Lionel Sujay Vailshery
Research expert covering cloud computing and emerging tech industries
Big data - statistics & facts
From healthcare data to social media metrics, modern technology allows large, complex data sets to be delivered in near real time. The term ‘big data’ is used to describe these data sets, be they structured or unstructured, and generally refers to data that cannot be analyzed using traditional methods. The size and complexity involved in big data instead necessitate the use of specialist software, which in turn can require considerable processing power and storage capabilities. The term is increasingly referred to in the context of cutting-edge technologies such as AI, which can consume, analyze and produce vast quantities of data with ease.
The rise of IoT is unleashing a flood of data
The emergence of big data analytics has accompanied exponential growth in the global volume of data. While it is difficult to visualize this deluge of data, forecasts expect the total volume of data worldwide to reach 182 zettabytes in 2025, and 394 zettabytes by 2028. This will be fueled in part by the ‘Internet of Things’ (IoT), which refers to the growing network of connected devices and sensors generating and exchanging data online. An IoT device can include anything from a smartwatch able to record a workout, to a sensor designed to monitor factory machinery. At the forefront, this data is being used by AI firms to train increasingly complex artificial intelligence (AI) models.Organizations look to subscription models
The sheer volume of data involved in big data analytics has proved a hurdle for organizations wishing to store and analyze data on-premises. The majority therefore opt to store data in the cloud, making it available anywhere, at any time. This has given rise to the ‘analytics as a service’ (AaaS) model, which enables organizations to carry out analytics via a cloud-based subscription service, and thus avoid the high costs associated with on-site storage and processing. In addition, businesses across the globe are recognizing the importance of data skills in the workplace, with the vast majority of those responding to a 2024 survey highlighting AI and big data skills as being of increased importance in 2025.Challenges ahead
Despite promising adoption figures, a range of challenges persist. One question surrounds data quality, with AI models able to fabricate poor quality synthetic data with ease, while another concerns cybersecurity, with a raising awareness that bad actors are actively seeking to access sensitive data sets. Moving forward, business leaders will look to consistently evaluate the tools, personnel, and processes they employ to harness big data while maintaining trust and reliability.
