Addressing Dark Data

One major challenge the company faces is managing dark data—unused data that consumes energy and storage space without providing value. Regularly reviewing and deleting unnecessary data can address this issue, reducing both operational costs and environmental impact. For example, the company can categorise data into “hot” data, like real-time GPS tracking and customer requests, which needs to be quickly accessible, and “cold” data, such as old ride history, which can be stored in more energy-efficient systems. This approach not only optimises data storage but also significantly reduces the company’s carbon footprint by minimising the energy used to maintain irrelevant data.

Implementing Edge Computing

Alongside addressing dark data, the location of data processing also plays a crucial role in sustainability. Edge computing, which involves processing data closer to its source rather than relying on distant data centres, can reduce energy consumption by up to 30% and improve decision-making speed, such as matching drivers with passengers during peak times. Additionally, edge computing can lower infrastructure costs by reducing the demand on central data centres. As companies grow, edge computing offers a scalable solution to handle increasing data volumes without overwhelming central systems, thereby supporting innovation while maintaining sustainability. (source: QuantumZeitgeist)

Exploring Renewable-Powered Data Centres

To improve sustainability, renewable-powered data centres offer a great solution.  Energy sources like solar and wind offer carbon-free, stable power capable of meeting high demands without increasing greenhouse gas emissions. When combined with AI, these data centres can optimise energy use, adjust consumption in real-time based on demand, and predict maintenance needs, reducing waste and enhancing efficiency.

In Australia, with net-zero emissions targets by 2050, renewables are key to reaching sustainability goals.The GenCost 2023-24 report published by CSIRO and authored by Paul Graham, Jenny Hayward, and James Foster, highlights that solar and wind not only offer low-cost energy solutions but are also the most rapidly expanding sources. While there are some additional costs for integrating them, such as storage and transmission, these costs are expected to drop significantly by 2030, making renewables even more affordable. As Australia’s renewable energy sector grows, companies can adopt these cost-effective, scalable solutions to support global sustainability efforts. (source: CSIRO, GenCost 2023-24 Report)

Leveraging Predictive Models and Optimisation Algorithms

While exploring innovative energy solutions, it’s equally important to optimise how we use data and resources through advanced analytics. Predictive models and optimisation algorithms also play a key role in efficiency. For instance, a Random Forest model, which uses historical data to make accurate predictions, can estimate demand for rides or deliveries, allowing the company to deploy resources effectively. Before a major event, the model can analyse past data, weather patterns, and local activities to anticipate high-demand areas, ensuring that drivers are strategically positioned. Once demand is predicted, optimisation algorithms match drivers with passengers in the most efficient way, reducing wait times, saving fuel, and improving overall service quality. This approach not only enhances the customer experience but also contributes to the company’s environmental goals by minimising energy and resource usage.

Monitoring and Reporting Sustainability Efforts