Graph Database for Telecom Networks Market Outlook

According to our latest research, the global market size for Graph Database for Telecom Networks in 2024 stands at USD 1.47 billion, with a robust compound annual growth rate (CAGR) of 22.1% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 7.02 billion. This remarkable growth is primarily fueled by the increasing complexity of telecom networks, the proliferation of connected devices, and the urgent need for real-time data processing and analytics to drive operational efficiency and competitive differentiation. As per our latest research, the adoption of graph database technologies is accelerating in the telecom sector, enabling organizations to address challenges related to data interconnectivity, fraud detection, and network optimization.

One of the most significant growth factors in the Graph Database for Telecom Networks market is the exponential rise in data generated by telecom networks, driven by the widespread adoption of 5G technology, IoT devices, and digital transformation initiatives. Telecom operators are increasingly leveraging graph databases to model and manage complex relationships between network elements, subscribers, and services. These databases enable organizations to gain a holistic view of their networks, streamline network management processes, and quickly identify and resolve issues. The ability of graph databases to handle dynamic, highly connected data structures gives telecom operators a strategic advantage in managing network topologies, optimizing routing, and delivering superior customer experiences. As the volume and complexity of telecom data continue to surge, the demand for advanced graph database solutions is expected to grow at a rapid pace, underpinning the market's impressive CAGR.

Another critical driver for the Graph Database for Telecom Networks market is the increasing emphasis on fraud detection and prevention. Telecom networks are frequent targets for sophisticated fraud schemes, including subscription fraud, SIM card cloning, and international revenue share fraud. Traditional relational databases often fall short in detecting complex fraud patterns that span multiple entities and relationships. In contrast, graph databases excel at uncovering hidden connections and suspicious activity in real-time, enabling telecom operators to proactively mitigate risks and reduce financial losses. By integrating graph analytics with machine learning algorithms, telecom companies can enhance their ability to detect anomalies, improve security, and comply with regulatory requirements. This growing need for advanced fraud detection capabilities is a key factor propelling the adoption of graph database technologies in the telecom industry.

The evolution of customer analytics and personalized service offerings is also playing a pivotal role in driving the Graph Database for Telecom Networks market. Telecom operators are increasingly focused on delivering tailored services and experiences to retain customers and increase revenue. Graph databases empower organizations to analyze customer interactions, preferences, and behavior across multiple touchpoints, enabling hyper-personalized marketing, targeted upselling, and improved customer support. The ability to map and analyze complex customer journeys in real-time allows telecom companies to identify high-value segments, predict churn, and design effective retention strategies. As customer expectations continue to rise, the adoption of graph database solutions for advanced analytics and personalized service delivery is expected to accelerate, further fueling market expansion.

Regionally, the Graph Database for Telecom Networks market is witnessing significant growth in Asia Pacific, North America, and Europe, with emerging economies in Latin America and the Middle East & Africa also showing considerable potential. North America currently leads the market, driven by the presence of major telecom operators, advanced network infrastructure, and early adoption of cutting-edge technologies. Asia Pacific is projected to exhibit the highest CAGR during the forecast period, supported by rapid digitalization, expanding mobile subscriber base, and substantial investments in 5G and IoT deployments. Europe remains a key market, benefiting from regulatory initiatives, strong R&D capabilities, and a mature telecom ecosystem. As telecom operators across regions strive to modernize their netw

The World Telecommunications/ICT Indicators database (WTID) contains time series data for the years 1960, 1965, 1970 and annually from 1975 to 2018 for more than 180 telecommunication/ICT statistics covering fixed-telephone networks, mobile-cellular telephone subscriptions, quality of service, Internet (including fixed- and mobile-broadband subscription data), traffic, staff, prices, revenue, investment and statistics on ICT access and use by households and individuals. Selected demographic, macroeconomic and broadcasting statistics are also included. Notes including metadata are also included.

Edge Database for Telecom Market Outlook

According to our latest research, the global Edge Database for Telecom market size reached USD 2.14 billion in 2024, driven by the escalating need for real-time data processing and network optimization within the telecommunications sector. The market is experiencing robust momentum and is projected to grow at a compound annual growth rate (CAGR) of 18.7% from 2025 to 2033, reaching an estimated USD 10.82 billion by 2033. Key growth factors include the proliferation of 5G networks, the exponential rise in connected devices and IoT applications, and the increasing demand for ultra-low latency services across global telecom infrastructures.

