Artificial Intelligence Operations (AIOps) – or the application of big data analytics, Machine Learning (ML), and other AI technologies to automate and improve IT operations – is driving a significant shift in the IT operations landscape and spurring the adoption of observability tools across the industry.
The emergence of technologies such as containerization, microservices, and multi-cloud provide enterprises with opportunities to improve their business models and develop new income sources. Application Process Monitoring (APM) and data logging tools cannot meet the operational demands of the new technologies, as these tools typically focus on the overall state of system and business metrics, which are pre-defined. Thus, organizations must adopt innovative solutions to address these challenges. This is where observability – which offers greater context and more precise insights into how systems behave – comes into play. Observability refers to the ability to comprehend the functioning of a software application using its telemetries, such as logs, metrics, and traces.
In this viewpoint, we look at the value proposition of observability solutions for enterprises, urge enterprises to rethink their observability adoption strategies and establish the right frameworks to measure the impact of observability tools, and examine enterprise considerations for third-party provider selection.
Scope
All industries and geographies
Contents
This report examines:
The value proposition of observability solutions in the IT operations landscape
An investment approach and value measurement framework to measure the value of observability solutions
Adoption challenges and enterprise best practices
Enterprise considerations for third-party provider selection
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