Petroc Taylor
Research expert covering global developments in the use of data
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. While costly, embracing big data enables organizations to derive powerful insights and gain a competitive edge.
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, the scale is illustrated by an explosion in mobile data traffic, which is set to exceed 167 exabytes per month in 2024. 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 artificial intelligence firms to train increasingly effective artificial intelligence (AI) algorithms.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. Indeed, 2023 proved a pivotal year for big data adoption, with a sharp increase in the share of data leaders across the globe who considered their organization to be driving innovation with data.Challenges ahead
Despite promising adoption figures, a range of challenges persist. One question surrounds data quality, with just 37 percent of businesses reporting that their efforts to improve data quality have been successful. Overall, organizations will need to address customer concerns with data collection, as the release of generative AI tools heightens privacy fears.
