Edge computing tends to decentralise data storage and processing, and this means bringing computation close to a given data source. It is important to highlight that this trend demands new approaches to data management, such as distributed data management systems and edge analytics, for the purpose of handling the velocity and volume of data generated at the edge.

Machine learning is used to automate tasks like anomaly detection, data cleansing, and data integration. Additionally, it can optimise data retrieval and storage processes, enhance predictive analytics capabilities, and improve data quality.

Data virtualisation is the abstraction of data from its storage, format, or physical location and presenting it to applications or users in a unified view. The reason why it is gaining traction is because it allows any given organisation to analyse and access data from disparate sources without requiring replication or physical movement. The result here is an improvement in agility and a reduction in complexity.

It is because of the increasing complexity and volume of data that today’s organisations are placing an immense level of emphasis on data governance. This is done in order to ensure security, compliance, and data quality. A host of privacy regulations, such as CCPA and GDPR, demand organisations to properly implement reliable data management practices in order to safeguard sensitive information and take into account individuals’ right to privacy.