Emergence of AIOps
There has been a gigantic growth of AIOps in the last two years. It has successfully transitioned from an emergent category to an inevitability. Companies adopted AIOps to automate and improve IT operations by applying big data and machine learning (ML). Adoption of such technologies compelled IT operations to adapt a multi-cloud infrastructure. According to Infoholic Research, the AIOps market is expected to grow at a CAGR of 33.08% during the forecast period 2018–2024.
What is AIOps?
AIOps broadly stands for Artificial Intelligence for IT Operations. With a combination of big data and ML, AIOps platform improvises IT operations and also replaces certain tasks including tracking availability, event correlation, performance monitoring, IT service management and automation. Most of these technologies are well-defined and matured.
AIOps data originates from log files, metrics, monitoring tools, helpdesk ticketing and other sources. It sorts, manages and assimilates these data to provide insight in problem areas. The goal of AIOps is to analyze data and discover patterns that can predict potential incidents in future.
Focus areas of AIOps
- AIOps helps with open data access without letting organizational silos play a part in it.
- AIOps upgrades data handling ability which also impacted on the scope of data analysis.
- It has a unique ability to stay aligned to organizational goals.
- AIOps increases the scope of risk prediction.
- It also reduces response time.
Impact of AI in IT operations
- Capacity planning: AIOps can support in understanding workloads and plan configuration appropriately without allowing a scope for speculation.
- Resource utilization: AIOps allows predictive scaling where auto-scale feature of cloud IaaS can adjust itself based on historical data.
- Storage: AIOps helps in storage activity through disk calibration, reconfiguration and allocation of new storage volumes.
- Anomaly detection: It can detect anomalies and critical issues faster with accuracy more than humans, reducing potential threats and system downtime.
- Threat management: It helps to analyze breaches in both internal and external environments.
- Root-cause analysis: AIOps is effective in root-cause analysis, through which it reduces response time and creates remedy after locating the issue.
- Forecasting outages: Outage prediction is essential for the growth of IT operations. Infact, the market of forecasting outages through AIOps, is expected to grow from $493.7 to $1.14 billion between 2016 and 2021 based on industry reports.
- Future innovation: AIOps has played a key role in automating a major chunk of IT operations in a massive way. It frees resources to focus on crucial things aligned to strategy and organizational goals.
Problems AIOps solved
The common issues AIOps solves to enable IT operations’ adoption of digitization are as follows:
- It has the ability to gain access over large data sets across environments while maintaining data reliability for comprehensive analysis.
- It simplifies data analysis through automation empowered by ML.
- Through accurate prediction mechanism, it can avoid costly downtime and improve customer satisfaction.
- Through implementation of automation, manual tasks can be eliminated.
- AIOps can improve teamwork and workflow activities between IT groups and other business units.
Peeping into the future
AIOps platform acts as a foundation stone in projecting future endeavors of organizations. It uses real-time analysis of data to provide insights to impact business decisions. Successful implementation of AIOps depends on key parameters index (KPIs). It can also deliver a predictive and proactive IT operation by reducing failure, detection, resolution and investigation.