Real-Time Processing at the Edge with NTT DATA

Real-time processing sits at the centre of current enterprise infrastructure strategy as organisations shift decision-making closer to where data is generated. 

NTT DATA is navigating this shift through a combination of private 5G, edge AI and managed services, linking connectivity with compute and operational control.

A recent partnership with Ericsson reflects this direction. The agreement focuses on deploying private 5G networks alongside embedded edge AI capabilities, allowing enterprises to act on data at source rather than relying on centralised systems. This reduces latency and enables continuous processing in environments where delays affect operations.

Shahid Ahmed, Global Head of Edge Services at NTT DATA, states: “As enterprises adopt AI at the edge, they need partners who can bring connectivity, intelligence and security together in a way that actually works in production.

“Together with Ericsson, we can deploy these solutions faster, operate them at scale and deliver outcomes. Private 5G gives enterprises the foundation they need to achieve real, measurable impact with edge AI and physical AI deployments.”

The approach reflects a move from pilot deployments to operational systems. Real-time processing requires stable connectivity, predictable performance and integration across IT and operational technology. NTT DATA’s model combines these layers within a managed service structure.

Private 5G as a processing layer

Private 5G networks act as an enabling layer for real-time processing by supporting consistent data transfer between devices, sensors and edge compute systems. Within the NTT DATA and Ericsson partnership, private 5G is delivered as a managed service with defined architecture and operational controls.

This model allows enterprises to standardise deployments across locations while maintaining control over data flows and security. It also supports workloads that depend on continuous data ingestion, such as monitoring systems and automated operations.

Åsa Tamsons, Senior Vice President and Head of Business Area Enterprise Wireless Solutions at Ericsson, says: “Ericsson has been advancing enterprise connectivity for over a decade. This extends that capability to support edge AI and physical AI at scale across industries.

“By combining our global platforms with NTT DATA’s engineering and managed services, industry expertise and AI-driven operations, enterprises can move from experimentation to always-on, production-grade operations.”

Real-time processing in this context is tied to system reliability. Data must be processed without interruption, particularly in sectors such as transport, energy and manufacturing where system responses affect physical processes.

Embedding AI into connectivity

A key element of NTT DATA’s strategy is embedding AI directly into network environments. Edge AI agents operate on infrastructure platforms where data is generated, allowing systems to interpret and respond to events without routing information to central cloud environments.

This architecture supports use cases that require immediate action. In manufacturing, systems analyse sensor and vision data to identify faults or deviations. In transport and logistics, vehicle and asset data feeds into routing and safety systems. In energy and mining, remote operations depend on continuous monitoring and automated responses.

The integration of AI with connectivity changes how enterprises design systems. Rather than separating data collection, transmission and processing, these functions operate within a unified environment. This reduces delays and simplifies system design.

“Private 5G is the backbone for scaling AI in production, where autonomous systems must operate reliably and at scale, but integration complexity often remains the final hurdle,” says Alejandro Cadenas, Associate Vice President of Worldwide Telco Research & Consulting, 5G, IoT and Mobility at IDC. 

“The combined expertise of NTT DATA and Ericsson seamlessly integrates edge AI and physical AI with enhanced connectivity, overcoming operational, scalability and accountability challenges and accelerating the deployment of AI with confidence.”

Real-time processing in data centres

NTT DATA applies real-time processing within its own data centre operations. At its Rhine-Ruhr 1 facility in Bonn, the company has deployed an AI-driven system to manage cooling infrastructure. The system replaces static control methods with dynamic optimisation based on live operational data.

The platform, developed with etalytics, uses a digital twin of the cooling system to simulate and adjust performance. It processes inputs such as ambient temperature, system load and flow rates, updating control parameters in real time. This allows the system to respond to changing conditions without manual intervention.

The digital twin combines data models with physical representations of system components. This ensures that predictions remain aligned with operational constraints. The system evaluates scenarios at short intervals, modelling performance across extended periods while maintaining real-time responsiveness.

The processing takes place on-site within the data centre. This local deployment ensures that control decisions are made without dependency on external systems, reducing latency and maintaining operational continuity.

The results showed a reduction of 19.1% in energy consumption for cooling within the observed period. The system includes safeguards that prevent actions outside defined parameters and allow operators to revert to manual control if required.

This use of real-time processing demonstrates how edge principles apply within data centre environments. Systems operate continuously, adapting to conditions and maintaining performance without interruption.

Linking edge and core infrastructure

NTT DATA’s approach connects edge processing with core infrastructure through managed services. Data centres remain part of the architecture, supporting workloads that require aggregation, storage or further analysis. At the same time, processing shifts outward to reduce reliance on central systems.

This balance allows enterprises to distribute workloads based on requirements. Time-sensitive processes remain at the edge, while other functions operate within central environments. The integration between these layers depends on consistent data models and orchestration.

The company’s focus on managed services supports this integration. By overseeing deployment, operation and maintenance, NTT DATA maintains alignment across systems. This reduces fragmentation and supports consistent performance across locations.

Scaling real-time systems

A recurring challenge in real-time processing is scaling systems from initial deployments to broader operations. NTT DATA addresses this through repeatable solutions and standardised architectures, particularly within the private 5G and edge AI partnership with Ericsson.

These solutions are designed for deployment across sectors including manufacturing, transport, energy and urban infrastructure. Each environment requires adaptation, but the underlying architecture remains consistent. This supports faster deployment and reduces integration effort.

Scaling also depends on operational governance. Systems must maintain performance across multiple sites while handling variations in data, infrastructure and regulatory requirements. Managed services provide a framework for maintaining this consistency.

The company’s approach reflects a shift towards operational platforms rather than isolated solutions. Real-time processing becomes part of a wider system that includes connectivity, compute and service management.

Industry application

Across sectors, real-time processing supports a range of operational use cases. In manufacturing, it enables inspection and maintenance processes based on continuous data streams. In transport and logistics, it supports routing and tracking systems that respond to changing conditions. In energy and mining, it enables remote monitoring and control of operations in complex environments.

Urban infrastructure also relies on real-time systems for traffic management, public safety and energy distribution. These applications depend on the ability to process and act on data within defined timeframes.

NTT DATA’s role in these environments centres on integrating technologies into operational systems. This includes deploying infrastructure, embedding AI and maintaining services over time.

Operational control and safety

Real-time processing introduces operational considerations related to control and safety. Systems that act on live data must operate within defined parameters to prevent unintended outcomes. NTT DATA incorporates safeguards within its deployments, particularly in environments such as data centres and industrial operations.

In the Rhine-Ruhr 1 project, optimisation actions are restricted if system conditions fall outside defined ranges or if communication issues occur. Operators retain oversight and can intervene when necessary. This ensures that automation does not override operational constraints.

Such controls are essential in environments where system actions have direct physical impact. Real-time processing must balance responsiveness with reliability, ensuring that systems remain predictable under varying conditions.