Managing data analytics in the telecom industry

The telecommunications industry is drowning in data, and analytics are the ticket to competitive advantage. Analysts now estimate the global telecom analytics market is in the mid-single-digit billions today and growing rapidly, with forecasts pointing to double-digit CAGRs over the next decade and market values in the low-to-mid tens of billions by 2030–2033 (see Grand View Research and IMARC for recent forecasts).

But investing in data analytics is only the starting point. To truly unlock its value, CSPs need clearly defined use cases, an integrated data approach, and modern technology. In this blog, Alen Muslić, Chief Innovation Officer at ZIRA, looks at the diverse use cases for data analytics and how it can help CSPs drive profitability and customer retention.

Understanding the landscape of big data analytics in telecom

In recent years, CSPs have been laser-focused on optimizing performance and cutting costs. As they face an increasingly competitive landscape, keep up with technological breakthroughs, and adhere to ever-changing regulations, it can be difficult to locate opportunities for improvement across their business.

In this context, data analytics has proven a powerful tool, allowing CSPs to turn data into insight. With the considered approach to data analytics, telecom companies can find and address inefficiencies, such as analyzing customer data to understand more about customer behavior and find actionable insights for improving their customer service.

Data analytics is also an increasingly important factor as CSPs face growing competition from technology companies and hyperscalers. TM Forum’s benchmarking and GenAI briefs show CSPs are rapidly maturing their AI and analytics strategies, moving from pilots to production use cases across customer experience, network assurance, and partner operations. TM Forum’s research from 2024 recommends building foundational data practices now to capture AI value.

Traditional use cases for data analytics in telecom

CSPs are already using data analytics in some areas of their business, such as:

Customer and partner behavior analysis

The provision of reliably positive customer and partner experiences is key for CSPs. For this, the first step is having a detailed understanding of what products and services partners and end-users need at what times. This then allows CSPs to tailor their solutions specifically to their partners and greatly increase customer satisfaction.

Data analytics can help CSPs analyze customer and partner behavior and locate opportunities for up- and cross-selling or providing customized services. For example, BSS products enhanced with data analytics, like ZIRA’s Revenue Management, can identify personalized promotions and corridor discounts for partners and customers, which invariably leads to higher levels of customer satisfaction.

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Network optimization

Network performance is of course a vital metric for CSPs, as it correlates directly with customer experience. Network users expect uninterrupted service at all times, so minimizing drops in service quality and issues with network congestion remains an important differentiator.

Using data analytics, CSPs can measure critical metrics that link to overall performance, such as network latency or data packet loss. Predictive analytics can even help network optimization teams anticipate future network problems and resolve them before a larger issue presents itself.

Fraud detection

Fraud remains a major and growing cost for the industry—the CFCA estimated telecom fraud losses at ~$39 billion in 2023 (a ~12% rise year-on-year)—and industry surveys show it remains a top-tier risk in 2024–25. That makes analytics-driven fraud detection non-negotiable. 

With advanced data analytics capabilities, CSPs can process their large volumes of network usage data, such as Call Detail Records (CDRs) and billing usage information, and identify any anomalies that may be caused by fraudsters. As a result, they can stop fraud-related revenue leakage much faster and more easily than without the help of data analytics.

The future of data analytics

These use cases, while helpful, don’t show the full spectrum of possibilities that data analytics represents in the complex domain of telecommunications today. To get the most value out of their data, CSPs will need more sophisticated use cases that recruit cutting-edge technology to analyze and forecast data.

One key example of this is using AI and ML to help trading and routing processes. These rely heavily on time-series data, from supplier prices to traffic patterns. Understanding changes in the data can greatly enhance a CSP’s ability to forecast fluctuations and prepare ahead of time. For example, with the right ML pipelines, traders and route managers can produce high-confidence supplier-price forecasts over rolling horizons (weeks to months) and use scenario simulations to limit exposure—a capability ZIRA’s Telco AI Platform is designed to support in live operations.

Having increased visibility over supplier prices also leads to profitable new deal opportunities, which can help CSPs form new partnerships in their ecosystem.

GenAI & real-time

Two trends are now driving next-gen analytics in telecoms: (1) real-time/edge analytics to make operational decisions in milliseconds for network assurance and QoE, and (2) generative/foundation models used to automate playbooks, summarize incident traces, and accelerate feature engineering for forecasting models. Standards and guidance, including TM Forum benchmarks and the ITU’s GenAI telecom guidance, are already shaping best practice.

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Overcoming challenges in data analytics management

Whether it is more traditional or future-looking, CSPs have to approach data analytics with the right mindset. Here are some considerations to bear in mind.

Data quality and consistency

You’ve heard it before, but your data analytics capabilities are only as good as your data. It is critical to have a constantly updated and reliable data source that your analytical tools can feed into.

To that end, CSPs should ditch uploading data manually and use automation whenever possible. For example, with ZIRA’s Trading and Routing Management, it’s possible to automatically upload and verify supplier prices from emails, ensuring accuracy throughout the process.

How trading and routing management can benefit from AI-enhanced data analytics

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Scalability and flexibility

As your business scales, your data grows with it. To ensure your analytical processes remain reliable, you need flexible and scalable tools that can expand along with your business. Our lead-to-cash BSS suite supports business growth with an agile Revenue Management system that can keep your billing data safe as you onboard more partners and customers without losing any existing functionality.

Security and privacy concerns

Data analytics and data security should be synonymous in every telecommunications business. CSPs have vast amounts of sensitive data at their disposal, from customer information and billing data to service agreements and partner records. To guarantee regulatory compliance and prevent the risk of a data breach, CSPs should have robust data security measures in place. A few important elements to consider:

• Implementing a multi-layered security strategy incorporating firewalls, intrusion detection systems, data encryption, and regular security audits to detect and respond to security breaches in good time.
• Educating employees on the latest security threats and ensuring they understand and adhere to the company’s security protocols.
• Managing third-party risks by establishing protocols to ensure that external parties with access maintain data confidentiality.
• Establishing robust procedures for backup and recovery to minimize the fallout from security incidents.  

Operationalizing models: DataOps & MLOps

Fast pilots are easy; production is hard. CSPs must invest in reproducible data pipelines (DataOps), model monitoring (MLOps), CI/CD for models, and retraining plans so that models don’t decay as traffic and pricing patterns change. Without these, predictive analytics deliver only transient gains. 

Regulation & explainability

Operators must align analytics and AI with local privacy rules (GDPR, data-localization laws) and adopt model explainability for any automated decisions that affect customers or partners, both to satisfy regulators and to maintain partner trust. 

Conclusion

The use of advanced analytics in telecommunications will only deepen in 2025 and beyond: rising data volumes, GenAI and the move to real-time network control mean analytics will be core to both operational resilience and new revenue models. CSPs that combine strong data governance, repeatable DataOps/MLOps practices and modern BSS integrations will unlock the highest value—reducing fraud leakage, improving QoE and creating commercial products from billing and partner data. ZIRA’s suite is purpose-built to integrate those capabilities into live operations and help operators move from experimentation to measurable, sustained outcomes.