One of the primary growth drivers for the Edge Database for Telecom market is the rapid deployment of 5G technology worldwide. As telecom operators race to upgrade their networks, the need for distributed, high-performance databases at the network edge becomes paramount. Edge databases enable telecom providers to process and analyze vast volumes of data closer to the source, reducing latency and improving the quality of service for end-users. This is particularly crucial for applications such as autonomous vehicles, augmented reality, and mission-critical IoT solutions, where real-time data access and decision-making are essential. Moreover, the increasing adoption of network virtualization and software-defined networking (SDN) further accelerates the integration of edge databases, as these technologies require agile, scalable, and resilient data management solutions.

Another significant growth factor is the surge in data traffic generated by mobile devices, IoT sensors, and smart applications. Telecom operators are under immense pressure to manage, store, and analyze this data efficiently to ensure seamless connectivity and personalized customer experiences. Edge databases provide the necessary infrastructure to cache, process, and analyze data at local edge nodes, reducing the load on central data centers and minimizing backhaul costs. Additionally, the integration of artificial intelligence and machine learning at the edge enables advanced analytics and automation, empowering telecom providers to optimize network performance, detect anomalies, and deliver value-added services to their customers.

Furthermore, the evolving regulatory landscape and growing concerns over data privacy and sovereignty are influencing telecom operators to adopt edge database solutions. By processing and storing sensitive data locally, telecom companies can comply with regional data protection regulations and minimize the risk of data breaches. This localized approach not only enhances security but also improves service reliability, as edge databases can operate independently in the event of network disruptions. The convergence of these factors—technological advancements, regulatory requirements, and customer expectations—continues to propel the Edge Database for Telecom market forward, fostering innovation and competitive differentiation across the industry.

From a regional perspective, North America currently leads the global Edge Database for Telecom market, accounting for the largest revenue share in 2024, followed closely by Asia Pacific and Europe. The presence of major telecom operators, advanced digital infrastructure, and early adoption of edge computing technologies contribute to North America’s dominance. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by massive investments in 5G rollouts, expanding mobile subscriber base, and government initiatives to build smart cities and digital economies. Europe, Latin America, and the Middle East & Africa are also witnessing steady adoption, propelled by increasing mobile penetration and strategic collaborations between telecom providers and technology vendors.

Component Analysis

The Edge Database for Telecom market is segmented by component into Software, Hardware, and Services, each playing a vital role in enabling edge data management and analyti

Edge Database for Telecom Market Outlook

According to our latest research, the global Edge Database for Telecom market size reached USD 1.42 billion in 2024, demonstrating robust adoption across the telecom sector. The market is expected to experience a CAGR of 17.8% from 2025 to 2033, projecting a value of approximately USD 7.12 billion by 2033. This remarkable growth is primarily driven by the surge in data traffic, the proliferation of 5G networks, and the urgent need for real-time data processing at the network edge, which collectively underscore the increasing reliance on edge database solutions within the telecom industry.

One of the most significant growth factors for the edge database for telecom market is the exponential increase in connected devices and Internet of Things (IoT) deployments. Telecom operators are under intense pressure to manage and process massive volumes of data generated from diverse endpoints, including mobile devices, sensors, and smart infrastructure. Edge databases enable telecom providers to process, analyze, and act on data locally, reducing latency and improving the responsiveness of network services. This capability is particularly vital for applications like autonomous vehicles, remote healthcare, and augmented reality, where milliseconds matter. The shift towards decentralized data architectures is fundamentally transforming telecom infrastructure, making edge databases a critical investment for future-ready networks.

Another driving force behind the expansion of the edge database for telecom market is the accelerating rollout of 5G networks worldwide. 5G technology promises ultra-low latency, high bandwidth, and massive device connectivity. However, realizing these benefits requires telecom operators to move data processing closer to the source of data generation. Edge databases provide the backbone for such distributed computing models by supporting real-time analytics, subscriber data management, and network optimization at the edge. As telecom companies race to differentiate their offerings and deliver superior customer experiences, the adoption of edge database solutions is becoming a strategic imperative. This trend is further amplified by the increasing demand for personalized content delivery and network slicing, both of which rely heavily on localized, real-time data processing.

