GigaOm Radar for AIOpsv4.0

1. Summary

Artificial intelligence for IT operations (AIOps) encompasses the technologies that automate, identify, and resolve IT issues. Additionally, it can predict and automatically resolve concerns before they become problems.

The infrastructure, services, and applications in an enterprise produce different types of data, including metrics, performance data, and log data. As a key component in the development of operational and organizational awareness, AIOps combines data and information from across the enterprise to improve root cause analysis, prediction, and automatic response, reducing time to resolution (MTTR) and producing better enterprise-level outcomes. Moreover, the integration of business intelligence (BI) data enables AIOps to answer questions about the state of the entire business.

By providing a link between IT operations and business operations, an AIOps solution helps answer questions about the state of the entire organization (Figure 1).

Figure 1. Organizational Awareness is Built on Operational Awareness and Business Awareness

Organizational awareness requires the monitoring and observability of IT systems and operations. The relationships among monitoring, observability, and awareness (MOA) are shown in Figure 2. MOA refers to a process by which data about operations (IT) and business (people and processes) is tracked and evaluated to enable a company to develop organizational awareness.

• Monitoring provides the state of that single system and metrics about it (performance or a break/fix condition).
• Observability combines the state of multiple systems and asks additional questions about the health of these systems as a whole (such as why devices, systems, or applications are behaving a certain way within the context of IT).
• Awareness brings together all information about the company to evaluate whether operations (IT) and business (people and processes) are OK, what is likely to break next, and how to prevent problems before they are monitored or observed.

AIOps solutions perform MOA functions—either internally or by ingestion of data from other tools—and as such, this becomes a critical consideration when evaluating AIOps solutions.

Figure 2. Monitoring, Observability, and Awareness Relationship

Vendor selection should be based on the needs of the organization, and the process involves more than just evaluating the technical capabilities of solutions. AIOps can be disruptive to organizations, and if the IT operations team lacks the technical and functional ability to move with changing business strategies, the political capital that may be required for successful implementation of an AIOps solution can be substantial.

With domain-specific tools, existing IT tools for monitoring and observability may have to be replaced or else the organization may have to live with redundant data gathering or analysis without retiring the displaced tools. However, if the displaced tools are retired, a single vendor can provide a solution that spans from monitoring to awareness.

In contrast, domain-agnostic solutions can be layered over existing tools, does not make them redundant, and requires less political expenditure. The challenge then becomes obtaining data from the various silos within the enterprise. Friction may be lower, but the time to value may be longer.

This GigaOm Radar report highlights key AIOps vendors and equips IT decision-makers with the information needed to select the best fit for their business and use case requirements. In the accompanying GigaOm report, “Key Criteria for Evaluating AIOps Solutions,” we describe in more detail the capabilities and metrics that are used to evaluate vendors in this market.