Turning real-time network data into monetizable insights — responsibly and at scale
As growth from the traditional connectivity business matures, telecom operators are increasingly looking to network data as a new engine of value creation. However, monetizing telecom data at scale is not simply about access to large datasets. It requires the ability to convert raw, real-time network signals into reliable, actionable intelligence — while maintaining trust, privacy, and regulatory compliance.
In a recent Mobile World Live webinar, Mobileum and Telkomsel discussed how telecom data can be monetized responsibly and how Telkomsel built one of the industry’s most advanced data monetization engines.
Alfian Manullang, Vice President – Data Solutions & Digital Financial Services at Telkomsel, Raja Hussain, Chief Revenue Officer at Mobileum, and Miguel Carames, Chief Product Officer at Mobileum, shared their insights and key learnings during the webinar.
Below is an edited Q&A from that discussion. To dive deeper into the use cases and platform details, be sure to watch the full webinar on demand.
Q: Why is data monetization becoming such a critical growth lever for telecom operators today? What has changed to make telecom data more valuable now than in the past?
Raja Hussain, Mobileum: The global telecommunications industry is facing slowing subscriber additions and acquisitions across most markets. As a result, sustainable growth increasingly depends on extracting more value from existing assets rather than expanding connectivity alone.
Data monetization has emerged as one of the most compelling opportunities. Based on current revenues and market traction, this segment is expected to reach around $13 billion by 2030, growing at more than 15 percent annually. Over the longer term, the opportunity could exceed $100 billion. Much of this growth is driven by demand from global advertising and market research industries, where insights-as-a-service alone already represents a $4–5 billion segment.
Historically, telecom operators have been largely absent from this value chain, despite holding unique behavioral data that cannot be replicated elsewhere. What has changed is the way data can now be used. Enterprises once relied heavily on surveys and extrapolated datasets. Today, aggregated, real-world behavioral insights derived from live network data are far more valuable — especially when delivered in a privacy-safe and compliant manner.
The rise of generative AI has further amplified this shift. AI systems depend on reliable, high-quality data streams for training, inference, and decision-making. This has pushed enterprises to actively seek trusted external data sources that can operate at scale. Telecom networks are uniquely positioned to meet this demand.
In parallel, the industry-wide push toward APIs has transformed how data is consumed. Data is no longer static information stored in systems, but insights generated dynamically by triggering network elements, running queries, and delivering real-time intelligence. Together, APIs, AI-driven demand, and the expansion of 5G and IoT have created a perfect storm for telecom data monetization.
Q: How did Telkomsel build its data monetization business over time?
Alfian Manullang, Telkomsel: Telkomsel launched its data monetization efforts in 2016 as a small unit focused on digital advertising. Over time, this evolved into a strategic pillar within Indonesia’s enterprise ecosystem, driven by offerings such as telco-based credit scoring, mobility insights, and lifestyle analytics.
While the initiative began in 2016, the real inflection point came in 2019 with the launch of a large-scale internal program known as the Darwin Project. Covering both internal and external use cases, this initiative became the backbone of analytics-driven decision-making across the organization and was strongly supported at the board and leadership level.
A key lesson from this journey was that building a data business cannot happen in silos. It requires coordination across business units. The foundation was built by creating a stable and reliable data ingestion layer, resulting in a Customer 360-degree platform that now serves as a single source of truth across the company.
Q: What organizational changes were required to make data monetization sustainable?
Alfian Manullang: Data monetization required a fundamental shift in how teams worked together. Close collaboration was established across business, IT, legal, regulatory compliance, and data governance functions to ensure both speed and accountability.
The business was structured around two main pillars. The B2B pillar focuses on enterprise services such as APIs, insights-as-a-service, and digital advertising. The B2C pillar serves the mass market and leverages shared assets from the B2B side, including risk models and credit scoring.
