In today's fast-paced digital world, things must run smoothly. One outage for service will bring all operations to a standstill, causing financial loss, frustrated customers, and overwhelmed IT teams. That's why automation has taken the game of incident management to a whole new level by bringing together Splunk and PagerDuty. Here, companies transform how they detect, escalate, and resolve their system failures, thus reducing downtime and making it easier for IT teams. Vidushi Sharma has led these automation-driven solutions, helping organizations shift away from old, manual processes. She has helped build a system with Splunk's powerful log analysis, where anomalies are caught in real-time, eliminating the need for constant manual monitoring. With this integrated with PagerDuty's alerting and escalation tools, incidents are assigned to the right teams instantly. The result? A 40% faster response time and a 30% improvement in mean time to resolution (MTTR). Taking it even further, she used machine learning models to classify incidents intelligently. Urgent ones get the necessary attention while low-priority alerts don't clog up the system. Of course, finding the problem is half the battle the real challenge is figuring out what's causing it and fixing it so that it does not snowball. That's where the difference has been substantial, in making advanced search capabilities in Splunk. Instead of wasting hours digging through logs, teams are now able to pinpoint almost immediately the root cause of the problem, says Vidushi. On top of this, self-healing automation workflows in PagerDuty, which she and her team applied, now automatically address recurring issues by performing service restarts or rollbacks without the need for human intervention. Because of these changes, organizations have seen a 60% drop in manual resolutions, allowing IT teams to tackle bigger challenges. Another shift has been the data-driven approach to incident management. With Splunk real-time dashboards set up, that Vidushi and her team have built, teams now have a clear, live picture of such key performance metrics as MTTR, MTTA, SLA adherence, and escalation trends. This visibility has made it possible for leadership to make better decisions and head off bottlenecks becoming more severe problems. At the same time, PagerDuty's automated escalation policies ensure that critical incidents never fall between the cracks, preventing late escalations by up to 50% while improving SLA compliance by 25%. To make it smoother, Vidushi also contributed to building a shared knowledge that provides access to troubleshooting guides and best practices, resulting in a 20% faster rate of resolution across the board. When asked about the trends in the field, she tells us that the future of incident management is all about AI-powered predictive analytics and adaptive automation. Instead of waiting for something to break, machine learning models will soon be able to predict failures before they happen, allowing teams to address potential issues proactively. Multi-modal AI insights will also provide richer, real-time analysis, helping companies make informed decisions on the fly. As IT infrastructure grows in complexity, the ability to anticipate, prevent, and resolve incidents by having a smart, automated system will be crucial to staying ahead of disruptions. Vidushi Sharma’s work in integrating Splunk and PagerDuty has already changed how organizations handle incident responses and the results have been faster, smarter, and more efficient. As companies continue to scale their digital operations, her contributions to automation-driven incident management will serve as a foundation for future advancements in IT resilience and operational efficiency. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program.
