MTN Is Rewiring Its Nerve System in the Cloud, Trading Control for Speed
MTN is moving its data brain into the cloud, chasing speed and intelligence while learning what it means to hand part of its infrastructure to someone else’s system
MTN Group has completed one of its most ambitious internal transformations yet — the modernization of its Enterprise Value Analytics (EVA) platform. Rebuilt as a fully cloud-based system on Microsoft Azure, EVA 3.0 is not just an upgrade in storage or processing power. It represents a deep structural rethinking of how one of Africa’s largest telecom operators manages and interprets its data.
Inside MTN’s South African operations, the platform now processes roughly 22 billion records a day, running more than 800 analytics workflows from over 1,700 data feeds. It’s an ecosystem humming with telemetry, performance logs, and usage data, all filtered through machine learning models. On paper, that might sound like another digital modernization story. In practice, it shows the scale and complexity of trying to run a telecom business in real time — where a single database update can affect network quality for millions of users.
EVA 3.0’s implementation has been described as the largest telecom cloud deployment in the Middle East and Africa. That claim, if fully borne out, places MTN in the upper tier of global operators experimenting with industrial-scale data platforms.
How Azure Became the Chosen Engine
The decision to rebuild EVA on Microsoft Azure rather than MTN’s private data centers hints at how far the company is moving toward public cloud architecture. Azure’s suite of tools — notably Databricks for analytics and Defender for cybersecurity — gives MTN’s engineers a common language across markets.
The company’s Cloud Centre of Excellence has been central to that transition, coordinating training, certification, and deployment across multiple African markets. By 2025, MTN engineers had collectively earned more Azure certifications than any other organization on the continent — a quiet sign of technical muscle that doesn’t rely on marketing slogans.
Choosing Azure also folds MTN more deeply into a global supply chain of cloud infrastructure, where updates, compliance, and AI frameworks evolve faster than in proprietary systems. That offers speed but introduces dependency. For a company that has long relied on self-contained network management, it’s a cultural and operational pivot that requires new instincts.
When Telecom Meets Data Science
EVA 3.0’s purpose isn’t only to crunch numbers faster. It’s to create a live feedback loop between operations, customer behavior, and business decisions. A single insight can ripple from a billing pattern to an engineering fix, from customer churn models to regional investment priorities.
MTN has said that the new system improves processing speed significantly compared with its previous platform. The technical claim is less interesting than what it implies: that telecoms now compete not just on network reach but on the intelligence of their internal systems. In that sense, EVA 3.0 turns analytics into infrastructure.
And yet, there’s an unresolved tension here. Telecom data carries deep personal and economic value, touching everything from user location to digital payments. “Responsible AI” becomes more than a buzzword in this context — it’s a governance question. How MTN uses that data, and what guardrails it enforces, could determine the level of trust regulators and customers grant in the coming years.
A Template for a Continental Playbook
MTN says the EVA 3.0 framework will serve as a blueprint for modernization across its other markets. That’s a practical goal and a symbolic one. Many of MTN’s subsidiaries still rely on hybrid legacy systems patched together over decades. Replicating EVA 3.0 across the group could streamline everything from analytics to fraud detection — but it also risks introducing uniform dependency on a single cloud provider.
The challenge ahead is balancing centralization with local adaptability. Data regulation, network conditions, and digital maturity vary widely between, say, Nigeria and Uganda. A single blueprint may need constant tuning. If EVA 3.0 can evolve without flattening those differences, it could mark a new chapter in how African telecoms handle digital transformation at scale.
The Broader Stakes for African Telecoms
MTN’s overhaul lands in a period when data has become both an asset and a liability for African operators. Every market is tightening privacy rules, while customer expectations are growing faster than infrastructure budgets. To stay competitive, telecoms need cloud systems that don’t just store data but learn from it — spotting inefficiencies, predicting failures, tailoring offers.
EVA 3.0 is part of that evolution. It gives MTN a technical foundation that might, over time, reshape how the group measures success: not just by subscriber count or revenue per user, but by how intelligently it converts data into service quality and new business models.
If the platform performs as intended, MTN could position itself as a reference point for digital modernization in the African telecom sector — a company treating data not as an archive but as an active instrument of decision-making. That’s the real experiment underway.
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