Skip to main content

Preventing Outages During the Holiday Shopping Season

Michael Butt

The most destructive root cause of 75 percent of outages during big online events like Black Friday and Cyber Monday are unplanned configuration changes to a system – when IT and Ops teams find something they think might cause a problem and try to fix it immediately, unintentionally creating a much bigger issue for the web or mobile site.


The following are BigPanda's top recommendations for preventing outages during throughout the entire holiday shopping season:

- Identify the systems that are mission critical to your business. Many companies don't and try to treat their entire system as business critical – and this is a mistake. 

- Have a bulletproof plan for your critical services. Once you've identified what your critical services are, know how to keep them up with a bulletproof plan for them. For instance, if Amazon checkout goes down – you need a disaster and recovery plan for this. But if the Recommendation Engine has problems, this is not at the same level of criticality. 

- Tier your services. Having 3-5 tiers makes prioritization and response much easier, quicker and more effective when there is a problem. And make sure you have a backup and failover plan for the highest tier of your services. 

- You don't need failover for everything. IT and Ops teams who try to have failover for everything often discover that they don't have it ready for anything. 

- Don't become overly focused on the components of infrastructure. Make sure you are spending more time and focus on your services. 

- Make sure you have planned for load capacity. Not planning for the sheer volume of people visiting your web or mobile site accounts for 25 percent of outages during big online events. 

- Use a tool that allows you to consolidate your IT data. Implementing an alert correlation platform allows IT and Ops teams to separate signal from noise and focus more on the customer experience by providing a consolidated view of their IT alert data. This allows them to stop being reactive firefighters and become proactive before an issue has the chance to affect the customer.

Michael Butt is Director of Product Marketing at BigPanda.

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

Preventing Outages During the Holiday Shopping Season

Michael Butt

The most destructive root cause of 75 percent of outages during big online events like Black Friday and Cyber Monday are unplanned configuration changes to a system – when IT and Ops teams find something they think might cause a problem and try to fix it immediately, unintentionally creating a much bigger issue for the web or mobile site.


The following are BigPanda's top recommendations for preventing outages during throughout the entire holiday shopping season:

- Identify the systems that are mission critical to your business. Many companies don't and try to treat their entire system as business critical – and this is a mistake. 

- Have a bulletproof plan for your critical services. Once you've identified what your critical services are, know how to keep them up with a bulletproof plan for them. For instance, if Amazon checkout goes down – you need a disaster and recovery plan for this. But if the Recommendation Engine has problems, this is not at the same level of criticality. 

- Tier your services. Having 3-5 tiers makes prioritization and response much easier, quicker and more effective when there is a problem. And make sure you have a backup and failover plan for the highest tier of your services. 

- You don't need failover for everything. IT and Ops teams who try to have failover for everything often discover that they don't have it ready for anything. 

- Don't become overly focused on the components of infrastructure. Make sure you are spending more time and focus on your services. 

- Make sure you have planned for load capacity. Not planning for the sheer volume of people visiting your web or mobile site accounts for 25 percent of outages during big online events. 

- Use a tool that allows you to consolidate your IT data. Implementing an alert correlation platform allows IT and Ops teams to separate signal from noise and focus more on the customer experience by providing a consolidated view of their IT alert data. This allows them to stop being reactive firefighters and become proactive before an issue has the chance to affect the customer.

Michael Butt is Director of Product Marketing at BigPanda.

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...