The third major growth factor is the rising focus on network security and regulatory compliance. As data privacy regulations become more stringent and cyber threats more sophisticated, telecom operators are seeking solutions that minimize data exposure and reduce the attack surface. Edge databases facilitate localized data processing and storage, ensuring sensitive information remains within specific geographic boundaries and complies with data sovereignty laws. Moreover, by processing data at the edge, telecom providers can implement advanced security protocols and threat detection mechanisms closer to the source, thereby enhancing overall network security. This trend is particularly pronounced in regions with strict data protection regulations, such as Europe and parts of Asia Pacific, further fueling the adoption of edge database solutions.

From a regional perspective, North America currently leads the edge database for telecom market, followed closely by Asia Pacific and Europe. The dominance of North America is attributed to the early adoption of 5G technology, significant investments in edge computing infrastructure, and the presence of major telecom operators and technology vendors. Asia Pacific, on the other hand, is witnessing the fastest growth, driven by large-scale digital transformation initiatives, rapid urbanization, and the expansion of IoT ecosystems in countries like China, Japan, and South Korea. Europe remains a key market due to its advanced telecom infrastructure and strong regulatory focus on data privacy and security. Latin America and the Middle East & Africa are also emerging as potential growth regions, supported by increasing mobile penetration and ongoing network modernization efforts.

Component Analysis

The edge database for telecom market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall market dynamics. The software segment encompasses database management systems, analytics engines, and security modules that enable telecom operators to efficiently manage and process data at the edge. As the demand for real-time

The Big Data in Telecom market is experiencing robust growth, driven by the exponential increase in mobile data traffic, the rise of 5G networks, and the increasing need for personalized customer experiences. The market is segmented by application (IoT, Retail, Media, Financial Services, Pharmaceuticals, and Others) and by type of database (Hadoop, NoSQL, MPP Databases, and Others). Key players like Accenture, Amazon, Cisco, IBM, Microsoft, and Oracle are heavily invested in this space, offering a range of solutions from data analytics platforms to cloud-based services. The North American market currently holds a significant share, followed by Europe and Asia Pacific. However, developing regions in Asia Pacific and the Middle East & Africa are exhibiting high growth potential, driven by increasing digital adoption and infrastructure investments. The market's Compound Annual Growth Rate (CAGR) is estimated to be around 15% during the forecast period (2025-2033), indicating substantial future expansion. This growth is fueled by the continuous demand for advanced analytics to optimize network performance, improve customer retention, and develop innovative services such as personalized offers and predictive maintenance. The market faces some restraints, including concerns regarding data security and privacy, as well as the complexities associated with integrating big data solutions into existing telecom infrastructure. Nevertheless, the overall outlook remains optimistic, with the market poised for significant expansion over the next decade. The adoption of advanced technologies such as AI and machine learning further fuels market growth by enabling more sophisticated data analysis and insightful business decisions. The forecast period (2025-2033) suggests a consistent upward trajectory. Several factors contribute to this projection: the continued expansion of 5G coverage globally will generate vast amounts of data requiring sophisticated analytics solutions. Furthermore, the increasing adoption of IoT devices in the telecom sector, coupled with the rise of cloud-based data storage and processing solutions, creates a fertile ground for further market growth. Competition among major players will likely intensify, leading to innovations in pricing models, service offerings, and technological advancements. The integration of big data analytics with other emerging technologies such as blockchain and edge computing will likely shape the future landscape of the market, providing opportunities for both established players and new entrants to contribute to this dynamic space.

The World Telecommunication/ICT Indicators Database contains time series data for the years 1960, 1965, 1970 and annually from 1975 to 2020 for more than 180 telecommunication/ICT statistics covering fixed-telephone networks, mobile-cellular telephone subscriptions, quality of service, Internet (including fixed- and mobile-broadband subscription data), traffic, staff, prices, revenue, investment and statistics on ICT access and use by households and individuals. Selected demographic, macroeconomic and broadcasting statistics are also included. Data are available for over 200 economies. However, it should be noted that since ITU relies primarily on official economy data, availability of data for the different indicators and years varies. Notes explaining data exceptions are also included. The data are collected from an annual questionnaire sent to official economy contacts, usually the regulatory authority or the ministry in charge of telecommunication and ICT. Additional data are obtained from reports provided by telecommunication ministries, regulators and operators and from ITU staff reports. In some cases, estimates are made by ITU staff; these are noted in the database.

