Craig Matsumoto is a senior research analyst in the 451 Research technology research group within S&P Global Market Intelligence. He covers distributed edge infrastructure, networking and interconnection for the Datacenter Services and Infrastructure channel. In the networking sphere, Craig’s current research focuses on cloud-native networking services, including multicloud connectivity. He also covers distributed edge clouds, such as content delivery networks, and tracks issues related to distributed data and on-demand infrastructure management. Prior to this role, Craig spent more than 20 years in technology journalism. As a lead editor at Light Reading, SDxCentral and EE Times, he followed the evolution of software-defined networking and its relationship to the then-emerging worlds of cloud computing and containerization. His past coverage has also included semiconductors, including semiconductor manufacturing, and telecom optical networking. Craig holds a Bachelor of Science degree in engineering mathematics from the University of California, Berkeley.
Having established its private 5G offering with some large enterprise customers, the company is building out a surrounding ecosystem by adding zero-trust security and evolving its channel partner program via Celona Frequency. These moves aim to push its 5G LAN beyond niche situations to become a full-blown replacement for the enterprise LAN.
AI inferencing has become a focus for enterprises regarding the edge, as reflected in our latest Voice of the Enterprise: Edge Infrastructure & Services survey. This is particularly true for enterprises that have previously deployed AI and now face the possibility of supporting it as well. Meanwhile, data management and security have long been drivers of edge interest and seem poised to grow in importance.
Voice of the Enterprise: Edge Infrastructure & Services focuses on key dynamics and requirements driving the enterprise implementation and use of infrastructure that sits outside of centralized public cloud and datacenters, closer to end users, connected machines and sensors.
The Edge Infrastructure & Services, Sourcing 2025 survey examines the types of edge services and resources organizations are adopting, the types of suppliers they are sourcing from and the consumption models they are adopting through acquisition of these technologies.
Two-part online surveys were conducted between Nov. 7 through Dec. 26, 2024. The main survey consists of ~360 respondents. The margin of error for topline statistics is +/- 5 pts at the 95% confidence level.
AI is upending edge strategies for some enterprises – Highlights from VotE: Edge Infrastructure & Services (Spotlight)
AI inferencing has become a focus for enterprises regarding the edge, as reflected in our latest Voice of the Enterprise: Edge Infrastructure & Services survey. This is particularly true for enterprises that have previously deployed AI and now face the possibility of supporting it as well. Meanwhile, data management and security have long been drivers of edge interest and seem poised to grow in importance.
Crosstabs (Excel tables | Download link in sidebar to the right) – Crosstabs of complete worldwide and regional survey results datasets, including key metrics and segmentation.
Charts and Figures (PowerPoint charts | Navigate charts in sidebar to the left | Download link in sidebar to the right) – PowerPoint slide deck of complete top-line survey results in charts and figures.
Data Insights Deck (PowerPoint charts | Coming soon) – PowerPoint slide deck of advanced charts supporting deeper analysis and insight.
While Syntiant has over 100 million products in the field using its AI technology, the recent acquisition of the Knowles Consumer MEMS microphone business provides a steady source of revenue to pursue the growing universe of far-edge AI opportunities and could pave the way to an IPO.
As the automotive industry increasingly integrates connected and autonomous vehicle technologies, the need for robust network connectivity and edge computing has become paramount. The Automotive Edge Computing Consortium seeks to address this need by fostering collaboration among industry leaders, technology providers and research institutions.
The company provides a platform for developing and deploying AI models in PCs, and even more constrained edge environments such as microcontrollers, making AI inferencing possible on otherwise unintelligent devices. Stream Analyze touts itself as a smaller and faster alternative to solutions offered by the public cloud.
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