Operations | Monitoring | ITSM | DevOps | Cloud

This is a two-part blog series which covers the fundamental questions businesses need to ask for cost-efficient cloud usage. These questions include: While you can find the first four questions answered in the first part of this series, you can also get access to the full list by downloading our latest white paper, How IT leaders can drive more with less: An enterprise guide to technology adoption and cloud usage in a disrupted economy. Now let’s explore the second half of the checklist.

In today’s digital landscape, IT operations face unique challenges and pressures unlike those of the past. Currently, the cost of a service failure for medium and large enterprises is estimated to exceed $100,000 per hour. At present high incident management costs, coupled with the impact on customer satisfaction, present significant challenges for enterprises. To resolve this challenge AI and ML assists in enhancing the overall management of incidents and reducing response times.

RMM—meaning Remote Monitoring and Management—has been a challenge for networks and IT departments since the very first ethernet cable sent a bunch of 1s and 0s in 1973. The challenge, of course, is that no administrator, either network or IT, can be everything everywhere all at once. Although if you’ve ever accidentally rebooted a device you weren’t supposed to, it certainly seems like they are.

Compliance standards are designed to create a robust framework that protects sensitive data from threat actors and ensures organizational integrity. Without them, organizations will be compromising both their IT security and privacy. If you are an IT manager, cybersecurity professional, legal advisor, or your employer has promoted you to be the new compliance officer, your aim is to ensure your organization's technology infrastructure meets regulatory requirements.

The term “observability” is a strange one. We understand its value as a way to describe a sophisticated approach to monitoring complex distributed systems and microservices. But the term is inherently passive (and let’s be honest. It’s a bit of a loaded marketing term). Simply “observing” doesn’t help you solve problems – especially if you are inundated with loads of non-actionable data.

Microsoft Azure offers a variety of solutions for cost management, with Azure Advisor being one of the core features. Azure Advisor provides insights into reservations and right-sizing for various Azure resources. While Microsoft Azure excels at building and deploying solutions, there is often a notable gap when it comes to operations and cost management.

A study estimated that 88% of users will not return to a website if they experience issues. It’s a huge number. And even if this may not be the case for all online platforms during downtime, it indicates how devastating the impact of downtime can be. That’s why prompt communication and efficient user updates are essential. The standard solution is a status page. A public status page can help businesses retain customers by reassuring them that you know the issues and do your best to fix them.

For network operations teams, having remote employees out of sight doesn’t mean they can be out of mind. This is particularly true for remote employees who directly support and interact with customers. In many industries today, organizations may have a significant percentage of employees working in some type of remote fashion, including those who deliver customer-facing services.

When you face issues like reduced throughput or latency spikes in your production applications, determining the cause isn’t always straightforward. These kinds of performance problems might not arise for simple reasons such as under-provisioned resources; often, the root of the problem lies deep within an application’s runtime execution.

Data is central to any business: it powers mission-critical applications, informs business decisions, and supports the growing adoption of AI/ML models. As a result, data volumes are only increasing, and teams rely on engines like Apache Spark and managed platforms like Databricks or Amazon EMR to process this data at scale.