AIOps and Observability Delivers Operational Insights
- By Carlos Casanova, Forrester
- February 13, 2024
Implementing AIOps and observability solutions can be a challenging task for organizations. To ensure success, it is crucial to develop the right approach and strategy that aligns with your enterprise’s specific IT complexity and goals. In Start With The Right Strategy And Approach For AIOps And Observability For IT Operational Insights, you can begin exploring the best practices for AIOps and Observability and what aspects you need to consider when delivering these.
The importance of a well-designed approach
Successful initiatives in AIOps and observability are well-designed and architected. They must, however, also provide continuous insights. These initiatives should support iterative processes with tight feedback loops. They must allow organizations to assess progress, adjust, and drive operational objectives.
To achieve this, ensure that all stakeholders agree on the desired business outcomes. While operational goals and desired business outcomes are not the same, they must be directly tied together as a prerequisite to AIOps. Communicate clear, documented outcomes to all parties involved, from business leaders to developers and support personnel. Drive prioritization of initiatives and tasks by business outcomes, considering seasonality, cyclical business patterns, and risk to business operations.
Determining the depth and breadth of insight
No single solution can satisfy all enterprises and deliver all the insights they need. Therefore, evaluating the required depth and breadth of insight is crucial to achieving desired outcomes. This evaluation should consider the criticality and external dependency of systems, as well as the level of real-time detail and breadth of details required.
AIOps technology providers can offer the necessary depth and breadth in one platform, but this depends on factors such as IT environment complexity, specialty technologies in use, and system criticalities. It is important to align the capabilities of observability and AIOps technologies with their supporting practices to ensure a successful implementation.
The relationship between AIOps enablement technologies and practice
While the marketing hype around AIOps may suggest that it can magically resolve all operational issues, it should be no surprise that the reality is different. Silos throughout the IT ecosystem often address day-to-day operations with mixed success. It is essential to acknowledge that technology and practice are not the same and should not be treated as such.
To deliver the full-stack vision and promise of AIOps, organizations must carefully weave together a broad array of capabilities. Eighteen foundational capabilities are defined in the Forrester AIOps reference architecture. Leading AIOps vendors offer these technologies in single platforms and as suites of tools, but it is crucial to establish a sound enterprise AIOps practice to succeed in this journey.
The viability of data for quality and completeness
The quality and completeness of data play a crucial role in the effectiveness of AIOps solutions. Observability and AIOps initiatives rely on high-quality and complete data and traditional monitoring data. Be cautious and deliberate with your data intentions, and ensure that every data point collected is of high quality and has a clear purpose. Collecting more bad or unused data only creates noise and increases operating costs.
Join the conversation
Implementing AIOps and observability solutions requires a well-designed approach and strategy. By aligning to the desired business outcomes, evaluating the required depth and breadth of insight, acknowledging the relationship between technology and practice, and ensuring the viability of data, organizations can pave the way for successful implementation.
Remember that one size does not fit all for insights. Implement a solution that enables appropriate actions and capabilities across the tech stack. Understand the depth and breadth of insights required and consider factors such as observability levels, application-layer heuristics, and IT ecosystem visibility to help you make informed decisions.
The original article is here.
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