AI and automation and the data center skills crisis

AI and automation and the data center skill crisis

As demand for secured, on-premises data storage grows exponentially in the Asia Pacific region, it becomes increasingly evident that the shortage of skilled talents to build and operate those data centers represents the biggest stumbling block in bridging what is already a widening gap between supply and demand. AI and automation could serve as the tools needed to bridge this gap.

Southeast Asia alone is looking on track to reach a US$1 trillion gross merchandise value economy by 2030, with over 40 million new digital consumers having joined the Internet economy in the region in 2021 alone. As market demands grow stronger, data center owners are also looking to achieve build cycles of six to nine months — a considerably shorter lead time compared to the traditional timeline of 12 to 18 months.

The talent issue is, quite simply, a war on two fronts. The role of a data center engineer is one that is constantly shifting and evolving, and requires the talent to have the ability to operate and maintain the hardware, but also be able to understand and work with the software that is behind the digital infrastructure.

So just how can the talent pool, which needs time for training and upskilling, catch up enough to narrow this gap? Technology, particularly the use of artificial intelligence (AI) and automation, appears to be the most feasible answer.

AI and automation in play

While it may have been possible to manage and secure networks without the benefit of intelligent, automated tools 20 years ago, managing a data center in this day and age has become unfeasible without those tools, especially when taking into account the quantum of data that has to be addressed. It becomes even more critical that these tools are in place for the talents that are behind data centers, considering the mounting demand that is being faced by existing data centers already.

Both AI and physical automation can serve to increase efficiencies by taking over repetitive tasks, such as by utilizing an AI-driven platform for monitoring and remediation processes, that can be left to run on their own with only minimal human supervision (mostly for routine scheduled maintenance). Meanwhile, the human data center specialist would be able to focus on more complex tasks, having handed off the comparatively menial tasks such as data entry to an automated partner.

Meeting skyrocketing demand

One aspect of those more complex tasks could eventually involve meeting the gargantuan data needs that will be inherent to a fully-virtual world like Meta’s metaverse, which is expected to require more than a small number of physical locations, as it needs to be expansive even as it feels local and has ultra-low latency. The simple fact is that future wide-scale adoption of the metaverse means the need for more data centers.

It becomes clear that AI and automation are the hammer and chisel for the data architect that is the data center engineer, and that tools that can meet the needs of the engineer have to be made available for the proper operation and growth of the data center industry.