Dive into the world of AIOps with our comprehensive guide exploring the leading AIOps platforms for seamless IT operations.
Table of Contents
1. Understanding the Essence of AIOps Solutions
1.1. Automation in IT Operations
1.2. Data Ingestion and Enrichment Capability
3. Implementation of Best AIOps Practice
3.1. Investing in the Correct AIOps Platform
3.2. Regular Monitoring of the AIOps Platform
Introduction
In the dynamic environment of IT operations, the emergence of AIOps (artificial intelligence for IT operations) has changed how IT professionals manage and optimize their systems. AIOps represent a radical shift in the integration of artificial intelligence (AI) and machine learning (ML) into conventional IT operations to enhance productivity, identify issues, and automate responses. The role of AIOps platforms is to serve as the control center for IT professionals like IT Ops and SRE teams by offering suite tools and functions to utilize the power of AI in their day-to-day activities.
In this article, we will examine the key features, the top six trending AIOpS platforms, and how these platforms will help your organization with the help of some interesting real-life case studies.
1. Understanding the Essence of AIOps Solutions
AIOps has evolved analytics in IT operations and benefited numerous businesses by helping them make informed decisions. Here are two essential key features that will help in improving IT efficiency and system development. Let’s take a look:
1.1. Automation in IT Operations
AIOps has a lot to offer, like improving key metrics and helping businesses survive and develop in an increasingly digitized environment. Thus, AIOps solutions help your business by offering solutions like desk automation, anomaly detection, predictive maintenance, and other functionalities.
1.2. Data Ingestion and Enrichment Capability
AIOps solutions are needed to break down and ingest the information from different sources, like networks, clouds, applications, etc., to understand the current landscape of the IT structure. These solutions need to be analyzed to streamline a wide range of data from different timelines. Moreover, the AIOps platforms enrich logs or events with tags and metadata that provide contexts for generating time series.
The key features of AIOps have helped in fetching good results in your business if the correct platforms are used which gives better insights, from collecting data to spotting real-time issues. Let’s take a look at how the AIOps platform or tool transforms medium and large companies.
2. Top Six AIOps Platforms
AIOps platforms are mainly used for monitoring, incident management, service desk, and log analysis solutions. These platforms leverage the power of AI and ML to analyze the huge volume of data and serve it on a centralized platform so that IT professionals and their teams can access it. Here are the top 7 AIOps platforms for you:
2.1. IBM Instana
IBM Instana, is a single fully automated application performance management (APM) solution developed for challenges in managing microservice and cloud-native applications. IBM Instana can address unique features that modern Dev+Ops teams might face.
2.1.1. Key Features
- Automated discovery and monitoring
- Application perspectives
- Unbounded analytics
- Root cause analysis
- Pipeline feedback
2.1.2. Case Study
Crédit Mutuel Arkéa, a mutual cooperative bank from France has deployed IBM Instana in its IT division to control tech-related issues and make communication between Dev and Ops more fluid. With the help of IBM Instana, Observability has resulted in advances in application stability and the detection of problems by allowing the pinpointing of the source, which helps the developers work on it.
2.2. BigPanda
BigPanda, a cloud-based IMP (incident management platform), helps IT operators detect issues in their system in real-time, report the root cause of the incident, and provide generative AI suggestions.
2.2.1. Key Features
- Smart ticketing
- Custom monitoring views
- Quicker insights
- Alert centralization
- Centralized visibility
2.2.2. Case Study
Autodesk, a leading software company has implemented BigPanda for monitoring its applications and infrastructure. After implementation, Autodesk has witnessed a reduction of incidents by 69%, which directly contributed to operational efficiency and improved ticketing processes.
2.3. PagerDuty
PagerDuty is a SaaS-based platform for developers, DevOps, and IT operations to resolve business-impacted incidents for a better customer experience. This platform offers more than 150 monitoring tools, ticketing tools, and deployment tools that help the user get alerts on their devices.
