Observability – transforming analysis with AIOps and machine learning (Guest blog by SolarWinds)
As public sector organisations make progress on their digital transformation initiatives and modernise applications, they still need to manage their complex and distributed network, cloud, system, application, and database infrastructures. Consequently, IT teams need comprehensive monitoring and visibility across the full IT stack to ensure effective analysis and troubleshooting.
Traditionally, monitoring works by aggregating and displaying the data that shows whether systems are operating correctly. It does so by generating alerts—often in significant volume—and while some hold the key to fixing a specific problem, others are just noise.
Today’s complex IT infrastructures frequently employ microservice architectures, and to observe, monitor, and analyse their cloud environments efficiently, tech pros need alerts but without the noise. Furthermore, they need easy access to visualisations showing critical data, so problems are spotted quickly, even in heavily distributed environments.
Ideally, observability solutions will allow IT teams to filter seasonal trends using time-series analysis and focus on where action is required, delivering end-to-end oversight of their service delivery and component dependencies. By integrating AIOps (artificial intelligence for IT operations) and machine learning (ML), hybrid and multi-cloud environments benefit from real-time control.
Applying AIOps and ML technologies means public sector organisations can analyse the noise and the deluge of accumulated data so tech pros can move quickly and effectively to manage services required by employees and the public alike. The result is analysis taking seconds rather than hours.
Integrated intelligence
This doesn’t mean observability will replace traditional monitoring. Instead, it uses the information gathered through monitoring as a critical element of its broader capabilities. By analysing the collected data and comparing it to expected outcomes and objectives, tech pros can fully understand the state of their infrastructure and applications faster than ever.
AIOps and ML will allow observability solutions to go further by delivering predictive analytics, detecting potential problems before they occur, and responding to them independently and automatically. But when a tech pro needs to be involved, they can be alerted with the necessary insights, automated analytics, and actionable intelligence through cross-domain data correlation, and massive real-time and historical metrics, logs, and trace data.
By minimising operational noise, public sector tech pros—including DevOps and security teams—can become more proactive in discovering issues and anomalies. This includes automating tasks and implementing advanced closed-loop operational management, reporting, and capacity planning efficiencies—across IT domains.
This can help enhance business agility by allowing tech pros to identify problems and discover deficiencies, using this insight to characterise and predict impactful business service, component, and activity-state changes.
Integrated observability solutions also optimise IT efficiency, eliminate redundant tools, and help reduce costs. Teams find they can shift away from a reactive to a proactive stance by analysing business service and component relationships, deviations, and dependencies. As a result, they also see performance, compliance, and resilience improve.
The observable IT universe
With hybrid remote work here to stay across almost every sector, any loss of connectivity can lead to poor workplace communication, a site going dark, or a large-scale disruption. AIOps and ML help observability solutions provide dynamic protection against these and other difficulties.
It’s important, however, not to view observability as one more component thrown into the stack. Instead, it’s a next-generation, integrated IT infrastructure, application, and database performance management solution.
With integrated AIOps and ML, observability enables public sector organisations to understand and manage IT service delivery more easily and holistically. It’s cost-effective because it continuously allows technology teams to improve performance and reliability. This enhances the user experience across complex, diverse, and distributed hybrid and cloud environments, taking traditional monitoring practices to the next level.
Authors
Sascha Giese
Giese holds various technical certifications, including being a Cisco Certified Network Associate (CCNA), Cisco Certified Design Associate (CCDA), Microsoft Certified Solutions Associate (MCSA), VMware Technical Sales Professional (VTSP), AWS Certified Cloud Practitioner, and Network Performance Monitor and Server & Application Monitor SolarWinds Certified Professional® (SCP).
