Empower your business with modern and adaptive digital capabilities that will generate 42% of revenue every year by combining AIOps and observability.
Table of contents
1. The Challenges and Solutions of Observability and AIOps
1.1 Complexity of Implementation
2. Integration and Cloud Complexity Resolution
3. Which One to Choose: AIOps or Observability?
4. Real-world Example – Dynatrace
Introduction
In the last few years, artificial intelligence for IT operations (AIOps) and observability have been hot topics in the IT operations sector. Organizations are looking for improvements in development and operation processes as these technologies have become more accessible, with various benefits and challenges. With the power of artificial intelligence (AI), machine learning (ML), and natural language processing, IT professionals such as engineers, DevOps, SRE (Site Reliability Engineering) teams, and CIOs can detect and resolve incidents, drive operations, and optimize system performance.
Today, we will understand how AIOps and observability have benefited most enterprises and why they are important for your business.
1. The Challenges and Solutions of Observability and AIOps
AIOps and observability have been critical tools in modern IT operations that have changed the traditional way of managing data. However, IT professionals need help with certain challenges and limitations that can bottleneck the use of these tools properly. Let’s explore some key challenges and their solutions:
1.1. Complexity of Implementation
Implementing observability and AIOps involves a lot of complexity, as these technologies require investment in infrastructure and expertise to implement and maintain. Moreover, a shift in mindset from traditional IT operations, where monitoring and responding to issues are done manually, is also crucial.
Solution: The only way to overcome these challenges is by investing in proper training and infrastructure that supports AIOps and observability, along with continuous organizational improvement and learning. The IT teams should also embrace new technologies and methods to stay updated and competitive in the AI industry.
1.2. AIOps’s Limitation
Even though AIOps is a powerful tool, it has certain limitations as it can partially replace human expertise. On the other hand, ML can recognize trends and patterns, but it struggles with the underlying cause of an issue.
Solution: To solve these complex issues, human expertise is still needed, as small organizations may not require the complexity of AIOps. The IT teams have to intervene to identify patterns and trends with the help of the ML algorithm.
Over the years, companies have digitally evolved into cloud-native architectures that require high maintenance, which can be a challenging task for IT professionals. But every challenge has some solutions that can eliminate the issues and help the digital transformation progress. Let’s see what exactly these cloud complexities are and how to resolve them:
2. Integration and Cloud Complexity Resolution
Through modern cloud systems, businesses can simplify their operations, but the system can be challenging when it is hard to manage, which hampers agility, and hinders rolling out updates. AIOps and the cloud enable applications to process faster, but at the same time, cloud environments face additional complexities. Managing cloud systems needs clarity and the ability to constantly observe all the changes and new elements, but the whole process works remotely.
Thus, observability is needed in the system as it can detect missing elements and problems, which can lead to the threat of losing data. To fight these complex challenges, you and your IT teams need to understand observability and AIOps to get accurate insights from orchestrated tools like Kubernetes with log and message data. Observability can remove the complexity of the performance optimizer with release velocity.
The most debated discussion in the big data space is about the two pillars of AIOps and observability. Both of these platforms have advantages that have uplifted companies, increased profits, and satisfied their customers 24×7. Numerous organizations are confused about which platform is best for AIOps or observability. Let us take a look at which platform will be good for your business:
3. Which One to Choose: AIOps or Observability?
Observability tools have been around for quite some time, as they allow IT professionals to gather metrics, traces, and logs from their systems to provide a holistic view of the incident. On the other hand, AIOps has an active approach to IT operations as it uses AI and ML to analyze data, predict issues, and take measures to prevent incidents from occurring. AIOps helps the IT team save time and resources in operations
Although AIOps and observability can work individually, they complement each other to form a holistic incident management solution. The AIOps need data observability to get good visibility of operational data, while observability depends on AI to auto-resolve since the data collection is huge. The combination of observability and AIOps solutions helps your company understand the tools’s performance and the operational results by resolving errors before hampering the end-user experience.
4. Real-world Example – Dynatrace
Dynatrace is a technology company that has been using Microsoft Azure for a better cloud offering and advancements in its architecture with Azure Kubernetes Services (AKS). The company’s Chief Product Officer, Guido Deinhammer, and his team realized that it needed to reinvent its platform for a dynamic hybrid and multi-cloud world. They decided to build a platform where everyone can set up their data with AIOps to help customers work even in the most complex cloud-native architecture. The reinvented Dynatrace platform, which is built on Azure, has helped the company connect with innovators who want to simplify their cloud journey and accelerate digital transformation. Mike Maciag, Chief Marketing Officer at Dynatrace, says, “We love the flexibility, scalability, and reliability the Azure platform brings.”
Bottom Line
Organizations today are under pressure to keep their IT solutions and infrastructure up and running with minimal downtime. While it is a tough job and has become harder to achieve with modern architecture, AIOPs and observability coming together can help your company enjoy cost-effective solutions to data and IT issues.
Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!
