Saudi Telecom Company Data Science Priorities

Answering the Question: What Are STC’s Top Data Science Priorities for 2024–2026?

Saudi Telecom Company (STC) is deploying data science as a strategic engine powering its alignment with Saudi Arabia’s Vision 2030, the Dare 2.0 transformation agenda, and its platform strategy spanning connectivity, cloud computing, fintech, and digital services. As the telecommunications industry evolves from traditional voice and data delivery toward becoming a full-stack digital infrastructure provider, STC is embedding advanced analytics and machine learning across every subsidiary and business line.

Here are STC’s concrete data science priorities for 2024–2026:

• Network optimization – Using AI to manage 4G, 5G, and emerging 5G-Advanced networks, including predictive maintenance, load balancing, and energy efficiency across thousands of sites

• Customer analytics – Churn prediction, personalization engines, and dynamic pricing models to protect ARPU and enhance customer experience across mobile, fixed, and digital services

• Fintech risk models – Credit scoring, fraud detection, and AML monitoring for STC Bank, leveraging telecom behavioral data

• AI platforms and MLOps – Centralized data lakes, feature stores, and model registries enabling scalable deployment of hundreds of production models

• Cybersecurity analytics – Threat detection, UEBA (user and entity behavior analytics), and fraud prevention across SOCs and managed security services

• Smart city and IoT analytics – Turning sensor data from mega-projects like NEOM into actionable insights for utilities, logistics, and public infrastructure

• Operational automation – Internal process optimization spanning HR, finance, procurement, and field services

These priorities tie directly to recent milestones: the STC Bank launch, center3 data center expansion across Saudi Arabia, TAWAL’s tower consolidation strategy, and the strategic Telefónica stake acquisition. Data science is treated as a strategic asset embedded across STC Group entities—stc (core telecom), solutions by stc, TAWAL, center3, and STC Bank—with focus on monetization through new revenue streams, operational efficiency through OPEX and capex optimization, and enhancing customer experience through personalized services and proactive retention.

Strategic Context: Why Data Science Matters for Saudi Telecom Company

Data science underpins Saudi Telecom Company STC’s transition from a traditional telecom operator to a diversified digital infrastructure and services platform serving Saudi Arabia and the broader MENA region. This isn’t just about improving existing infrastructure—it’s about fundamentally reimagining what a telecom company can become.

Vision 2030 and the national push for digital economy:

Saudi Arabia has committed $100 billion through Project Transcendence to establish the Kingdom as a global AI and data analytics leader. The Saudi market for data analytics is projected to reach $8.8 billion by 2030, creating both opportunity and urgency for telecom providers to develop sophisticated analytics capabilities.

STC’s regional ambitions amplify this need:

• The 9.9% Telefónica stake (acquired in 2023) creates data exhaust across European and Latin American markets

• TAWAL’s tower strategy spans thousands of sites requiring algorithmic management

• center3’s data centers and subsea cables generate massive network telemetry

• STC Bank processes financial transactions that demand real-time analytics

The Dare 2.0 strategy explicitly aligns data science with three pillars:

Pillar	Data Science Role
Expand Core	Network optimization, capacity planning, customer retention
Scale Beyond	Fintech analytics, IoT monetization, enterprise services
Digitize STC	Process automation, AI-driven decision-making, workforce analytics

Rising network complexity—spanning 4G, 5G, upcoming 5G-Advanced, and eventual 6G research—combined with multi-cloud environments means that manual operations simply cannot scale. Algorithmic management through artificial intelligence and machine learning becomes the only viable path forward for telecom operators facing exponential data growth.

Network & Infrastructure Analytics

This section covers how data science improves capacity planning, 5G/5G-Advanced performance, and tower/data-center utilization across Saudi Arabia and STC’s expanding regional footprint.

