improving Efficiency
Organisations can gain a deeper understanding of their operational health and identify opportunities to increase efficiency.

Use Cases:

  • Smart metering to forecast energy loads and optimise energy distribution
  • Fleet management to improve transportation efficiency and reduce costs
  • Asset tracking to optimise supply chain logistics

Improving Reliability
In the event of a fault, connected devices can automatically act to fix faulty systems or notify key stakeholders of potential errors before they occur. In this way, an IoT platform can be used to automatically monitor complex systems and improve their operating reliability.

Use Cases:

  • Computer vision to detect drain blockages in a water delivery system
  • Predictive maintenance on factory machines to prevent faults
  • Agricultural monitoring to ensure optimal soil conditions for farmers

Improving Assurance
Sensor data captured in real-time can ensure that compliance requirements are being fulfilled by forming important evidence. It can also be used to investigate the causes and extent of non-compliance in order to develop an appropriate response.

Use Cases:

  • Construction site sensors to ensure compliance with noise and disruption regulations
  • Shipment monitoring to record light and air quality for long-distance agricultural shipments
  • Water quality monitoring to ensure consistent delivery of clean water to consumers

Requirements for an IoT Platform

Data rapidly loses value over time. While traditional business intelligence platforms enable decision-making based on data that’s days, months or even years old, cloud-based IoT platforms enable organisations to act on their data in real-time, while it’s at its most valuable.

Source: AWS

To extract value from IoT data in real time, organisations need a secure, reliable and scalable IoT platform to process and action data coming in from many different sources. We’ve found a cloud-native approach to be the best way of addressing these requirements.

While having a robust architecture is necessary for deploying a scalable IoT platform, adding value through IoT initiatives requires more than just technology. Establishing a data-driven culture that can leverage insights in a timely fashion is crucial and will help organisations maximise their return on investment.

An Example IoT Analytics Architecture

We’ve developed a use case that involves receiving temperature, light and sound data from edge sensors, processing it in the cloud, and providing an interface for users to monitor and analyse it. Although this application is somewhat contrived, it demonstrates how a cloud-based IoT architecture can be purpose-built to meet the needs of specific IoT analytics applications.

Let’s look at the example of predictive maintenance. Our use case involves running an anomaly detection algorithm over temperature sensor data and sending an SMS notification as soon as an irregularity is detected. This kind of infrastructure would play a key role in a predictive maintenance system, and highlights how the base architecture provides a robust foundation for the automation and augmentation of a range of business processes, grounded in industry best-practices.

IoT Analytics at Your Organisation

While an IoT Analytics platform can have many moving parts, the insights generated by a well-architected platform can rapidly produce significant value. Working with a partner that has extensive experience developing analytics and ML pipelines can dramatically improve the quality of insights and the speed at which they are generated.

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