Does the following sound familiar? You have a complex, hybrid and dynamic IT stack – with your cloud infrastructure changing by the minute and your container infrastructure changing by the second. Your monitoring and observability tools provide excellent visibility into your infrastructure, your applications and your services, but the dynamic environment in which they operate causes them to generate large volumes of heterogeneous machine data, with thousands of alerts a minute.

In the present fast-moving digital world, it has become critical for businesses to measure and track their service delivery performance especially the incident management metrics that monitor the uptime of systems, downtime due to outages, and how fast and efficiently issues are resolved because even a slight glitch in the system can cause disruption in the business processes costing millions of dollars.

For many businesses, one of their ultimate goals is to provide excellent services to their customers. But what about the employees facilitating the customer experience? Today’s employees are more reliant than ever on technology to help them do their jobs effectively, which in turn enables them to better serve external customers. And let’s face it, employees are customers, too. How can companies provide a positive employee experience with the technology they use daily?

Digital transformation has transformed the way people interact with brands, software, and making buying decisions. Businesses need to find ways to adapt with these changes and manage the customer journey. Businesses have to continue to provide excellent service and a superior customer experience in these times of transformation. In the case of service-based companies, this requires efficient customer request management.

Root cause analysis can be a difficult challenge when you are troubleshooting complex IT systems. In this blog, we are going to take you through how you can perform root cause analysis on your IT Service Intelligence (ITSI) episodes using machine learning, or more specifically causal inference. The approach shown here is included in the Smart ITSI Insights app for Splunk, with this blog largely detailing how to use the ITSI Episode Analysis dashboard.

Recently, I was asked to comment on “…what skills IT practitioners should plan to learn or improve as we head into 2021.” While a few of those thoughts appeared online I wanted to provide a bit more detail and context here. Before digging in, you should know I’ll answer this question twice. The second answer will be the IT-centric one everyone expects to hear. But the first answer is, in my opinion, more honest, accurate, and relevant.

On Tuesday, January 26, 2021, Apple released iOS and iPadOS 14.4. As with previous releases, each new OS update is available to all Apple devices capable of running iOS and iPadOS 14. If you’re an IT admin, you should evaluate how you want to approach the latest release. Do you want to accelerate or slow down the updates? One concern we often hear about is that the apps that a company is deploying may not be compatible or may not have been tested with the latest release.

In this blog we are going to describe how you can create a notable event policy in IT Service Intelligence (ITSI) that is able to group your events using labels generated by unsupervised machine learning in the Smart ITSI Insights App for Splunk – and don’t worry you don’t have to be a data scientist to read this blog!

In this new series of Now on Now blogs, we share how we, ourselves, use ServiceNow products to transform our operations and create great experiences. We hope our stories will inspire you, our customers, and partners, to achieve even greater business value and resiliency from the Now Platform®. For more information, visit the Now on Now Book of Knowledge.

In traditional IT environments, services to customers are delivered and supported by the organization. A Service Level Agreement (SLA) is created with details like what would be the availability of service be, how reliable the service would be, what penalties can be charged in case of downtime, etc. The internal teams like the network administration team, development team, IT service desk, etc. would then draw up Operational Level Agreements (OLAs) to support the SLA.