Today, almost every enterprise organization is adopting some form of cloud computing. These efforts include moving applications or workloads to cloud computing platforms for the first time, merging cloud and native activities into hybrid cloud platforms, or cloud-native application architectures based on microservices and apis.
In all of these variants, traditional tools to help organizations ensure the performance and availability of their services and applications have failed. There is a growing need for more modern tools to provide better observability and insight into what is happening, as well as AI-based AIDS to help ensure continuous availability and best-in-class performance.
There are several issues driving the need for modern tools. First, there is increased complexity for organizations to deploy applications and run workloads on cloud environments.
Even a simple application, such as providing a mobile front end for user accounts, involves back-end elements maintained by the enterprise, databases on the public cloud, connections through user providers, and any of the major mobile operating systems. There are many interdependencies between the various elements, and the business has little control over most of the elements that might affect performance or availability. When something goes wrong, it can take a lot of time to determine the source of the outage. Modern observability tools using AIOps can help automate root cause analysis and speed repair of outages or other problems (MTTR). This can significantly reduce repair/recovery time.
Second, organizations can no longer be passive and take action after a problem has occurred. The traditional IT management approach is to wait for a call from a customer or internal user complaining about service interruptions or poor service quality. AIOps provides a more predictive mode of operation. It supports a proactive approach that can detect increases in dropped or retransmitted packets, as well as other indicators of poor performance, and take corrective actions in real time.
Third, security is more challenging when applications and services are delivered using multiple cloud elements, some of which are outside the control of the enterprise. With modern observability tools, security teams can use AIOps to find anomalies that are precursors to attacks or activities that indicate a data breach. For example, AIOps can be used to alert security teams that large amounts of data are being sent out of the organization through a port that is usually rarely used.
Continuous availability is critical to meeting end-user expectations
Application performance and availability are important to any organization. Employees have certain expectations that the applications and services they need to do their jobs will be available when they need them and will perform well.
Similarly, any customer-facing application or service today faces more demanding user expectations. Because people are used to getting everything immediately when they need it, there is little tolerance for products that are not available or that perform poorly.
Many studies have quantified the impact of any problem on the bottom line. Forty percent of users will abandon websites that take longer than three seconds to load, and 53 percent will abandon mobile apps that won't load in three seconds.
If a website or mobile app doesn't work or doesn't perform well, users abandon the site or app. This leads to a loss of revenue. For example, customers who shop online simply jump to another merchant's website to place a one-time order. If customers have a good experience on the site, they may never come back. So, it's not just a loss on a purchase. That could mean losing a customer for life.
By contrast, slow performance drives business. A classic Google analysis of the problem found that 53% of users abandoned sites that took longer than three seconds to load. In fact, website and mobile app performance is so important that Google now includes both in its SEO rankings. This will also have a serious impact on revenues. Imagine going from Google ranking second on the page to the first page of search results and never seeing the company when customers look for its products or services.
The tools of modern business
Continuous availability and optimized performance are critical today. One way to ensure both is to use observability to complement AIOps, the basic layer that any digital organization needs to run around the clock when running in a cloud environment.
AIOps is the deployment of machine learning to track data from sensors, trajectories, logs, and other sources to prevent internal and external outages, whether through event correlation or anomaly detection. It can also better analyze the causes of events by determining the number of casualties.
The advanced AIOps platform brings together all the data -- metrics, traces, logs, changes, and events -- for fast, accurate reporting and analysis. Unlike past, rule-based techniques, this approach can manipulate portions of evidence and detect problems before they become serious. AIOps also uses machine learning to analyze events, understand how to detect problems early in the event lifecycle, and identify patterns that drive continuous availability.
Given the complexity of cloud-based digital organizations in 2022, with multiple layers of microservices and AD hoc architectures, AIOps is critical to efforts seeking to ensure that applications and services are available and performing well.