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A detailed tracker of global telecoms capex, broken down into eight major categories and 32 subcategories. Includes revenue, capex, capital intensity, and capex by category globally and for selected operators from 2019–23.
Global Telecoms Opex Tracker – 2023 Full Year
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A detailed tracker of global telecoms opex, broken down into 11 major categories and 17 subcategories. Includes revenue, opex, opex/revenue, and detailed opex by category globally and for 11 selected companies from 2019–23.
Network Equipment Provider CaaS Survey Analysis
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Telcos are evaluating horizontal cloud architectures to simplify operations and reduce costs. Having explored the container as a service (CaaS) offerings of companies such as Red Hat, SUSE, VMware, and Wind River in previous research, this report examines the CaaS platforms of key network equipment providers (NEPs) that supply network functions to telecom operators. The research is based on a survey of Ericsson, Huawei, Mavenir, Nokia, Oracle, and ZTE.
The Analyst Team

Adam Mackenzie
Follow AnalystSenior Analyst, Service Provider Networks
Adam Mackenzie is the Senior Analyst for Omdia’s Telecoms Network Evolution Intelligence Service and a contributor to Omdia’s Telecoms Strategy Service.
His research focuses on quantitative and qualitative analysis of global telecoms capex and opex trends, as well as forecasting and market analysis of the network API ecosystem. Adam has presented webinars to hundreds of attendees, hosted podcasts, delivered in-person presentations on these topics, and serves as a judge for the GSMA’s GLOMO awards.
Adam’s consultancy projects have helped clients benchmark against industry peers and identify opportunities for improving cost and investment efficiency. He has consulted on projects that have earned recognition via industry awards.
Before joining Omdia in 2022, Adam worked at Analysys Mason within the data, networks, and SMB teams. He holds a Master of Arts in Economics from Heriot-Watt University, Edinburgh.
More Content By Adam Mackenzie
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These are Omdia driven recommendations based on content that is both similar, and has been frequently visited by other users in combination with the content you are currently viewing.
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