FINAL WORD
The problem is not just that low sample rates are bad – they do not tell the whole story , which can lead analysis in the wrong direction .
Reducing noise
Increasingly , teams are joining unified observability with the power of artificial intelligence , AI and machine learning , ML . Together they can quickly provide context for anomalies and uncover leads that create actionable insights .
The huge number of alerts can often leave IT teams feeling alert fatigue . Sorting through the noise to find the particular root cause of a delay can be time consuming and difficult , particularly when also contending with the constant flow of data from full telemetry . Previously , resource-intensive war rooms would be used to solve these problems , but they were often inefficient , leading to more finger-pointing than solutions .
Alternatively , there would be a senior level employee who would be the expert at spotting the individual problems . However , it was a waste of resources to have such a skilled employee troubleshooting problems across IT siloes . And if they were to leave , the company would have no way of replicating their results .
With unified observability , IT teams have fewer tickets and alerts to deal with – maximising their efficiency and improving job satisfaction . Its ability to cut across siloes also helps teams to work collaboratively to solve problems . With the current talent shortage plaguing the IT sector , unified observability is a key tool that can alleviate some of the burden IT teams face on a dayto-day basis .
Streamline processes
AI and ML allows all IT staff to use runbooks to automate tasks . It is common
Implementing unified observability can be challenging , particularly for large , global organisations . for organisations to have documented runbooks that can be used to manually resolve particular problems . But , with unified observability , teams can create workflow engines that automate processes and simplify finding solutions . These engines can also be customised , allowing teams to configure them until they are sure positive outcomes are delivered .
In fact , libraries of preconfigured solutions can be customised to provide automated actions for frequently encountered issues , allowing more senior IT staff to spend their time on higher-level tasks .
Maximising productivity
The recent IDC survey sponsored by Riverbed also found that three quarters , 75 % of teams find it difficult to gain insight from their range of siloed observability tools . With unified observability , IT teams can analyse the full breadth of their organisation ’ s data to create actionable insights .
In turn , these insights ensure end-users receive a valuable digital experience , where operations run smoothly and safely , keeping employees happy and productive . And , in the background , automated remediation improves agility , maximises return on investment , and optimises services .
The large number of organisations using observability demonstrates a widespread understanding of the importance of monitoring infrastructure in modern business . It is a critical practice that helps to provide frictionless digital experiences to both customers and employees . Despite this , many organisations are still using multiple outdated tools that cannot provide the scope of data provided , and therefore lead to an incomplete view of network performance and low end-user satisfaction .
To combat this , more and more companies are moving towards unified observability to minimise toolsets . The result is IT teams that can maximise their ability to find actionable insights , vastly improving productivity across the entire organisation . •
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