AIOps stands for “Artificial intelligence for IT operation”.
It is the application of AI, and related technologies, such as big data and machine learning to enhance IT operations, including event correlation, anomaly detection and casualty determination.
If you have been following the corporate IT landscape for a while, you must be familiar with several acronyms used these days to describe new developments in the IT industry.
Indeed, it is true that the world has changed significantly because of technology and the evolving nature of business. There’s one-upmanship and competition at every new level, whether on a local or global scale.
Companies are finding new ways to grow their profits using cutting-edge technology and internet-savvy strategies that make them stand out from others.
What is AIOps?
It is a multi-disciplinary field that integrates analytics, machine learning and data centre automation to support core IT functions.
It primarily focuses on the automated, real-time execution of distributed transformations across a highly dynamic business environment, including cloud delivery and the integration of software-defined technologies.
AIOps aims to improve operational efficiency through continuous integration and deployment of sustainable development tools along third-party APIs.
The platform not only empowers organisations to manage the heaps of data they generate, but it will also harness the power of this data via analytics.
It is possible to identify hidden growth patterns, cost efficiency, redundancy, productivity, etc., and gather actionable insights.
In addition to historical organisational data insight, predictive analytics provides reliable, data-driven insights into various segments.
Objectives:
In a technical sense, AIOps aims to transform current conventional IT environments into one that supports faster loading times and improved scalability. And will be achieved by creating self-contained environments for executing IT processes in real-time and on-demand, with the ability to scale on demand for workloads that require elastic capacity.
How Do AIOps works?
All AI tools are not equal. Some IT operations monitoring tools are better than others.
The most effective implementation is an independent platform that ingests data from all IT monitoring sources and acts as a central engagement system. And that should be powered by five types of algorithms that fully automate and streamline five main dimensions of IT operations monitoring:
#1- Selecting Data:
Selecting the data elements that indicate a problem within an IT environment is back-breaking.
By taking the massive amount of highly redundant and noisy IT data generated by a modern IT environment and filtering out up to 99% of this data, an analyst can identify the information that indicates the problem.
#2- Pattern Discovery:
Correlation helps to find relationships between the selected data elements and group them for advanced analytics.
#3- Root cause analysis:
Root cause analysis is a process whereby you determine the reasons behind recurring problems and issues so that you can take action.
#4- Collaboration:
When people are working remotely, frequent communication channels are not available. It is vital to notify appropriate operators and teams.
Collaboration among them can preserve data and interpret similar problems in the future.
#5- Automation:
Automate response and remediation processes make solutions precise and quick.
The Benefits of AIOps for Startups:
AIOps can help to reduce downtimes, improve customer experience, and increase efficiency in your IT operations.
In the long run, it can help you scale your business with a reliable and automated solution for your IT needs.
#1-Productivity:
With AIOps, IT operations noise is filtered through and correlated with data from multiple environments to improve Mean Time to Resolution.
Using AIOps, startups can significantly improve their MTTR goals in a much shorter time than humans.
It detects and resolves critical issues more rapidly than humans, enabling IT specialists to focus on the bugs and avoid being distracted by irrelevant alerts and noise, resulting in higher productivity and output.
AIOps improve the speed at which critical outages and poor customer experiences get detected and resolved, improving the customer experience and sales.
#2-Excellent Customer Service:
More agile and adaptive processes directly improve customer service quality. Customers usually expect a brand to appear friendly to them.
AIOps may also result in better customer retention due to the sequence. According to a study by Harvard Business School, a 2% increase in customer retention leads to a 10% cost reduction in profit.
#3- Risk Management:
AIOps is an integrated incident management system capable of managing operational tools, including single-sign-on and workflows.
It allows businesses to monitor the day-to-day process and oversee the changing IT environment without burdening them with the need to learn multiple applications or frameworks.
( For example, a critical failure can be known before using AIOps to analyze historical data from past incidents. )
#4-A Proactive Approach to Predictive Management:
AIOps help the team to identify anomalies and perform proactive monitoring.