To maintain consistency and control, two horizontal units were introduced: an Analytics Center of Excellence and a Service Management function. This structure allows business units to remain agile while ensuring centralized oversight for data management, compliance, and operational standards.
Q: How is telecom data packaged into products that enterprises are willing to pay for?
Alfian Manullang: External data monetization offerings are structured into three categories. The first is Insights-as-a-Service, where aggregated data is delivered through reports and dashboards. Enterprises use these insights to understand market dynamics, mobility trends, and competitive positioning. Survey-based insights are layered on top to capture dimensions such as brand perception and sentiment that network data alone cannot provide.
The second category is Insights via APIs, enabling direct integration into enterprise workflows. Telco risk insights are among the most widely adopted use cases, while authentication APIs are expected to grow rapidly as enterprises move away from SMS-based OTPs into more secure and convenient method.
The third category is the Managed Analytic Platform, enabled through data clean room technology. This allows secure collaboration through double-blind data joins between client first-party data and telco data-insight, supporting use cases such as joint risk modeling, data enrichment, and privacy-safe digital advertising without exposing personally identifiable information.
Mobility and positioning data have proven particularly valuable for government agencies and enterprises alike, supporting tourism planning, transportation management, infrastructure development, offline footprints expansion, and regional competition strategy.
Q: What scale of data does Telkomsel manage today to support these use cases?
Alfian Manullang: Data is collected from more than 225 network and IT sources, with approximately 61 petabytes stored daily. Around 300 terabytes are ingested into the data warehouse each day, and close to one trillion internet transactions are processed daily across more than one million mobile network cells.
This governed environment ensures that analysts, engineers, and data scientists operate from a unified and trusted dataset while maintaining strict compliance and privacy standards.
Q: How does Mobileum enable telecom operators to run data as a business?
Miguel Carames, Mobileum: Running data as a business requires a platform designed for scale, efficiency, and real-time operation. The ability to ingest and analyze massive volumes of data across both control and user planes is essential, whether deployed across nationwide networks or smaller environments such as private networks and edge use cases.
The core value lies in efficiently extracting metadata from high-volume data streams and converting it into actionable intelligence delivered via APIs, dashboards, analytics, or AI-ready datasets.
Three primary opportunity areas emerge: internal optimization, such as service assurance and customer experience management; external monetization for enterprises; and government and regulatory use cases. Across all three, success depends on processing large volumes of data in near real time with an efficient compute footprint.
Q: Why is real-time analytics becoming essential for data monetization?
Miguel: Real-time analytics is no longer optional. Customer behavior, applications, and technologies evolve too quickly for insights delivered days or weeks later to remain relevant.
Enterprises increasingly require near real-time data to support AI-driven systems, automation, and dynamic decision-making. The ability to ingest, process, and expose data in real time—while maintaining accuracy, governance, and trust — is critical to making data monetization scalable and commercially viable.
Q: How should operators identify the right use cases to start with?
Raja: Successful data monetization begins with discovery. Raw data on its own has limited value; the challenge lies in structuring and contextualizing that data into insights that enterprises can easily consume.
Dedicated teams are required to work closely with partners to explore and validate use cases. Real-time access to network intelligence — spanning mobility data, location signals, behavioral patterns, and intent indicators — makes these use cases possible.
Starting small is critical. Operators do not need to launch everything at once. Targeted pilots, proofs of concept, and demos should be developed quickly to demonstrate value before scaling across segments such as roaming, IoT, or 5G.
Q: How can telecom operators differentiate themselves from hyperscalers in this space?
Miguel: Telecom operators control network-level data that reflects real-world behavior at scale, under strict regulatory oversight. Hyperscalers cannot replicate this combination.
If operators fail to capitalize on this opportunity, they risk remaining just connectivity providers rather than value creators. Data monetization enables telecom operators to participate meaningfully in the digital economy — extending their role far beyond the network.
Watch the full Mobile World Live webinar to see how Telkomsel and Mobileum are operationalizing data monetization at scale.
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