Concrete use cases in network analytics:

• Radio network optimization – ML models continuously tune parameters across RAN sites to maximize coverage and throughput

• Load balancing – Traffic steering algorithms distribute demand across cells during peak periods

• Anomaly detection – Pattern recognition identifies potential outages before they affect customers

• Energy efficiency analytics – Optimization algorithms reduce power consumption across thousands of TAWAL-managed sites

TAWAL targets 30,000+ tower sites with tenancy ratios around 1.7–2.0x by 2026. Prediction models help prioritize:

• Site acquisitions based on coverage gap analysis

• Sharing opportunities with competitor telecom operators

• Upgrade schedules for legacy systems to 5G-ready equipment

center3 data centers in Riyadh, Jeddah, and Dammam use machine learning to forecast IT load, cooling demand, and GPU utilization. This optimizes capex as STC scales AI-ready capacity through at least 2027, ensuring reliable connectivity for enterprise workloads.

5G-Advanced and traffic prediction:

• Demand heatmaps guide capacity allocation across geographic areas

• Network slicing optimization allocates resources dynamically based on QoS requirements

• Latency prediction models support industrial IoT and other latency-critical services

AIOps and self-optimizing networks on the RAN and transport layers leverage time-series data for predictive maintenance and real-time performance tuning. This represents a shift from reactive break-fix to proactive network management—essential for maintaining service delivery standards as complexity grows.

Customer & Revenue Analytics

STC uses data science to protect and grow ARPU, reduce churn, and personalize offers across mobile, fixed, and digital services in Saudi Arabia and other STC Group markets. In a rapidly evolving industry where customer expectations continuously rise, analytics capabilities directly translate to competitive advantage.

Customer segmentation and behavioral clustering:

• Usage patterns (data consumption, call minutes, app preferences)

• Channel preferences (app, web, retail, call center)

• Device ecosystem (handset type, connected devices, IoT subscriptions)

• Demographic and lifestyle indicators

This micro-segmentation enables precise targeting in marketing campaigns, moving beyond broad demographic categories to behavior-based clusters.

Churn prediction models:

• Separate models for prepaid and postpaid segments, each with distinct behavioral signals

• Proactive retention offers triggered by predicted churn risk scores

• Personalized win-back journeys delivered via app notifications, SMS, and call center outreach

• Real-time intervention capabilities that respond to customer interactions showing dissatisfaction signals

Dynamic pricing and bundle optimization:

Saudi Telecom bundle offerings across mobile, fiber, TV, and cloud services require sophisticated pricing models. These systems:

• React to competitor moves from Mobily and Zain KSA

• Optimize promotional timing and discount depth

• Balance customer acquisition costs against lifetime value projections

Cross-sell and upsell models:

Propensity scoring recommends add-ons including:

• International roaming packages based on travel history

• Cloud storage upgrades tied to device and usage patterns

• Entertainment services aligned with content consumption preferences

• Cybersecurity add-ons for enterprise-adjacent consumer segments

Revenue assurance and fraud analytics:

Protecting margins requires detection of SIM-box fraud, international bypass, and unusual usage patterns. As traffic shifts increasingly to data and OTT channels, these analytics become essential for identifying revenue leakage that traditional monitoring would miss.

Fintech, STC Bank & Risk Modeling

Financial services—specifically stc pay’s evolution into STC Bank—represent a key growth engine requiring sophisticated data science for risk management, regulatory compliance, and customer acquisition. This is where telecom behavioral data creates unique competitive advantage in the financial sector.

Credit scoring for underbanked segments:

Traditional credit bureaus have limited data on many Saudi consumers. STC Bank’s models incorporate:

• Prepaid top-up patterns and consistency

• Bill payment history and timeliness

• Device type and upgrade behavior

• Location patterns indicating employment stability

• App usage suggesting financial sophistication

All models must comply with Saudi Central Bank (SAMA) requirements while delivering responsible access to credit.

Real-time fraud detection:

• Digital wallet transaction monitoring

• P2P transfer anomaly detection

• Merchant payment pattern analysis

• Cross-border remittance screening

• Graph-based techniques identifying organized fraud rings

AML monitoring and transaction screening:

ML models reduce false positives in anti-money laundering screening while meeting SAMA requirements. The goal is balancing regulatory compliance with customer experience—excessive friction drives customers to competitors.

