Tom Nolle is founder and principal analyst at Andover Intel, a unique consulting and analysis firm that looks at evolving technologies and applications first from the perspective of the buyer and the buyers’ needs. Tom is a programmer, software architect, and manager of large software and network products by background, and he has been providing consulting services and technology analysis for decades. He’s a regular author of articles on networking, software development, and cloud computing as well as emerging technologies like IoT, AI, and the metaverse.
Net neutrality policies are the most significant regulatory influence on the Internet and data services, and they're the reason why end-to-end Internet QoS isn’t available.
We’re in the infancy of in-house AI with many unknowns and plenty of questions. On the infrastructure front, enterprises agree that AI deployments should be designed as a new cluster with its own fast cluster network.
After the CrowdStrike outage, some enterprises IT teams are rethinking their assumptions about how use of the cloud impacts application reliability.
As the number of security tools deployed continues to climb, it gets more difficult for enterprise security teams to know, with confidence, their overall state of security.
The AI deployments that appeal to enterprise IT teams are those with real, measurable gains – such as AI-driven customer support chatbots, using AI to automate network operations, and self-hosted AI models for business analytics.
Hyped technologies create staffing pressures. And as technology gets more complex, it’s harder to understand what skills are needed and identify who has them.
Open technology appeals to enterprises that want to avoid vendor lock-in. But to deliver real value, open-model technologies need to do more.
Independent broadband and telecom-infrastructure providers could provide connectivity options in areas where service is thin, if enterprise concerns about business viability and technology operations are addressed.
As a subset of distributed computing, edge computing isn’t new, but it exposes an opportunity to distribute latency-sensitive application resources more optimally.
The justification for HPE buying Juniper may be a mundane, economy-of-scale play or a move to gain Juniper's AI networking technology. Or there may be a vision for something more ambitious.
Despite prevailing cynicism, many enterprises find their primary IT vendor to be the most trusted source of network transformation insight, helping to drive both strategy and purchasing.
AI usage will surely drive additional network traffic, but for most enterprises it likely won’t require a major overhaul of the entire data center network.
If not VPNs, then what? These three ideas for connectivity might change the network future by rethinking the traditional service provider concept.
Enterprises see the potential for AI to benefit network management, but progress so far is limited by AI’s ability to work with company-specific network data and the range of devices that AI can see.
Many enterprises want to pursue a unified virtual network strategy, but visibility challenges and pressure to curb cloud costs are complicating 2024 network planning.
The network is the preferred vector of attack. Make it your prime defense.
Look for genuine value when under pressure to implement hyped technologies such as AI-driven network automation, private 5G, open networking and zero trust.
Network outages can often be traced to four error-prone activities: fault analysis and response, configuration changes, scaling and failover, and security policies.
Using generative AI technology for network operations issues can yield results that sound credible but are actually completely wrong.
Enterprises hope to invest in security, operational stability, and better application-delivery performance using technologies that include SD-WAN and AI.
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