By Michael Kurniawan, Schneider Electric Secure Power Business Vice President for Singapore, Malaysia & Brunei
Due to the ongoing pandemic, more Malaysians are now working from home and are more dependent on the usage of technology to maintain productivity levels. The local data traffic in Malaysia is expected to grow 5 to 10 percent year-to-year until 2025, according to the Malaysia Data Center Market Investment Analysis and Growth Opportunities 2020 – 2025 report.
The report also revealed that Malaysia’s data center market is expected to reach revenues of over RM3.3 billion by 2025. Coupled with the government’s plan to improve connectivity nationwide with the National Fiberisation and Connectivity Plan (NCFP), it is timely for organisations to start optimizing their data centres to meet the increasing demand.
It comes to no surprise when we hear organisations discuss their plans to enhance their data center infrastructure with technologies like Artificial Intelligence (AI) and focus on automation to improve uptime while controlling costs — all of which are important for companies to drive operational efficiency and business resiliency.
The current pandemic is driving forward-looking companies to become increasingly interested in predictive technology and remote capabilities in data centers. The ability for IT department to predict disruptions and unplanned downtime can potentially minimize impact to the business, especially in today’s environment that is riddled with uncertainties. According to analyst firm Aberdeen Research, depending on the industry, business interruptions can cost a company as much as $260,000 an hour.
AI & Machine Learning in Data Centers
Over the years, AI and machine learning have witnessed significant development and have now become smarter than before. In the case of data centers, algorithms that have been built for task automation and predictive maintenance are becoming more refined, therefore enabling IT departments to focus less on routine tasks and more on future planning.
Algorithms will leverage historical data to predict more accurately when maintenance is required. Therefore, not only can they alert IT departments that something is about to fail, but these intelligent systems can also minimize the chances of failure thanks to data-driven predictive maintenance models. Proactive insights on critical assets can help IT staff management the health and availability of an IT environment. These insights provide them with the ability to deliver actionable real-time recommendations to optimize data center performance, mitigate risk and ensure uptime.
In the wake of the pandemic, companies that relied on on-site data center support staff soon realized they had limited or no visibility into their data center operations. With cloud-based, next-generation management platform, IT support staff can now manage sites remotely and more importantly, in a much safer manner.
Better Data Center Performance with Predictive Capability
Increasing the intelligence and automation of the physical infrastructure and management systems enable data centers to be more reliable and efficient both in terms of energy use and operations. This also enables the collection and analysis of data that can lead to better performance with predictive capability.
According to the U.S. Department of Energy, predictive maintenance (the ability to repair a plant asset just before it fails) is highly cost-effective, saving roughly 8 percent to 12 percent over preventive maintenance (which is regularly scheduled, calendar-based maintenance), and up to 40 percent over reactive maintenance (performing no maintenance on operating equipment until it unexpectedly breaks).
AI and machine learning will underpin the next generation of what we think of now as data center infrastructure management. Disruptive technologies like these will integrate people and processes resulting in a true digital data center. As digital transformation progresses, we will see data center evolve based on real-world experience and are driven by demand for ever higher levels of profitability.