Connecticut General Statutes §16-50dd requires the Connecticut Siting Council to develop, maintain and update on a quarterly basis a Statewide Telecommunications Coverage Database that includes the location, type and height of all telecommunications towers and antennas in the state. Although the Siting Council has made every effort to ensure that this database is as inclusive as possible, it makes no representation that all telecommunications sites in the state are included in this listing. As the Siting Council becomes aware of sites that are unlisted, it takes steps to add these sites to the listing. The Council also welcomes corrections or additions to this database

Dataset

This dataset was created by Chetan K

Contents

The World Telecommunication/ICT Indicators database contains time series data for more than 180 telecommunication/ICT (Information and Communication Technologies) statistics. It covers fixed-telephone networks, mobile-cellular telephone subscriptions, quality of service, Internet (including fixed- and mobile-broadband subscription data), traffic, staff, prices, revenue, investment and statistics on ICT access and use by households and individuals. Selected demographic, macroeconomic and broadcasting statistics are also included. The data is for the years 1960, 1965, 1970 and annually from 1975 to 2017. The WTI Database also includes: Economy yearbook pages featuring in the Yearbook of Statistics. These pages show data in economy tables allowing readers to view the evolution of telecommunication services by economy. Statistics are provided for the ten-year period 2007-2017. The latest (2017) data on ICT access and use by households and individuals. Data are presented in tables and broken down by socio-demographic variables, such as age, sex, income and education level etc. Please note: The World Telecommunication/ICT Indicators database is a relational database which must be used with the associated Software Application. In order to search and extract data from the Data file, users will need to download and install the Application and the Data file to the same folder on their personal computers. The database must be installed by first launching the executable (ending in “.exe”) file.

Customer Contacts Database Information showing customer contacts to UK Contact Centres and One Stop Centres by month. Dataset Guidance:

  F2F = Face-to-face (One Stop Centre) 
  CC = Contact centre (Call centre/telephone) 

Contains customer contact details, support details and support emails. Data collection Published by Intellectual Property Office.

Customer contact data helps support the provision of the corporate data as well as assisting customers with their dealings with IPO. For example contacting customers regarding - acceptance or rejection of services, patents or designs, usage of products and telecommunication services provided by big brands such, Sky, BT, Vodafone, Virginmedia & more.

This dictionary gathers different disciplines and topics such as: PABX (Private Automatic Branch eXchange), public telephone exchange, switch, microwave radio system, satellites, multiplexer, signalling, printed circuit, telephony, etc.It also contains many synonyms and abbreviations in both languages, as well as meaning, case or applications for polysemic terms.Languages : French - English (GB, US), English (GB, US) - FrenchNumber of entries: 89,200Number of terms per language: about -10% with respect to the number of entries (i.e. ca. 80,000 terms)Disciplines: about 185Format: The database will be delivered as .DBF files, sorted alphabetically in French and English A viewer is also available upon demand. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by abbreviation.Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

The OECD broadband database provides access to a range of broadband-related statistics gathered by the OECD. Policy makers must examine a range of indicators which reflect the status of individual broadband markets in the OECD. For further details, please refer to https://www.oecd.org/digital/broadband/broadband-statistics/

Context and Methodology

The dataset originates from the research domain of Customer Churn Prediction in the Telecom Industry. It was created as part of the project "Data-Driven Churn Prediction: ML Solutions for the Telecom Industry," completed within the Data Stewardship course (Master programme Data Science, TU Wien).

The primary purpose of this dataset is to support machine learning model development for predicting customer churn based on customer demographics, service usage, and account information.
The dataset enables the training, testing, and evaluation of classification algorithms, allowing researchers and practitioners to explore techniques for customer retention optimization.

The dataset was originally obtained from the IBM Accelerator Catalog and adapted for academic use. It was uploaded to TU Wien’s DBRepo test system and accessed via SQLAlchemy connections to the MariaDB environment.

Technical Details

The dataset has a tabular structure and was initially stored in CSV format. It contains:

• Rows: 7,043 customer records

• Columns: 21 features including customer attributes (gender, senior citizen status, partner status), account information (tenure, contract type, payment method), service usage (internet service, streaming TV, tech support), and the target variable (Churn: Yes/No).

Naming Convention:

• The table in the database is named telco_customer_churn_data.

