Everything is Green but Users are Complaining. What's Your Next Move?

Chris Siakos

This is a classic scenario which continues to plague Network, Application and IT leaderships teams. The toolsets tell a good story showing "green" yet the complaints keep coming. Lots of questions, very few answers!

The network team is typically the first to get blamed and the default mode is to prove its innocence. Meanwhile leadership continues to receive complaints, and back and forth troubleshooting communications between network and app teams consume valuable and smart human capital for days. We're all aware of the technical intra-company and inter-company relationship debt that these situations bring with them. Is there sufficient collaboration to make sure everyone is on the same page?

While teams frantically drive towards problem detection, user complaints all of a sudden stop for no apparent reason and life continues until the next performance event. If problem detection drags on for days, users lose faith in the IT organization and stop complaining. How can you analyze data in real-time and win back their confidence?

Many of these challenges stem from loosely inferring user experience levels by looking at network performance (NPMD) tools and cobbling together data from a variety of different tools. Teams are inundated with telemetry data which not only prove pointless for this problem but makes their job even harder. What about the APM or vendor-specific application monitoring tools? Great tools to monitor performance within the application — what about the network and the end users?

Then we have the move to the cloud — Yet Another Tool for Cloud Monitoring? The "swivel chair effect" goes to a whole new level. If you're thinking that there is a gap somewhere which hinders speed to detecting user experience issues then you're right. It's a gap that's about to get bigger.

We call this the "application intelligence" gap. This is the intelligence gap between network, application and cloud which makes performance problem detection very challenging and expensive. When users complain about application experience, what do you do? Is the problem with the network, the application or the cloud?

Detecting and diagnosing end-to-end user experience issues is hard and gets harder with cloud and serverless computing. Establishing the right foundation starts with bringing Network, Application and Cloud teams together on a common framework. A framework that provides operational performance intelligence to show what matters very quickly so teams can detect fast, predict and avoid performance issues and focus on what they do best. Are you ready to break the silos and foster collaboration?

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Everything is Green but Users are Complaining. What's Your Next Move?

Chris Siakos

This is a classic scenario which continues to plague Network, Application and IT leaderships teams. The toolsets tell a good story showing "green" yet the complaints keep coming. Lots of questions, very few answers!

The network team is typically the first to get blamed and the default mode is to prove its innocence. Meanwhile leadership continues to receive complaints, and back and forth troubleshooting communications between network and app teams consume valuable and smart human capital for days. We're all aware of the technical intra-company and inter-company relationship debt that these situations bring with them. Is there sufficient collaboration to make sure everyone is on the same page?

While teams frantically drive towards problem detection, user complaints all of a sudden stop for no apparent reason and life continues until the next performance event. If problem detection drags on for days, users lose faith in the IT organization and stop complaining. How can you analyze data in real-time and win back their confidence?

Many of these challenges stem from loosely inferring user experience levels by looking at network performance (NPMD) tools and cobbling together data from a variety of different tools. Teams are inundated with telemetry data which not only prove pointless for this problem but makes their job even harder. What about the APM or vendor-specific application monitoring tools? Great tools to monitor performance within the application — what about the network and the end users?

Then we have the move to the cloud — Yet Another Tool for Cloud Monitoring? The "swivel chair effect" goes to a whole new level. If you're thinking that there is a gap somewhere which hinders speed to detecting user experience issues then you're right. It's a gap that's about to get bigger.

We call this the "application intelligence" gap. This is the intelligence gap between network, application and cloud which makes performance problem detection very challenging and expensive. When users complain about application experience, what do you do? Is the problem with the network, the application or the cloud?

Detecting and diagnosing end-to-end user experience issues is hard and gets harder with cloud and serverless computing. Establishing the right foundation starts with bringing Network, Application and Cloud teams together on a common framework. A framework that provides operational performance intelligence to show what matters very quickly so teams can detect fast, predict and avoid performance issues and focus on what they do best. Are you ready to break the silos and foster collaboration?

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Network Data Problem Slowing Down Enterprise AIOps

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

The Governance Gap Behind AI's Infrastructure Problem

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

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