GlobalData has revealed key findings from its latest report, Integrated Observability Systems Help Make Sense of Distributed IT Portfolios, which reveals that accelerated digital transformation is steering operations teams toward new observability stacks to oversee an increasingly diverse distributed IT infrastructures.
The report credits this automation acceleration to companies overwhelmed with the move from monolithic apps to microservices where service components within a single app must be secured and managed. Consequently, new monitoring tools are emerging to help developers collaborate under DevOps models and gain automated visibility into the impact of modern coding on underlying systems.
““Not only will these monitoring tools shorten lengthy feedback cycles, but they will also enhance the quality of apps moving through the pipeline, as well as help companies remain competitive and agile,” notes Charlotte Dunlap, Principal Analyst at GlobalData.
GlobalData cites integrated analytics with monitoring solutions as one advancement underscoring observability alongside broad industry acceptance of interoperability OSS technology, such as OpenTelemetry, and innovative disruptors of the traditional monitoring space.
The rise of observability, Dunlap notes, can be credited to a number of factors, including a change from data-based to event-based architectures and increased focus on infrastructure as code — giving rise to an IT model that allows developers to have a greater role in application lifecycle management.
“The move towards emerging observability is helping it become a relevant part of the cloud’s value chain and important technology to watch,” Dunlap says.
GlobalData anticipates that the observability market will evolve in the next 12 months to include more comprehensive solutions that provide application-level observability data alongside systems-level data delivered through pre-set parameters. Integrated observability will support event streaming to detect anomalies and instantaneously highlight areas of concern through machine learning by measuring baseline thresholds and learning over time via modeling when things are not consistent.
“The future of observability,” notes Dunlap, “ is around ML-powered predictive and prescriptive analytics to enable proactive responses that prevent incidents.”