Using Synthetic Transaction Monitoring to Track SaaS SLAs
According to Gartner estimates, “by 2025, 55 percent of large enterprises will successfully implement an all-in cloud SaaS strategy.” Synthetic transaction monitoring is one method IT teams and vendors alike are using to ensure that service level agreement (SLA) targets are being met as business-critical applications move to software as a service (SaaS) models. Synthetic data is useful because it can help identify reoccurring pain points with SaaS that cut into user productivity. When coupled with real-user monitoring, synthetic transactions can also be used to pinpoint the root cause of SaaS issues, even when accountability falls on the SLA vendor. The ability to manage SaaS is an integral part of any digital experience monitoring strategy but can be difficult to execute. Here’s how to simplify dual usage of synthetic transactions and real-user monitoring to ensure a positive end-user experience with SaaS.
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Synthetic transaction monitoring vs. real-user monitoring
Synthetic transactions work by simulating workloads with “synthetic” (i.e. not real) users, typically during periods when real users are offline. These workloads or “transactions” allow IT to see the results of user behavior without actual end-user involvement. For example, an IT admin could run a script during a specified time window that measures how long it takes to launch Outlook. Spikes in the transaction trend would show IT when Outlook launch times were slow—something that would affect real user productivity. But the time that it took to complete a process is generally the only metric returned by synthetic transactions; meaning that, while they do a good job of alerting IT to a potential problem, they don’t provide the context needed to find the solution.
Read the entire article here, Using Synthetic Transaction Monitoring to Track SaaS SLAs
Via the fine folks at Lakeside Software.