Modernizing data center infrastructure: how businesses thrive beyond legacy data centers

Data centers are the backbone of the digital age; however, many are stuck in the past. Legacy facilities, which were built for a different era, are now struggling under the weight of modern computing demands. With ageing infrastructure, poor energy performance and rising operational costs, these outdated environments are quickly becoming a liability in a world that demands speed, scalability and sustainability.

In light of this, forward-thinking organizations are taking action and migrating their core applications from traditional on-premise infrastructure to the cloud. Such a move achieves modernization of a bank’s IT environment whilst also positioning it to be more agile and cost-efficient when it comes to customer-centric innovation in an ever-evolving digital economy.

Despite this, transforming legacy data centers is not the undemanding cure-all that banks and other financial organizations might be vying for, or even economically viable for that matter. For some, the path forward means shutting down outdated facilities altogether. For example, Singaporean telco Singtel announced last year the closure of five of its legacy data centers in Singapore as part of its plans to pivot towards sustainable, AI-focused facilities, reflecting a wider shift to a more measured and strategic approach to IT infrastructure investment.

The growing need for data agility and cloud-native platforms

Organizations are therefore under growing pressure to respond at speed, often forced to relocate critical data with little notice when closures or capacity shifts occur. It's a logistical challenge, but one that underscores a broader truth, which is that data agility is no longer optional. Rather, it has become a critical need for financial organizations to be able to access, process and act on data, especially in an era where the priorities for technology in an organization can shift overnight.

With ESG standards now central to the tech industry’s agenda, traditional data centers that overheat, guzzle energy and leave a heavy environmental footprint are increasingly out of step with where the sector is headed. But the challenges go far beyond sustainability. Legacy infrastructures create operational drag given the fragmented systems, inconsistent data quality and rigid architectures that hinder fast, informed decision-making.

Cloud-native models provide real-time insights and the ability to scale on demand, which in itself stands as a sharp contrast to outdated data infrastructures that require significant upfront investment based on predicted needs. This often leaves businesses stuck with inflexible systems and delays whilst waiting for hardware. Security is another growing concern.

Whilst cloud providers constantly update and strengthen their defenses, many on-premises teams struggle to keep up. Add to that the rising pressure of data sovereignty regulations and escalating energy costs, and the need to modernize becomes not just compelling, but critical.

A smart migration starts with a clear strategy

Given its elastic scalability, precise cost controls and robust, built-in security, it is certainly no surprise that many organizations are turning to the cloud. However, whilst the benefits are clear, the journey isn’t instantaneous. Successful cloud adoption requires a well-thought-out roadmap. The first step is crafting a strategy that’s tightly aligned with business objectives.

This translates to assessing workloads based on their impact, prioritizing mission-critical applications and selecting the right deployment model, whether it is public, private, or a combination of the two. Multicloud is increasingly gaining traction as it helps avoid vendor lock-in and gives businesses the freedom to leverage the unique strengths of different providers.

But moving to the cloud must be done with precision, especially in complex, legacy-heavy environments such as those that can be found in traditional banks. A rushed migration can have the opposite effect of what financial organizations may hope for when undertaking mass data migration to cloud-native platforms.

Cloud adoption affects multiple parts, if not every part of the business, be it applications and data architectures or operating models and governance frameworks. Missteps in timing or execution can lead to budgets being stretched too tight, unplanned downtimes and costly inefficiencies. Mitigating this risk requires financial organizations to approach migration as a deliberate, end-to-end transformation that reshapes how banks operate, innovate and ultimately deliver value to their customers, instead of a quick fix or a reactive measure.

At the same time, integration of robust governance at every stage of the journey is critical, with security, compliance and data protection ensured from the very outset. With cloud environments demanding continuous optimization, it is a key consideration that the work does not stop at the point of migration. Performance and cost efficiency must be reviewed regularly to ensure systems are running as expected and delivering value.

Recent advances with generative AI and AI tools can also help to accelerate the migration from on-premises legacy data centers to cloud environments by automating discovery, dependency mapping and workload classification. These tools analyze infrastructure and application behavior to identify optimal cloud targets and architectures. AI coding products also assist in refactoring legacy code, creation of testing and documentation, predicting migration risks, and simulating performance in the cloud, reducing migration time and enhancing planning accuracy.

The symptoms of ageing infrastructure in banking

This imperative for cloud migration is especially acute in the banking sector, where traditional institutions are locked in a race against digital-first challengers that can deploy new features in a matter of weeks, whilst legacy banks remain reliant on ageing mainframes. The cost of inaction is no longer hypothetical.

Earlier this year, more than 1.2 million UK customers were hit by banking disruptions on payday, marking a critical moment for individuals and businesses alike. Major high-street banks reported service failures ranging from login delays to hours-long customer service waits.

These outages weren’t isolated incidents, but symptoms of deeper systemic fragility tied to ageing IT systems. In contrast, cloud-native organizations are built for resilience, scalability and real-time responsiveness, which are qualities that legacy setups struggle to emulate.

These failures were not just unfortunate, but predictable and more importantly, preventable, further highlighting the point that embracing cloud-native infrastructure is no longer a long-term goal, rather an urgent priority for maintaining customer trust and securing future competitiveness for incumbent banks.

Data agility as a means of being future-ready

Ultimately, data agility is the engine of faster decision-making, operational flexibility and innovation, whilst cloud-native platforms make that agility possible. By using a fully cloud-native core, financial institutions can sidestep the constraints of traditional infrastructure allowing them to focus squarely on customer needs and accelerate time to market in ways that legacy banks simply would not be able to through traditional channels.

Achieving these results requires organizations to invest not just in modern cloud platforms, but in a data-driven culture that treats information as a strategic asset. Real-time data streaming paves the way to immediate insights, rather than lagging by hours or days. And as market demands continue to evolve, success depends on ongoing integration and innovation.

Cloud transformation isn’t a one-off milestone; it’s a continuous journey of aligning strategy, technology and execution to stay ahead of what’s next.

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