Revenue growth analytics:

• Customer lifetime value (CLV) models for STC Bank customers

• Pricing and fees optimization across products

• Incentive balancing for card usage versus account deposits

• Acquisition channel ROI modeling

Responsible AI in financial decisions:

Given the sensitivity of credit and eligibility decisions, STC implements:

• Fairness checks across demographic segments

• Explainable models that provide clear reasoning

• Governance frameworks supporting regulatory audits

• Continuous monitoring for model drift and unintended bias

AI Platforms, MLOps & Data Infrastructure

Scaling data science at STC requires unified data platforms, standardized MLOps practices, and strong data governance across subsidiaries and regions. Without this foundation, individual analytics projects remain isolated experiments rather than enterprise capabilities.

Centralized data architecture:

STC is moving toward data lakes and lakehouse architectures that unify:

• Network telemetry and performance data

• Customer BSS/OSS data

• Financial transaction records

• IoT device and sensor data

Strict access controls and data residency compliance ensure that sensitive data remains within appropriate boundaries while enabling cross-functional analytics.

MLOps infrastructure supporting production scale:

Component	Purpose
Feature stores	Consistent feature computation across training and inference
Model registries	Version control and governance for deployed models
CI/CD pipelines	Automated testing and deployment for ML models
Monitoring dashboards	Real-time tracking of model performance and drift

This infrastructure supports hundreds of models in production across network, customer, fintech, and cybersecurity domains.

Integration with center3 compute resources:

• Sovereign cloud instances ensuring data remains within Saudi Arabia

• GPU clusters supporting training of large models

• Arabic language model development for customer-facing applications

• Anomaly detection models processing network log data at scale

Data quality and metadata management:

• Automated data profiling identifies quality issues proactively

• Lineage tracking shows how data flows through transformation pipelines

• Standard domain definitions create consistency across STC Group entities

API-driven monetization:

STC is exposing telco-as-a-platform APIs for location services, identity verification, messaging, and billing. Analytics-as-a-service offerings enable partners to build solutions using STC’s data processing capabilities through CPaaS and developer ecosystems—creating new digital solutions revenue streams beyond traditional connectivity.

Cybersecurity, Fraud & Trust Analytics

Rising cyber threats and accelerating digital adoption make security analytics a top data science priority for STC, especially in managed services and critical infrastructure protection. This extends beyond protecting STC’s own networks to providing security services for enterprise and government clients.

SOC threat detection using ML:

• Log analysis across network, application, and endpoint sources

• UEBA (user and entity behavior analytics) identifying suspicious patterns

• Insider threat detection through access pattern analysis

• Correlation of indicators across multiple data sources

OT and critical infrastructure protection:

Smart city projects and industrial deployments using NB-IoT and LTE-M require specialized security analytics:

• Anomaly detection across industrial networks

• Protocol-aware monitoring for SCADA and ICS systems

• Integration with energy, utility, and transportation sector security requirements

Telecom-specific fraud detection:

Fraud Type	Detection Approach
Account takeover	Behavioral biometrics and access pattern analysis
Subscription fraud	Identity verification and credit risk signals
Roaming abuse	Usage pattern anomalies during international travel
International revenue share fraud	Call pattern analysis and destination profiling

Graph algorithms and deep learning models identify organized fraud operations that would evade rule-based detection.

Identity and access analytics for enterprise clients:

Managed security services support zero-trust strategies with:

• Risk-based authentication adjusting requirements based on context

• Continuous monitoring of access patterns

• Privileged access analytics identifying potential compromise

• Integration with enterprise identity providers

Regulatory alignment:

CITC and Saudi cybersecurity frameworks require explainable AI and audit trails. This builds trust with enterprises and regulators considering STC for sensitive deployments—robust governance becomes a competitive advantage rather than compliance burden.

IoT, Smart Cities & Industry Analytics

Data science enables STC to capture value from IoT connectivity in Saudi mega-projects and industrial sectors, turning raw sensor data from millions of devices into actionable insights that enhance service delivery for enterprise and government customers.

