In today’s fast-paced and tech-savvy world, the volume of data being generated by IT systems is mind-boggling. Without proper monitoring and analysis tools, there can be several missed opportunities and costly downtime. However, the inception of machine learning and big data has resulted in the emergence of a new tool in the IT industry known as AIOps.
What does AIOps (Artificial Intelligence for IT Operations) mean?
- AIOps refers to the practical application of artificial intelligence and related technologies, such as natural language processing (NLP), machine learning, and big data, to traditional IT activities and processes.
- In AIOps, various technologies augment, support, and aid in the automation of various processes in IT environments.
- AIOps helps the DevOps and IT Ops teams detect digital issues earlier and resolve them quickly before customers and business operations are negatively impacted.
- With the use of the latest technologies such as ML, NLP, and IT operations analytics, algorithmic analysis of IT data is possible.
- With AIOps, the operations teams are able to overcome the highly complex and enormous volume of data being generated in modern IT environments, thus ensuring uptime and preventing unnecessary breakdowns.
- IT is at the core of any organizational transformation, and with the implementation of AIOps, organizations can operate their businesses at great speed to deliver a superior user experience.
Why is AIOps required?
According to Gartner, 40% of companies will be using AIOps for infrastructure and application monitoring by 2023. A survey conducted by Enterprise Management Associates reveals that nearly 81% of the organizations using AIOps have reported a positive return on investment (ROI).
The implementation of AIOps enables IT ops teams to respond quickly and proactively to outages and slowdowns with minimal effort.
Makes analysis easier
- AIOps makes it possible to analyze and correlate the performance metrics of multiple applications.
- It serves as a single point of visibility and analysis across various domains in an organization, allowing it to deliver a top-notch customer experience.
- AIOps also helps to eliminate the need for using multiple tools to reduce the incidence of false positives, develop correlations, and identify the root cause of the problem.
- The implementation of AIOps resolves surfacing IT issues before they can cause any significant downtime.
Effectively manage the huge volume of data and alerts
- Without AIOps, the enormous volume of data and alerts being generated every month may be unmanageable.
- By using AIOps, the implementation of technologies such as AI and ML makes it possible to eliminate the problems of early detection of issues, team collaboration, and alert correlation.
- AIOps reduces the downtime and the time spent by the team on analyzing the data and issuing alerts every month.
Delivering an exceptional user experience through predictive analytics
- In today’s competitive world, every business is just one bad user experience away from losing a customer.
- In order to avoid losing customers, organizations focus on delivering an exceptional user experience.
- AIOps has the potential to deliver a great user experience with predictive analytics.
Facilitates improved collaboration between the IT team and other departments in a business
- With comprehensive reports and data presentation, AIOps empowers IT teams to communicate various key metrics to improve their services.
- Even if the IT team is located remotely, AIOps facilitates remote collaboration between them and other teams as well.
- With the help of customized and highly intuitive dashboards, the productivity of IT teams and others will definitely increase.
- Machine learning technology used as a part of AIOps can help detect anomalies and suspicious activities on the network.
- Once identified, automatic notifications are sent to IT operations teams on the underlying issues for further diagnosis.
Reduces IT operational expenses
- AIOps has the potential to significantly lower the cost of IT operations.
- With AIOps, there is no need to purchase and maintain expensive IT infrastructure.
- Since major tasks can be automated with the use of AIOps, it means a significant reduction in manpower, hardware, and other operational costs.
Expediates problem-solving
- Resolving problems arising out of IT activities is quite tedious and time-consuming.
- The unavailability of in-depth data makes it difficult to identify the root cause of the problem.
- However, with the deep insights and analytics offered by AIOps, it is possible to trace the cause of the problem to its root so that the relevant action can be taken.
- Thus, AIOps helps to speed up the problem-solving process.
Automation
- Manual data collection and analysis, along with fixing problems related to the IT environment whose root cause is unknown, is tedious.
- Manual processes in the IT environment increase downtime and outages, resulting in a lot of losses for the organization.
- AIOps makes use of ML algorithms to streamline, detect, correlate, resolve, and automate different aspects of the incident management cycle.
- With the integration of AIOps into IT operations, it helps automate workflows, restore systems after outages, and schedule workloads to lower the burden on the IT Ops team and allow them to focus on other core issues.
How does AIOps (artificial intelligence for IT operations) work?
To drive maximum value, an organization should use the AIOps tool as an independent platform that takes in data from various IT monitoring sources.
AIOps, with the use of various algorithms, helps streamline and automate the following five dimensions of IT operations analytics and monitoring:
Data selection
Out of all the redundant and massive amounts of data generated by the modern IT environment, AIOps helps identify and surface the most relevant data through filtration.
Pattern discovery
Correlating and identifying relationships between the selected data elements and grouping them for advanced analytics
Inference
Identifying the root cause of the problems and recurring issues, so that actions can be taken on the insights (discovery).
Collaboration
Notifying relevant teams and operators and facilitating improved collaboration between them
Automation
Automating responses and remediations to make solutions more precise and faster
Use cases for AIOps
- Identify the root causes of the problem and take proactive steps to avoid similar issues in the future.
- Anomaly (threat) detection to avoid data breaches and regulatory issues
- Performance monitoring of storage systems and cloud infrastructure based on various performance metrics
- Pattern recognition and discovery to identify irregularities in the system for proactive incident management
- It eliminates the problems generated by IT noise and selects only the truly relevant data, removing the false positives.
If you are a business looking to survive and thrive in today’s highly competitive and digital economy, you must definitely look forward to implementing AI in your IT business operations. AIOps will play a crucial role in improving the efficiencies and functionalities of IT Ops teams. Whether you are a small business or a large one, it is time to operate; it is time to implement AIOps solutions that deliver the top-notch experience that customers are looking for.