Software Requirements:

• To open and work with the dataset, any standard database client or programming language supporting MariaDB connections can be used (e.g., Python etc).

• For machine learning applications, libraries such as pandas, scikit-learn, and joblib are typically used.

Additional Resources:

• Source code for data loading, preprocessing, model training, and evaluation is available at the associated GitHub repository: https://github.com/nazerum/fair-ml-customer-churn

Further Details

When reusing the dataset, users should be aware:

• Licensing: The dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

• Use Case Suitability: The dataset is best suited for classification tasks, particularly binary classification (churn vs. no churn).

• Metadata Standards: Metadata describing the dataset adheres to FAIR principles and is supplemented by CodeMeta and Croissant standards for improved interoperability.

The Organisation for Economic Co-operation and Development (OECD) Telecommunications and Internet Statistics covers provides time series of telecommunications and economic indicators such as network dimension, revenues, investment and employment for OECD countries from 1980 onwards. These data allows evaluation of policy performance in this sector across the OECD region.

The OECD Telecommunications and Internet Statistics is made up of the Telecommunications Database and Broadband database.

The Telecommunications Database provides both telecommunication and economic time-series data covering thirty OECD Member countries from 1980 onwards. The broadband database on information and communication technology (ICT) contains indicators on wireless and fixed broadband usage, access and diffusion to OECD households, individuals and businesses.

These data were first provided by the UK Data Service in March 2015.

This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process.

You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below:

Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. The 16th International Conference on Quality of Multimedia Experience (QoMEX). IEEE. DOI: 10.1109/QoMEX61742.2024.10598264

BibTeX:

@inproceedings{zerman2024spherical,
title = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},
author = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},
booktitle = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},
year = {2024},
organization = {IEEE},
doi = {10.1109/QoMEX61742.2024.10598264}
}

This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included.

N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09.

High Frequency Indicator: The dataset contains year- and month-wise All India compiled data from the year 2011 to till date on the total number of active wireless telecom subscribers, based on the Visitor Location Register (VLR) data published by TRAI

The VLR is a temporary storage base system, where mobile users data of different mobile network areas is stored. Like Home Location Register (HLR), the VLR also collects the mobile usage data of users. But, unlike HLR, it does not store the users data permanently. It is mainly used to temporarily store the database of mobile users, especially roaming users, within a mobile switching center’s (MSC) location area and reduce the load of information being fed into HLR system at a time.

Note:

The TRAI presents VLR subscriber data based on the active subscribers in VLR range, on the date of Peak subscriber number in VLR of the particular month for which the data is being collected. This data has to be taken as the switches having the purge time of not more than 72 hours.

description: The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council. This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council. Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein. The public is advised that the records upon which the information in this database is based are kept in the Siting Councils offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday. Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation. Cingular: SNET, SCLP, and New Cingular after merger with AT&T T-Mobile: Omni (Omnipoint), VoiceStream Verizon: BAM, Cellco AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T Nextel: Smart SMR; abstract: The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council. This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council. Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein. The public is advised that the records upon which the information in this database is based are kept in the Siting Councils offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday. Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation. Cingular: SNET, SCLP, and New Cingular after merger with AT&T T-Mobile: Omni (Omnipoint), VoiceStream Verizon: BAM, Cellco AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T Nextel: Smart SMR

Average Monthly Revenue: Per Telephone Number: Public Phone data was reported at 29.000 THB in Sep 2019. This records an increase from the previous number of 24.000 THB for Jun 2019. Average Monthly Revenue: Per Telephone Number: Public Phone data is updated quarterly, averaging 26.000 THB from Mar 2016 (Median) to Sep 2019, with 15 observations. The data reached an all-time high of 60.000 THB in Mar 2016 and a record low of 18.000 THB in Jun 2018. Average Monthly Revenue: Per Telephone Number: Public Phone data remains active status in CEIC and is reported by Office of The National Broadcasting and Telecommunications Commission. The data is categorized under Global Database’s Thailand – Table TH.TB006: Telecommunication Statistics: Office of The National Broadcasting and Telecommunications Commission .

This dataset is about book series. It has 1 row and is filtered where the books is CD-ROM and online computing, electronics and telecommunications databases. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Database Automation Market size was worth around USD 1.74 Billion in 2023 and is predicted to grow to around USD 16.52 Billion by 2032