Analytics on NB-IoT and LTE-M device fleets:

• Smart meter data for utilities enabling demand forecasting

• Logistics tracking with predictive ETA and exception alerting

• Public infrastructure monitoring identifying maintenance needs

• Non-technical loss detection for utility operators

Enabling smart city initiatives:

Saudi Arabia’s smart city projects—including NEOM, Riyadh smart initiatives, and Diriyah—require comprehensive analytics:

• Traffic management optimization using connected vehicle and sensor data

• Smart lighting systems adjusting based on occupancy and conditions

• Environmental monitoring tracking air quality, noise, and temperature

• Public safety analytics supporting emergency response

Private 5G and edge analytics for industrial sectors:

• Factory automation with real-time quality inspection

• Port operations optimization through container tracking and vessel scheduling

• Oil and gas predictive maintenance on remote equipment

• Computer vision at the edge for safety and security applications

• Digital twin simulations integrating real-time sensor feeds

center3’s edge nodes enable local data processing meeting latency and data-sovereignty requirements for sensitive applications.

IoT analytics capabilities are a key differentiator for solutions by stc in large enterprise and government tenders, supporting long-term managed service contracts that drive recurring revenue and strengthen the competitive landscape positioning.

Internal Digitization & Operational Excellence

Data science applies internally under initiatives like “Digitize STC” to streamline operations across HR, finance, procurement, and field services. The same cutting edge technologies deployed for customers also drive internal operational efficiency.

Workforce analytics:

• Attrition prediction identifying flight risks before departure

• Skills mapping for critical AI and cloud roles

• Workforce planning for digital transformation

• Training recommendation engines accelerating capability building

As STC shifts from traditional telco roles to digital and data positions, these analytics support the talent strategy required for technological advancements.

Field service optimization:

• Predictive maintenance scheduling for network equipment

• Route optimization for technicians visiting towers, fiber routes, and enterprise sites

• SLA compliance tracking with real-time alerts

• Truck roll reduction through remote diagnostics

Procurement and inventory optimization:

• Demand forecasting for network equipment and devices

• Safety stock optimization considering supply chain volatility

• Vendor performance analytics informing sourcing decisions

• Lead time prediction for planning purposes

Sustainability analytics:

• Energy consumption dashboards across network and data centers

• Carbon footprint modeling supporting ESG reporting

• Renewable energy site tracking and expansion planning

• Cooling efficiency optimization in data centers

Finance and planning applications:

• Revenue forecasting across business lines

• Capex prioritization models balancing infrastructure development needs

• Scenario simulations for strategic decision support

• Financial instruments exposure analysis for treasury operations

These internal applications demonstrate that data science isn’t just customer-facing—it fundamentally transforms business processes throughout the organization.

Data Governance, Ethics & Regulatory Alignment

As STC scales data science, governance, privacy, and ethics become central to maintaining trust with customers, enterprises, and regulators. In a region with stringent data residency requirements and evolving privacy frameworks, governance capabilities directly impact market access.

Group-wide data governance policies:

• Data ownership roles clarifying accountability

• Stewardship programs ensuring ongoing quality

• Classification schemes aligning protection with sensitivity

• Retention schedules meeting regulatory and operational needs

Privacy-by-design in customer and fintech analytics:

• Anonymization techniques protecting individual identity

• Consent management integrated into customer touchpoints

• Purpose limitation ensuring data use aligns with customer expectations

• Data minimization reducing unnecessary collection

AI ethics principles:

Principle	Implementation
Fairness	Regular testing for demographic bias in models
Transparency	Documentation of model logic and training data
Explainability	Human-interpretable reasoning for high-stakes decisions
Accountability	Clear ownership and escalation paths for model issues

Monitoring for model drift and unintended bias is especially critical in credit, pricing, and eligibility models where operational risks include both customer harm and regulatory exposure.

Regulatory collaboration:

STC works with CITC, SAMA, and national cybersecurity entities to ensure data science initiatives support national policy goals. Saudi Arabia’s Vision 2030 emphasizes that reliable data is foundational to development—the Saudi Data and Artificial Intelligence Authority (SDAIA) emphasizes that relying on modern technologies without ensuring accurate and reliable data sources can lead to misleading outcomes.

Robust governance and ethics represent a competitive advantage when bidding for sensitive government and critical infrastructure projects. Enterprises evaluating strategic partnerships prioritize vendors demonstrating mature data practices.

