This report defines the enterprise midmarket segment and addresses its characteristics, challenges, investment plans, and assessing trust and satisfaction with the main communication service provider.
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The Analyst Team

Adam Etherington
Follow AnalystPractice Leader, Cybersecurity
Adam is part of Omdia’s global Digital Enterprise Services practice. He leads international research, advisory, and analysis for cybersecurity and cloud B2B services. A regular speaker at industry events and executive roundtables, Adam thrives on driving deep insight through custom engagements with clients.
Adam joined Omdia in 2020, bringing more than two decades of experience as an executive at some of the world’s largest telecoms and IT services vendors. His experience spans corporate strategy, product marketing, technical operations, market intelligence, sales, and strategic alliance roles. Adam holds a Bachelor of Economics from Macquarie University and an MBA from Deakin University (both in Australia). In 2022, he was awarded the 2021 Brookes Medal for outstanding academic achievement.

Hwee-Xian Tan
Follow AnalystSenior Analyst
Hwee-Xian is a senior analyst with Omdia’s service provider enterprise and wholesale service team. He focuses on the SoHo and SME intelligence services, providing analysis and data to help service providers benefit from market opportunities, and general trend analysis in the Asian ICT services market.
Before joining Omdia (formerly Ovum), Hwee-Xian was a senior analyst for IDC’s Asia/Pacific telecoms group, where he led telecoms services, managed services, unified communications, and collaboration trackers for a broad portfolio of customers across the region. His responsibilities included delivering innovative customized research, project management, and presales product marketing on telecoms operators and infrastructure vendors. Hwee-Xian holds a bachelor’s degree in human ecology and a master’s degree in business administration. He is based in Kuala Lumpur, Malaysia.
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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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