Pivotal to the continued growth of cloud and AI implementation will be sustainability and data management issues, according to this crystal ball.

As data plays a crucial role in the learning and decision-making processes of AI systems, the demand for data processing is poised to experience exponential growth next year. 

However, a prominent yet surmountable hurdle is the anticipated environmental impact of AI stemming from the energy and data center resources required to operate larger computing models. 

Some industry estimates suggest that training certain AI models, such as Natural Language Processing, can produce carbon emissions equivalent to building and driving five cars over their lifetimes. This underscores the pressing need for greener data centers. 

Power, water and carbon usage are all critical metrics in assessing the efficiency of data centers. Fortunately, water cooling technology has proven itself as a far more energy-efficient and sustainable way of cooling, eliminating the need for air conditioning — with significant benefits to costs and environmental impact. Collaborating with efficient providers, especially in light of the increase in AI workloads, will become increasingly valuable next year. 

Terry Maiolo, Vice President & General Manager (Asia Pacific), OVHcloud

Data sovereignty predictions

 In establishing a truly secure digital ecosystem in the Asia Pacific region (APAC), governments are strengthening initiatives to secure consumer data. However, government policies and regulations cannot be the sole benchmark. Cloud providers and other technological entities will need to collaboratively address the escalating threat of cyberattacks by implementing additional safeguards. 

Despite that, disparities in data protection policies among the region’s countries persist: these variations are creating a complex regulatory landscape, leaving companies perplexed about the varied enforcement practices worldwide, and impeding local customers from fully leveraging cloud services. 

Ultimately, these considerations will see data sovereignty grow to become a driver of data storage trends in 2024 and beyond. Given the intricacies involved, progress in embracing cloud solutions has to emphasize transparency. Cloud providers will need to offer businesses enhanced support through local compute and storage capabilities for meeting evolving data compliance needs. Transparency will be the established norm sought by businesses, alongside cloud providers willing to adhere to this standard.

Cloud trend predictions 

 With the spotlight on emerging technologies such as AI, ML and IoT, businesses are actively incorporating these innovations into their strategies to unlock new growth frontiers. However, the success of these initiatives relies on robust processing capabilities capable of handling complex algorithms and vast datasets. Some organizations have even opted to migrate cloud workloads back to on-premises infrastructure due to factors like management decisions, data security concerns, higher-than-expected costs, and limited access to new technologies.

In some cases, the rush to adopt cloud computing has presented considerable challenges. Many organizations had neglected the crucial step of assessing technical requirements first and determining which workloads would genuinely benefit from a cloud environment. Consequently, workloads relocated without proper consideration have sometimes led to suboptimal performance or the need for extensive refactoring. 

Therefore, the imperative to process growing amounts of data generated with high-performance computing necessitates a nuanced approach to server infrastructure: 2024 will see businesses increasingly turning to fit-for-purpose servers (such as bare metal servers) to provide the robust platforms required for handling their sensitive data effectively. 

This bespoke approach will not only address the specific computational needs associated with emerging technologies but also caters to the heightened sensitivity and regulatory considerations surrounding the processing of confidential and proprietary data.