2.3.1. Key Features
- Reduce the time to repair the system
- Notifying the right person to address the incident
- Customized incident alerts
- Filtering out the notifications
- Reduces duplicate alerts
2.3.2. Case Study
BlaBlaCar, a coach service in Europe, was suffering from challenges like delays in response time, false positive alerts that decreased the efficiency of on-call engineers, and more time consumption in logging on-call hours. After implementing PagerDuty, BlaBlaCar has achieved the ability to respond in real-time with the correct resources and improve operational efficiency.
2.4. Site24x7
Site24x7, a one-stop cloud-scale application solution for IT and DevOps helps monitor the performance of the website with the help of customized KPIs built for your business. The platform is mostly used by small and medium businesses for its instant alerting capabilities.
2.4.1. Key Features
- Real-Time Monitoring
- Trace individual transactions.
- Maintenance scheduling
- Bandwidth monitoring
- Uptime monitoring
2.4.2. Case Study
The service desk analysts at First Quantum Minerals Ltd., a mining and metals company, adopted and tested Site24x7 for monitoring ITSM around the clock. The company found the platform’s ability to notify the service delivery managers to utilize the platform without having to log in to the server system while identifying the root cause analysis of the operations during the event of downtime.
2.5. IBM Turbonomic
IBM Turbonomic, a hybrid cloud cost optimization platform used by IT professionals to automate critical actions in real-time without human intervention, especially when computing, storing, or networking resources into apps to build a stronger ROI.
2.5.1. Key Features
- Automated, intelligent decisions
- Full-stack virtual visualization
- AI-generated real-time insights
2.5.2. Case Study
Carhartt, an apparel company known for making heavy-duty working clothes for numerous manufacturing industries. It was founded in Detroit in 1889. Carhartt has been generating billions of dollars in revenue every year; however, the company faces sudden business surge challenges during Black Friday sales, which create issues in keeping track of its inventory and loyalty systems. After using IBM Turbonomic, the software helps its hybrid cloud infrastructure handle the drastic new spikes in demand during festive season sales. Carhartt achieved a 15% improvement in resource utilization and witnessed an improvement in efficiency of 45%.
2.6. Coralogix
Coralogix, a cloud-based streaming data platform for software and DevOps engineers to access real-time data insights and long-term trend analysis with metrics without relying on storage.
2.6.1. Key Features
- Data Analysis
- Compliance Reporting
- Data Visualization
- Data Integration
2.6.2. Case Study
Monday.com, a cloud-based work OS that manages teams’ productivity, projects, and daily tasks. The company was facing challenges saving the different log locations as there was no backtracking of data flow. The problem was solved after implementing Coralogix, which can enter data into the system and automatically start searching. Now, the company can see the full journey logs without facing any complexity in how the logging infrastructure is built.
3. Implementation of Best AIOps Practice
To achieve good results, your company must not just concentrate on understanding how and which AIOps platforms will benefit your team but also focus on the best practices that help in achieving outstanding results in present and future scenarios. Here are some of the practices:
3.1. Investing in the Correct AIOps Platform
AIOps is a combination of AI and IT processes to make work simple; however, it is only possible if the integrated tools have most of the features like data sources, business applications, and understanding of the resources. The AIOps platforms make your IT ecosystem better and fine-tune your requirements according to your needs.
3.2. Regular Monitoring of the AIOps Platform
Your team should monitor the platform and network to keep an eye on their performance, as it helps in implementing new AIOps practices. There are monitoring tools that feature supportive analytics and give a deeper dive into ML and AI performances. These will help in noticing possible issues and offering corrective actions, if any.
3.3. Document AIOps Processes
It is essential to have concrete and detailed documentation of the AIOps processes after they are tested and approved by IT professionals and decision-makers in the organization. This detailed documentation helps in giving theories and authorization if there are any management changes, important resources, data storage, and accessibility in database systems.
Bottom Line
If properly strategized, AIOps has the potential to successfully and digitally transform your business scope. “With the goal of digitally transforming their operations, enterprises are relying on the capabilities of AIOps that can deliver resiliency, business assurance, and better customer experiences,” said Akhilesh Tripathi, CEO of Digitate. AIOps platforms are currently used by a few MNCs to enhance IT operations. Numerous IT professionals believe that within the next few years, there are good chances to adopt AIOps in medium and large IT companies.
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