Cloud reliability startup Shoreline.io has now released Shoreline Incident Insights, a free tool that would make it simple and quick for Cloud Ops teams to assess their incidents and boost reliability, client happiness, and on-call experience. The solution uses a machine learning algorithm to filter and organize tickets after automatically ingesting ticketing data from incident management systems.
This approach by Shoreline.io would help locate the underlying reasons for occurrences simpler. Users of the free Incident Insights tool can determine critical metrics like MTTA (mean time to acknowledge) and MTTR (mean time to repair) and highlight the top issues.
Typically, incident data can be disorganized, frequently generated by computers, while containing numerous duplicates. As a result, managers may find it challenging to spot trends and patterns that might raise team productivity. It can take a lot of time to manuallyscrub and aggregate the records each time an analysis is required. Because of the friction caused by the additional work, reports are frequently never produced and opportunities for improvement are lost, stated Shoreline.io.
Out-of-the-Box Reports and Dashboards
Managers using Shoreline.io’s Incident Insights tool may import their data in minutes and start obtaining insights in just a few seconds thanks to the tool’s “easy-to-use” data import engine and pre-built reports. This would end the tedious hours-long process of writing incident reports.
“Too many Cloud Ops teams are flying blind,” said Anurag Gupta, Founder and CEO of Shoreline.io. “I ask leaders which of their teams are carrying the heaviest on-call burden, and they don’t know. I ask which incidents are most common, and they don’t know that either. We need to learn from incidents to continuously improve availability for customers and make on-call better for our teams.”
Incident Insights’ out-of-the-box reports and dashboards would include the following:
- Top Problems – Engineers may concentrate on choosing the best course of action to improve the issue because the top incident categories are automatically derived from the tickets themselves. Users have a great deal of freedom to arrange incident groups according to important criteria like frequency, severity, and MTTR.
- Setting changes are automatically saved, and reports can be filtered by service, user, category, or search. This clarifies the hazy ticket data.
- Detailed statistics, such as MTTA, participants, and links to the original source data, are available when you drill down into a particular category. It’s critical to evaluate the on-call actions taken or a root cause software fix in order to identify the best automation opportunities.
- Operational Efficiency – Summary level data, including incident count by time period, average MTTR, and tickets by service, illustrates how the on-call team is doing. This compares actual performance to customer-promised SLAs and SLOs.
- Team Health – Demonstrates how on-call affects each team, which team members are carrying an excessive load, and where there are gaps in individual performance.
- Historical Trends – Clearly determine if new initiatives are having the desired impact and whether critical indicators are going in the right manner. This would keep the team focused on quarterly and yearly improvement objectives.
Shoreline PagerDuty is already integrated with incident reporting out of the box. Coming shortly are integrations for the ticketing systems Opsgenie, ServiceNow, and ZenDesk.
Shoreline.io holds SOC 2 accreditation. Built by AWS experts, the design would fully incorporate best practices for data security, including end-to-end data encryption in both transit and at rest. As a read-only tool, Incident Insights would not be able to interfere with operational systems.
