What is AIOps?
AIOps, (for artificial intelligence for IT operations) is the application of artificial intelligence (AI) to enhance IT operations. Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following:
- Collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications, and performance-monitoring tools
- Intelligently sift ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance and availability issues.
- Diagnose root causes and report them to IT for rapid response and remediation—or, in some cases, automatically resolve these issues without human intervention.
By replacing multiple separate, manual IT operations tools with a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with a lot less effort.
It bridges the gap between an increasingly diverse, dynamic, and difficult-to-monitor IT landscape, on the one hand, and user expectations for little or no interruption in application performance and availability, on the other. Most experts consider AIOps to be the future of IT operations management.
Why do we need AIOps?
Today, most organizations are transitioning from a traditional infrastructure of separate, static physical systems to a dynamic mix of on-premises, managed cloud, private cloud, and public cloud environments, running on virtualized or software-defined resources that scale and reconfigure constantly.
Applications and systems across these environments generate a tsunami of data that keeps growing. In fact, Gartner estimates that the average enterprise IT infrastructure generates two to three times more IT operations data every year.
Traditional domain-based IT management solutions can’t keep up with the volume. They can’t intelligently sort the significant events out of the crush of surrounding data. They can’t correlate data across different but interdependent environments. And they can’t provide the real-time insight and predictive analysis IT operations teams need to respond to issues fast enough to meet user and customer service level expectations.
Enter AIOps, which provides visibility into performance data and dependencies across all environments, analyzes the data to extract significant events related to slow-downs or outages, and automatically alerts IT staff to problems, their root causes, and recommended solutions.
How does AIOps work?
The easiest way to understand how AIOps works 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 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
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.
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 specific benefits:
- Achieve 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, telecommunications provider Nextel Brazil was able to use AIOps to reduce incident response times from 30 minutes to less than 5 minutes.
- Go from reactive to proactive to predictive management: Because it never stops learning, AIOps keeps getting better at identifying less-urgent alerts or signals that correlate with more-urgent situations. This means it can provide predictive alerts that let IT teams address potential problems before they lead to slow-downs or outages.
- Modernize your IT operations and your IT operations team: Instead of being bombarded with every alert from every environment, AIOps operations teams only receive alerts that meet specific service level thresholds or parameters—complete with all the context required to make the best possible diagnosis and take the best and fastest corrective action. The more AIOps learns and automates, the more it helps ‘keep the lights on’ with less human effort, and the more your IT operations team can focus on tasks with greater strategic value to the business.
AIOps use cases
In addition to optimizing IT operations, AIOps visibility and automation can support and help drive other important business and IT initiatives:
- Digital transformation: Digital transformation is what creates the IT complexity (e.g., multiple environments, virtualized resources, dynamic infrastructure) that AIOps is designed to tackle. The right AIOps solution gives an organization more freedom and flexibility to transform based on strategic business goals, without worrying about the IT operations burden.
- 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.
AIOps and IBM Cloud
IBM Cloud allows you to build and deploy across multicloud architectures and existing IT. AIOps solutions from IBM enable new IT operations efficiencies by providing centralized visibility across all environments so your operations teams can diagnose problems and resolve incidents faster.
IBM Cloud Pak for Watson AIOps uses machine learning and natural language understanding to correlate structured and unstructured data across your operations toolchain in real time to uncover hidden insights and help identify root causes faster. Eliminating the need for multiple dashboards, Watson AIOps feeds insights and recommendations directly into your team workflows to speed incident resolution.
To get started, sign up for an IBMid and create your IBM Cloud account.