In today’s complex hybrid multi-cloud environment, CIOs understand that monitoring of logs, metrics, and traces is no longer sufficient.
BOULDER, Colo. (PRWEB) November 01, 2022
Enterprise Management Associates (EMA™), a leading IT and data management research and consulting firm, today announced the release of its new research report, “Driving Observability Through Machine Learning and Predictive Analytics,” authored by Will Schoeppner, research director covering application performance management and business intelligence at EMA.
The need for real-time, reliable data is increasing, and that data is a necessity to remain competitive in today’s business landscape. At the same time, observability has become even more critical with the complexity of a hybrid multi-cloud environment.
“In today’s complex hybrid multi-cloud environment, CIOs understand that monitoring of logs, metrics, and traces is no longer sufficient,” said Schoeppner. “Organizations require an observability solution that will provide crucial visibility into the health and performance of the environment and enable predictive solutioning and remediation of critical events prior to impacting customer performance.”
To add to the challenges and complexity, the term “observability” has not been clearly defined and can be broad in context. Across the industry, a commonality is that the reach of observability extends well beyond simply the collection of logs, metrics, and traces. Unified observability brings infrastructure monitoring, security, logs, application performance monitoring, and SaaS monitoring into a single platform for complete end-to-end visibility for cross-functional teams, driving streamlined collaboration and faster resolution of issues. Based on this definition, EMA’s research explores challenges technology teams face in a complex landscape and how the benefits of observability can have an impact on driving business outcomes and customer success.
This study explored the rapid growth of observability and its critical importance in an organization. It also evaluated how observability that provides predictive analytics developed using machine learning models can make the difference in delivering customer expectations, reducing technology resource cost, and eliminating fatigue within an organization’s technology teams.
The research delivered several fascinating key findings detailed throughout the report. Some of these key findings are:
- 73% of companies indicated they have been data-driven in their decision-making process for three years or more.
- Only 27% of organizations use the same solution for observability across all IT software development functions.
- 71% of companies indicated they have been mature in the use of analytics and the use of machine learning in observability for three years or more. However, only 54% of organizations believe their maturity in analytics and the use of machine learning in observability is advanced or superior.
According to respondents, the greatest benefit of observability is being able to prioritize and resolve issues faster, followed by being able to proactively detect issues.
Elastic sponsored this independent research report.
A detailed analysis of the research findings is available in the report, “Driving Observability Through Machine Learning and Predictive Analytics.”
You can get highlights from the report by viewing the on-demand webinar, “Driving Observability Through Machine Learning and Predictive Analytics.”
Founded in 1996, EMA is a leading industry analyst firm that provides deep insight across the full spectrum of IT and data management technologies. EMA analysts leverage a unique combination of practical experience, insight into industry best practices, and in-depth knowledge of current and planned vendor solutions to help their clients achieve their goals. Learn more about EMA research, analysis, and consulting services for enterprise line of business users, IT professionals, and IT vendors at https://www.enterprisemanagement.com