Big Data, in a sense, is the next evolutionary step in technology from. Big data is considered a management revolution in business. It allows for greater measurements, and more precise business management. The big data movement, like analytics before it, seeks to glean specified intelligence from data and translate that into business advantage.
The greatest difference that big data introduces is in the name, volume. As of 2012, roughly 2.5 exabytes (approximately 2.5 million terabytes) of data are created each day, with that number roughly doubling every 40 months. More data cross the internet search second than was stores on the internet 20 years ago. This means that companies are able to work with many petabytes (approximately 1000 terabytes) of data in a single dataset, and that is not limited to data on the internet. This includes internal databases, client databases and partner databases.
The speed of this data creation is even more important than the volume. Real-time data collection gives companies the agility to make decisions faster than their competitors. Big data takes the form of text, messages, updates, images, video, posts on social networks, readings from sensors and instruments, GPS signals from mobile devices and more.
As more business activity is digitised, new sources of information and cheaper equipment combine to bring a new era where vast amounts of digital information exist on every topic imaginable. With mobile phones connecting the vast majority of us to the internet, people have become walking data generators.
This opens a world of exceptionable data that gives service providers a competitive edge. Work Management processes become highly informed of not only internal operations, but of global business processes, challenges, solutions, and technological developments. With so much shared data the Work Management field becomes highly competitive, where organisations battle to see who can use the data in the most effective and efficient ways, to optimise their processes. This level of data can be used to train new bots and increase automated processes for example. AI tools are trained off of this data to increase their knowledge and build their decision-making skills, the more data the AI has access to the more intelligent it becomes.
The data available are often unstructured, so the task is to use big data intelligently. This is where human insight has been instrumental. When everyone has access to so much data, only those skilled at interpreting certain kinds of data should be making business decisions for organisations. Enter AI and now human input can be greatly reduced, whilst the processing and analysing of data is sped up to almost instantaneous, empowering business managers to make even faster decisions. This is especially vital in-service delivery across all industries.
This is where IoT becomes invaluable.
IoT (Internet of Things) is a network of physical objects, devices that are embedded with sensors and software for the purpose of collecting, connecting, and exchanging data with other devices and systems across the internet. IoT devices can range from ordinary household devices; mobile phones, kitchen appliances, cars, thermostats, baby monitors, to sophisticated industrial instruments. There are over 17 billion IoT devices connected today, and that number has been increasing by 2-3 billion each year.
Through affordable computing, cloud services, big data, analytics, and mobile technologies, physical objects can seamlessly exchange and accumulate data with little human involvement. In this interconnected environment, digital platforms can document, observe, and fine-tune every interaction among connected entities, merging the physical and digital worlds in a collaborative ecosystem.
IoT devices generate vast amounts of data that can be leveraged by AI systems to significantly improve work management systems, particularly in infrastructure maintenance and urban planning. When it comes to identifying, early detecting, and predicting problems that need repairing or fixing, such as potholes, fading street markings, or damaged streetlights, the integration of IoT devices with AI analytics offers numerous benefits.
Devices, such as drones or vehicles equipped with radar and LiDAR (Light Detection and Ranging), can continuously monitor the condition of infrastructure in real-time. This allows for the immediate identification of issues such as potholes, cracks, fading street markings, and non-functioning streetlights.
The data collected by these devices can include images, depth measurements, GPS co-ordinates and exact locations, providing a comprehensive dataset that AI can analyse for insights and use to trigger repair work in a Work Management system.
Machine learning algorithms can process the collected data to not only identify existing issues but also predict future infrastructure failures. For example, by analysing the progression of wear and tear on road surfaces over time, AI can predict when a pothole is likely to form. By monitoring the pooling of water on the road surface AI can predict where cracks on the road surface are likely to develop.
AI can further identify patterns that may not be immediately obvious to human inspectors, such as subtle changes in road texture or street light functionality, leading to early detection of potential issues.
AI can then help prioritise repair tasks based on the severity and impact of detected issues. For instance, a large pothole on a busy road may be flagged for immediate repair, while smaller issues in less critical areas may be scheduled for later.
By predicting potential future problems, AI enables more efficient allocation of resources, ensuring that maintenance crews are dispatched where they are needed most, thus preventing the escalation of minor issues into major ones.
Early detection and predictive analysis help shift the focus from reactive to preventive maintenance. This not only saves costs by addressing issues before they become severe but also enhances public safety by reducing the risk of accidents caused by poor infrastructure.
The most valuable element of IoT devices is that they provide ongoing updates to work management systems, allowing for dynamic adjustment of maintenance plans as new data is receive, continuously ensuring that the most current information is always being used to guide decisions. IoT is instrumental to achieving accurate and seamless automatic scheduling and dispatching.
While the integration of IoT and AI offers significant benefits for infrastructure management, it also presents challenges such as data privacy, security, and the need for significant computational resources to process and analyse the big data generated. Additionally, the accuracy of AI predictions and the reliability of IoT devices must be continually assessed and improved.
In summary, IoT devices producing big data for AI to sort through can revolutionise work management systems by enabling real-time monitoring, early detection, predictive maintenance, and optimised resource allocation, leading to more efficient, cost-effective, and safer infrastructure maintenance practices.