Building Data Science Talent & Partnerships

Talent and ecosystem partnerships are as important as technology for executing STC’s data science agenda. In a market where data science skills are scarce globally, building sustainable capability requires multi-faceted approaches.

Internal capability-building:

• Dedicated data science and analytics hubs within STC

• Training programs for engineers and business teams

• Clear career paths for data roles across the group

• Knowledge sharing across subsidiaries and regions

Technology vendor partnerships:

Global vendors extend STC’s capabilities:

• Oracle partnerships for enterprise data platforms

• Ericsson collaboration on network AI solutions

• Huawei joint development on 5G analytics

• Cloud hyperscaler relationships for advanced services

Academic and startup ecosystem:

• University collaborations on research and talent pipelines

• Research center partnerships on emerging technologies

• Saudi tech hub engagement for local innovation

• STC’s corporate investment fund (launched February 2023) focusing on AI, cybersecurity, and IoT startups

Industry visibility:

Participation in events like LEAP 2024 and LEAP 2025 showcases data-driven solutions while attracting talent, partners, and enterprise clients. These gatherings bring together senior data and AI stakeholders including CDOs, CIOs, and heads of data science—essential networking for the industry landscape.

Cross-functional execution:

Models only create value when deployed in day-to-day operations. STC emphasizes:

• Cross-functional squads combining network, IT, marketing, finance, and data science expertise

• Business outcome ownership rather than model accuracy metrics alone

• Change management ensuring adoption by frontline teams

• Feedback loops connecting production results to model improvement

Future Outlook: Evolving Data Science Roadmap for STC

Data science will continue to evolve at Saudi Telecom as emerging technologies like 5G-Advanced, 6G research, and generative AI mature. The future outlook points toward deeper integration of analytics into every aspect of operations and customer engagement.

Expanding generative AI use cases:

• Arabic-language customer support reducing contact center load

• Code generation accelerating internal development

• Document intelligence for contract analysis and compliance

• Knowledge assistants improving employee productivity

Edge AI integration:

Real-time analytics will move closer to customers and critical applications through:

• Network edge inference for latency-sensitive services

• On-device processing for privacy-sensitive applications

• Industrial edge deployments for manufacturing and logistics

• Connected vehicle and autonomous system support

New monetization models:

Data science enables innovative revenue approaches:

• Outcome-based SLAs where payment ties to business results

• AI-powered managed services with continuous optimization

• Analytics-based revenue-sharing with ecosystem partners

• Premium tiers offering advanced insights beyond basic connectivity

STC aims to keep EBITDA margins strong and outperform regional peers by combining infrastructure scale with advanced analytics and automation. Saudi Telecom’s growth forecast depends on successfully executing this data-driven strategy while navigating competitive pressure that could slow Saudi Telecom’s growth if analytics capabilities lag.

Strategic positioning:

Saudi Telecom expanding its data science capabilities isn’t optional—it’s essential for:

• Diversify revenue beyond traditional connectivity

• Maintaining market share against digital-native competitors

• Supporting economic growth objectives under Vision 2030

• Enabling high speed internet and telecommunications infrastructure modernization

Data science is not a side initiative but a core pillar of Saudi Telecom Company’s long-term growth strategy, resilience, and leadership in Saudi Arabia and the wider MENA region. As the Kingdom positions itself as a global technology hub, STC’s analytics capabilities will determine whether it leads or follows in the digital transformation of the telecom sector.

Key takeaways:

• Saudi Telecom Company prioritizes seven core data science domains through 2026: network optimization, customer analytics, fintech risk, AI platforms, cybersecurity, IoT/smart city, and internal automation

• Data science is embedded across all STC Group entities, not siloed in a central team

• Governance and ethics are competitive advantages for sensitive government and enterprise contracts

• Talent development and ecosystem partnerships are as critical as technology investments

• Generative AI and edge analytics represent the next frontier for STC’s data strategy

For organizations evaluating the Saudi telecom market—whether as investors, partners, or enterprise customers—understanding these data science priorities provides essential insight into where STC is heading and how the telecommunications industry in Saudi Arabia will evolve through 2030 and beyond.