Big Data: Hype or Real Transformation?

The transition from traditional gasoline-powered vehicles to electric vehicles has been gradual for some industries. There has been increasing interest in electric vehicles, and big data has played a key role in this shift. Big data, combined with intelligent algorithms, can provide insights into the placement of charging stations, vehicle energy efficiency, and the reduction of greenhouse gas emissions. These insights can contribute to the development of more sustainable, data-driven cities. There is also a need for companies to collaborate in order to achieve these outcomes.

Companies such as Enterprise Rent-A-Car have served as a useful example of how information sharing can support organisations seeking to adopt greener practices.

The trade of wildlife, timber, and fisheries products is widespread but often hidden. Much of this trade impacts endangered species and their habitats. It also has serious consequences for local communities. The development of better algorithms for modelling and predictive analysis can help strengthen conservation efforts and improve global trade monitoring. Sharing case studies, collecting more precise data, and building partnerships between conservation organisations are key steps in this direction.

Combining machine learning with supplementary analysis can help speed up the identification of illegal online sales and track supply chains within the global wildlife trafficking industry.

Food waste is a serious but often overlooked problem. According to the United Nations Food and Agriculture Organization (FAO), an estimated 1.6 billion tonnes of food is wasted, of which around 1.3 billion tonnes is edible. This contributes to approximately 3.3 billion tonnes of CO₂ emissions.

Leveraging big data and analytics to collect real-time operational data across the food supply chain can help improve waste management systems and increase the efficiency of organic waste streams. In many cities, IoT and big data are already being used to improve waste processing systems.

Big data can be used to evaluate candidates for technical roles by analysing publicly available information such as GitHub contributions, LinkedIn profiles, social media activity, and past projects. Not only does big data help make hiring processes more efficient, but it also expands access to a broader and more diverse talent pool.

Big data and data science can help solve complex problems and drive innovation within organisations. To deliver meaningful impact, companies must focus on addressing real-world environmental and social challenges. According to an article published by upGrad, there are numerous big data projects that can be completed remotely, including analysing crime rates, developing cybersecurity systems, detecting malicious users, and predicting health outcomes.

However, it is important to note that there are significant challenges involved. According to the Harvard Business Review, an estimated 85% of big data projects fail, often due to inadequate assessment of project risks.