There is no leadership without technology leadership, the pandemic revealed. Customer demands are prompting rapid overhauls of how business gets done and a digital-first approach. Cloud migration is an essential requirement for organizations embracing digital.
As per Accenture’s Technology Trend report 2021, 82% of organizations are ramping up their cloud usage in direct response to the pandemic, and 66% of the organizations continue to grow their cloud usage in the near future. Cost is no more a driving factor for cloud adoption. Moving to the cloud meant availability, accessibility, flexibility, scalability, and resilience to changes. Most of our lives revolve around applications for interaction, productivity, transactions, entertainment, and many more. What is your reaction to downtime or low performance in an application? Like many customers, do you have less tolerance for such problems, or do you refresh the application?
Building resilient systems with high uptime is the goal of every business. IT teams always strive to minimize the Mean Time to Resolution (MTTR), but this has become hard with complex IT environments. Traditional Application Performance Monitoring tools (APM) have their limitations in providing a clear root cause analysis, manual dependencies, rapid application updates (a few organizations update hundreds of times in a day), and data overload.
AIOps and Observability for 360-Degree Visibility
Like everywhere, there is a sheer explosion of IT data too, 70% of the technology executives reported an increase in IT ticket and call volumes during the pandemic. IT leaders must measure, monitor, and manage this data to find new ways to manage this application complexity. So how do Observability, Monitoring, and AIOps work together?
Observability is about 360-degree visibility through your applications and combining business metrics with technical data, Monitoring is a top view if things are working perfectly, and AIOps is about deriving insights from that complete visibility.
A sneak peeks into trends about the need for AIOps and Observability:
- IT teams have complete visibility into 11% of their environments, even after investing in 10 different monitoring tools on average, as per a Global survey of CIOs.
- 74% of CIOs reported that they are already using cloud-native technologies and 61% say these change every minute or less, as per a Global survey of CIOs.
- As per Gartner, the number of business leaders relying on AIOps platforms for automated insights may increase by10x by 2024.
Now, the role of DevOps teams is to automate analysis from the observability data to prevent any outages and maintain the uptime for all business-critical apps. So AIOps and Observability both need to be leveraged together for a holistic solution.
Access, Diagnose, Resolve and Prevent Incidents with AIOps
Traditional Monitoring tools result in a few blind spots and disparate data limiting the capability to a few infrastructure segments. AIOps act as a bridge to enable intelligent automation while also predicting incidents and resolving them. All AIOps initiatives leveraging machine learning algorithms aim at providing the right insights across the technology stack – on-premises, cloud, and everywhere. The single source of truth established can enable IT teams to effectively manage their time, productivity while also automating manual data collection processes.
End-to-end Observability to link infrastructure health and performance
Observability is not just about the data collected but it is about Metrics, Traces, and Logs.
How can this help?
What is happening within your application? Metrics allow you to look at the status of the application and its components. You should be able to monitor signals like latency, traffic, errors, and saturation stated in the Google SRE.
Where is it happening? Tracing the path can help you figure out where the application is slowing or what causes the issue.
Why is it happening? All the Logs (Machine and human) can help you analyze if there is an issue.
Elements of Observability are not new, but complete visibility with these elements is gaining attention with complex architectures and infrastructure systems.
Observability and AIOps do not add value without the other
AIOps requires Observability data, powered by machine learning to give a holistic prediction and analysis of application performance. Observability data is nothing without any deep insights, data is huge in the current distributed application ecosystem. With the total Observability information fed to an AIOps system, it can correlate and identify the issues using Machine Learning algorithms. Observability is the solution for complex and distributed Cloud-native systems. Monitoring and AIOps are the catalysts to fuel the data and resolve any issues.
How can you leverage our expertise?
Estimates say that AIOps and Observability market is worth around $17bn per year and Datadog is one among the few players updating their platform with all these capabilities. Datadog is a SaaS monitoring and security platform that combines the three pillars of observability (metrics, traces, and logs) and many more machine learning approaches in one unified platform.
We, at Saxon, are partners with Datadog and many others in the cloud and data eco-system. Embrace your cloud and digital transformation journey with our data and consulting services alongside many partner implementations. Please get in touch with our team to accelerate your digital transformation journey.