What is AIOps?
Coined by Gartner, AIOps—i.e. artificial intelligence for IT operations—is the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows. By aggregating data in real-time, AIOps platforms can make predictions around operational hazards, such as a data breach, which can either kick off a prescriptive action automatically, like a defense protocol, or alert security teams to action on an urgent issue more immediately. These tools are typically integrated into DevOps and DevSecOps teams to help with performance monitoring and reduce mean-time-to-know (MTTK).
The demand for AIOps has only grown with the increased business focus on digital transformation initiatives. While the use of virtual machines, container-based microservices and shared multi-tenant infrastructure have accelerated application development, it has unfortunately come at the expense of operational efficiency as each app has its own set of data. AIOps attempts to break down the operational silos by aggregating this data and providing more transparency and insight to it organizations. This, in turn, allows businesses to reduce costs and improve decision-making to make progress against goals.
Types of AIOps
AIOps tools fall into two main categories, self-healing (active) and not self-healing (inactive):
Self-healing: As the name suggests, self-healing, or active, AIOps solutions proactively respond to unintended events, such as slowdowns and outages. By feeding application performance metrics into predictive algorithms, they can identify patterns and trends that coincide with different IT issues. With ability to forecast IT problems before they occur, AIOps tools can launch relevant, automated process in response, rectifying issues quickly.
This type of technology is the future of IT operations management as it can help business improve the both the employee and customer experience. Not only do self-healing AIOps systems ensure that IT service issues are resolved in a timely manner but they also provide a safety net for IT operation teams, addressing issues that may fall through the cracks due to human oversight, such as organizational silos, under-resourced teams, and more.
Not self-healing: Unlike active AIOps tools, not self-healing, or passive, ones do not take corrective action to address IT issues. Instead, these tools aggregate IT data from a variety of data sources to alert end users of potential issues, expecting IT service teams to implement the necessary remediation. While the data and corresponding visualizations from these tools are valuable, passive AIOps solutions create a dependency on IT organizations to respond appropriately to technical issues. Resource optimization that requires an operator to manually update operational systems will fall short in dynamic demand situations.
Benefits of AIOps
The overarching benefit of AIOps is that it enables IT operations to identify, address, and resolve slow-downs and outages faster than they can by sifting manually through alerts from multiple IT operations tools. This results in several key benefits:
- Faster mean time to resolution (MTTR): By cutting through IT operations noise and correlating operations data from multiple IT environments, AIOps is able to identify root causes and propose solutions faster and more accurately than humanly possible. This enables organizations to set and achieve previously unthinkable MTTR goals. For example, Vivy’s IT infrastructure reduced the mean time to repair (MTTR) for the company’s app by 66%, from three days to one day or less.
- Lower operational costs: Automatic identification of operational issues and re-programmed response scripts will reduce operational costs, allowing for better resource allocation. This also frees up staffing resources to work on more innovative and complex work, leading to an improved employee experience. Carhartt has experienced this benefit firsthand with IBM Turbonomic, allowing them to improve overall performance and reduce resource consumption by 15%.
- More observability and better collaboration: Available integrations within AIOps monitoring tools facilitate more effective cross-team collaboration across DevOps, ItOps, governance and security functions. Better visibility, communication, and transparency allows these teams to respond to issues more quickly. As an example, Dealerware brought more observability to their container-based architecture, which improved app performance during the pandemic and reducing delivery latency by 98%.
- Increased focus key metrics: Customized data views allow respective teams to focus on the metrics that matter most. Additionally, AIOps tools consolidate alerts and only surface ones that meet specific thresholds, preventing teams from being inundated with endless alert notifications. This allows IT operations teams to prioritize system issues more easily.
- Go from reactive to proactive to predictive management: With built-in predictive analytics capabilities, AIOps continuously learns to identify and prioritize the most urgent alerts, letting IT teams address potential problems before they lead to slow-downs or outages. These platforms enable teams to set up preventative measures so they can focus on tasks with the greatest strategic value to the business.
AIOps use cases
AIOps incorporates big data, advanced analytics, and machine learning capabilities to tackle the following use cases:
- Root cause analysis: As the name suggest, root cause analyses determine the root cause of problems in order to remediate with the appropriate solutions. By identifying root causes, teams can avoid unnecessary work involved with treating symptoms of the issue versus the core problem. For example, an AIOps platform can trace the source of a network outage to resolve immediately and set up safeguards to prevent the similar problems in the future.
- Anomaly detection: AIOps tools can comb through large amounts of historical data and discover atypical data points within a dataset. These outliers act as ‘signals’ which identify and predict problematic events, such as data breaches. This capability allows businesses to avoid costly consequences, such as negative PR, regulatory fines, and declines in consumer confidence.
- Performance Monitoring: Modern applications are often separated by multiple layers of abstraction, making it difficult to understand which underlying physical server, storage, and networking resources are supporting which applications. AIOps helps to bridge this gap. It acts as a monitoring tool for cloud infrastructure, virtualization, and storage systems, reporting on metrics such as usage, availability, and response times. In addition, it leverages event correlation capabilities to consolidate and aggregate information, enabling better information consumption for end users.
- Cloud adoption/migration: For most organizations, cloud adoption is gradual, not wholesale, resulting in a hybrid multicloud environment (private cloud, public cloud, multiple vendors), with multiple interdependencies that can change too quickly and frequently to document. By providing clear visibility into these interdependencies, AIOps can dramatically reduce the operational risks of cloud migration and a hybrid cloud approach.
- DevOps adoption: DevOps speeds development by giving development teams more power to provision and reconfigure infrastructure, but IT still has to manage that infrastructure. AIOps provides the visibility and automation IT needs to support DevOps without a lot of additional management effort.
How does AIOps work?
The easiest way to understand how AIOps works is to review the role that each AIOps component technology—big data, machine learning, and automation—plays in the process.
AIOps uses a big data platform to aggregate siloed IT operations data, teams, and tools in one place. This data can include the following:
- Historical performance and event data
- Streaming real-time operations events
- System logs and metrics
- Network data, including packet data
- Incident-related data and ticketing
- Related document-based data
- Application demand data
- Customer demand data
- Infrastructure supply data
AIOps then applies focused analytics and machine learning capabilities:
- Separate significant event alerts from the ‘noise’: AIOps uses analytics like rule application and pattern matching to comb through your IT operations data and separate signals—significant abnormal event alerts— from noise (everything else).
- Identify root causes and propose solutions: Using industry-specific or environment-specific algorithms, AIOps can correlate abnormal events with other event data across environments to zero in on the cause of an outage or performance problem and suggest remedies.
- Automate responses, including real-time proactive resolution: At a minimum, AIOps can automatically route alerts and recommended solutions to the appropriate IT teams, or even create response teams based on the nature of the problem and the solution. In many cases, it can process results from machine learning to trigger automatic system responses that address problems in real-time, before users are even aware they occurred.
- Learn continually, to improve handling of future problems: Based on the results of the analytics, machine learning capabilities can change algorithms or create new ones to identify problems even earlier and recommend more effective solutions. AI models can also help the system learn about and adapt to changes in the environment, such as new infrastructure provisioned or reconfigured by DevOps teams.
AIOps and IBM
Explore the IBM AIOps offering. IBM AIOps helps organizations assure app performance while quickly cutting IT costs. Organizations have been able to reduce IT spending by 50%, save up to USD 2 million in incident management and reduce MTTR by 50%. In addition, teams were able to debug apps 75% faster.