As growth from the traditional connectivity business matures, telecom operators are increasingly looking to network data as a new engine of value creation. However, access to large datasets is only one aspect of scaling telecom data monetization. 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.
During the webinar, 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, presented their insights as well as the most important lessons they had learned. Below is an edited Q&A from that discussion. Watch the entire webinar on demand to learn more about the use cases and platform details.
Q: Why is data monetization now such an important growth lever for telecommunications providers?
What has changed to make telecom data more valuable now than in the past?
Raja Hussain, Mobileum: In the majority of markets, the global telecommunications industry is experiencing slower subscriber additions and acquisitions. Consequently, rather than relying solely on expanding connectivity, sustainable growth increasingly depends on maximizing the value of existing assets. One of the most compelling opportunities is data monetization. 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. Demand from the global advertising and market research industries, where insights-as-a-service already represents a $4–5 billion market, is driving much of this expansion. Despite possessing unique behavioral data that cannot be replicated elsewhere, telecom operators have historically been largely absent from this value chain. 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. Offerings like telco-based credit scoring, mobility insights, and lifestyle analytics drove this to become a strategic pillar within Indonesia’s enterprise ecosystem over time. The Darwin Project, a massive internal program, was launched in 2019 and marked the initiative’s real turning point, despite its start in 2016. 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 necessitates collaboration between 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 targets the general public and makes use of shared assets from the B2B side, such as credit scoring and risk models. To maintain consistency and control, two horizontal units were introduced: an Analytics Center of Excellence and a Service Management function. While ensuring centralized oversight for data management, compliance, and operational standards, this structure enables business units to remain agile.
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. These insights help businesses comprehend the dynamics of the market, mobility trends, and competitive positioning. To capture dimensions like brand perception and sentiment that network data alone cannot, survey-based insights are added on top. The second category includes insights that can be integrated directly into enterprise workflows through APIs. 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: To support these use cases, at what scale of data does Telkomsel currently manage to support?
Alfian Manullang: Each day, approximately 61 petabytes of data are stored from more than 225 network and IT sources. The data warehouse receives approximately 300 terabytes each day, and more than one million mobile network cells process close to one trillion internet transactions each day. Analysts, engineers, and data scientists operate from a single, dependable dataset while adhering to stringent privacy and compliance standards in this controlled environment.
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 data monetization increasingly dependent on real-time analytics?
Miguel: Analytics in real time are now mandatory. 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 begin by selecting the appropriate use cases?
Raja: Successful data monetization begins with discovery. The difficulty lies in structuring and contextualizing raw data into insights that businesses can easily consume. Raw data itself has limited value. 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. There is no need for operators 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.
