Why data centre proximity now affects more than just performance
Why data centre proximity now affects more than just performance
For years, the distance between where data is generated and processed has been viewed through the lens of performance. The formula is a simple one: reduce distance, reduce latency, improve responsiveness.
That logic remains true, but the rise of geopolitical uncertainty, the proliferation of advanced cybersecurity threats, and increased government focus on the robustness of domestic digital infrastructure means where data is processed is no longer just a matter of speed.
As data-rich applications become more sophisticated and businesses process ever-greater volumes of sensitive, high-value data, the proximity of data infrastructure is becoming important for another reason entirely - data sovereignty.
What changed?
More than ever, businesses deploying modern, intelligent services depend on data at every level. AI models, real-time analytics, automated decision-making, and connected digital services all rely on the rapid processing of unique, often sensitive, information.
This is especially true for businesses operating in regulated or sensitive environments. Financial services, healthcare, the public sector, and critical infrastructure operators all manage data and services where trust and security are essential. In these contexts, moving data unnecessarily across territories can create additional complexity, increase exposure to risk, and reduce user confidence in how data is being handled.
We can see this focus on data sovereignty emerging in wider industry trends. IDC predicts that by 2028, 80% of commercial enterprises will require infrastructure providers to deliver digital sovereignty guardrails. This points to a growing demand for infrastructure that does more than provide raw compute capacity. Businesses increasingly want assurance that their critical workloads are being managed with the right safeguards.
Locality is now about more than performance
At Latos, the conversations we have with partners also reflect this shift. Those delivering AI-based services offer perhaps the best example, as large-scale inference workloads often need to process data in real-time, close to where that data is generated or consumed.
In practical terms, performance and data sovereignty are becoming increasingly connected. By processing closer to source, AI vendors benefit from improved responsiveness while also reducing unnecessary data movement, retaining control of how critical workloads are managed, and ensuring their infrastructure strategy aligns with regulatory and operational requirements.
Preparing for what comes next
The growing link between localised data infrastructure and data sovereignty is changing how infrastructure needs to be planned. At Latos, we see this challenge as not simply about placing more capacity closer to users, but about designing data centres that support performance, resilience, and control in equal measure.
That means thinking carefully about where compute is deployed, how workloads are managed, and how data movement can be reduced. We’re also exploring how businesses can retain greater visibility over the environments supporting their critical services. For example, our Neura edge data centres are designed to support localised, high-performance processing with facilities that can be deployed closer to where data is generated and used. This helps businesses meet the performance requirements of data-rich applications like AI, while also retaining control over workload placement and data governance.
We believe that as the adoption of data-intensive applications such as AI accelerates, infrastructure providers have a responsibility to help businesses innovate without adding unnecessary complexity or risk. The future of data infrastructure won’t be defined by performance alone, but by how effectively that performance can be delivered alongside sovereignty that keeps organisations in total control over their data.