So it keeps better at identifying alerts that relate to the more critical ones.
By grouping such signals, AIOps can create a predictive model to let IT teams address potential anomalies before they disrupt services and cause costly outages.
It leads to savings of millions of dollars through the elimination of service disruption based on poor analytics and big-data analysis.
#5-Streamlined IT operations:
Digital transformation has long been talked about but not fully embraced by many companies.
Yet companies are unaware of the importance of digitizing IT and operations to transform themselves into more agile and competitive businesses.
AIOps is an approach to streamline business processes in IT by automating routine tasks, automatically gathering relevant information and orchestrating other initiatives in line with the business goals.
How is an AIOps platform different from a DevOps platform?
- AIOps platforms are a combination of tools that allow teams to identify, monitor, and resolve issues in their IT environments. DevOps, on the other hand, is a single automation solution focused on continuous integration and delivery.
- AIOps solutions have a broader focus and cover many aspects of IT operations, such as network monitoring, security monitoring, application monitoring, and even product usage behaviour. While DevOps works on the automation of infrastructure and code.
- AIOps platforms operate in real-time, identifying dynamic patterns in large amounts of data generated by innovation-enabling technologies like microservices and hybrid IT infrastructure without human intervention. They also reduce the risk of human error in managing large amounts of data that result from diverse IT environments, which causes stress and fatigue in concerned teams.
- AIOps platforms handle big data generated by innovation-driving technologies like microservices and hybrid IT infrastructure in real-time and identify complex patterns dynamically. Furthermore, they can manage data from vast sources, a function that traditional processes, driven by functional silos, can’t accomplish.
- AIOps is a multi-layered platform that can automate traditional IT operations and have ML algorithms for algorithmic analysis. DevOps platforms are used for agile development methodologies and for automating self-service functionalities.
- Through DevOps, platforms automate the build, deployment, and integration process is automated. However, DevOps fails in system operations, security, and compliance.
- CI/CD pipelines provide a streamlined build process through DevOps, while AIOps provides an automation and management framework for scalability.
- The rapid evolution of enterprise applications is driving AIOps adoption and will soon phase out the way businesses develop and deploy applications by automating tasks. Since data integration is challenging on multiple cloud platforms, AIOps will play a crucial role.
Challenges of AIOps:
The biggest challenge with AIOps is the level of maturity of the technology and the lack of standardization. Every AIOps vendor offers its solution in its favour.
This makes it challenging to integrate these solutions with your existing tools, such as your ticketing systems, change management, monitoring tools, network management system (NMS), or security tools.
In addition, integrating these tools could be a very complex and lengthy process, which could take several months to deploy.
Is AIOps the future of DevOps?
Despite DevOps has become the standard for automation, AIOps is positioned as the next generation of DevOps because it reduces dependencies on specific tools. Algorithms can assist bug-tracking and issue-tracking services in data changes.
Read: Why should Business Invest in DevOps?
Integrating AIOps and business operations:
Businesses should shift away from conventional IT operations and enable fast problem identification by monitoring infrastructure behaviour.
It may dynamically manage public cloud utilisation to keep costs in check as well as monitor infrastructure behaviour at the edge.
AIOps platforms draw from large data sets and thus enable IT, teams, to utilise the right set of tools.
AIOps provides data resources intending to enhance work processes. It will also improve the quality of data fed into machine learning systems.
Wrapping Up:
Overall, we believe that AIOps is the future of IT operational functions. AIOps systems are already available on the market, but they have yet to be widely adopted.
As more organizations recognize the benefits of this innovative technology, and as it grows in popularity, a greater number of enterprises will implement it.
However, there continue to be some challenges to adoption. For one thing, AIOps systems take time and resources to implement.
Additionally, the large quantities of data collected by these systems can be overwhelming in and of themselves. As a result, users must learn how to effectively manage these significant influxes of data.
Know about the Top DevOps trends that will lead the future.