BigPanda
BigPandaExternal reviews
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Turn your IT support teams into a powerhouse with AIOps and BigPanda!
What do you like best about the product?
BigPanda allows our organization to integrate all our disparate monitoring tools into a single pane of glass. Alerts are intelligently de-duped and correlated through machine learning into a single event, and can be displayed in a timeline view making it easy to determine the first system(s) to detect the issue and which team(s) should be engaged to take action. The analytics views provide rich data on both team and user performance while breaking down detailed statistics around incident handling (MTTD, MTTR, MTTA) and capturing user actions.
What do you dislike about the product?
There is nothing that we dislike about the platform. The platform and our strong relationship with the BigPanda team has provided us with a ton of value throughout our partnership.
What problems is the product solving and how is that benefiting you?
Prior to implementing BigPanda our teams suffered terribly from manual and inefficient incident handling processes. Outages often resulted in fire drills where all IT teams were engaged to participate on lengthy bridge calls that could easily consume an entire day. This was extremely disruptive, and a major distraction, to both project work and innovation. With BigPanda we can now reduce the noise and turn it into actionable insight. The correct teams can be engaged from the start and the unified console provides a means for all teams to have the same visibility. As we continue to mature as an organization we are extremely excited to take advantage of the change management correlation features and continue to build out automated workflows to action incidents as they occur.
Easy software to implement.
What do you like best about the product?
The redemption tickets for all working teams saved at least 50% of our time when closing them. However, so many noise tickets tend to lose focus on the critical, great pandas helped concentrate on the critical warnings.
What do you dislike about the product?
Intelligent correlation is one of the features I am looking for, rather than the rules for correlation. It would also be nice to integrate cloud watch with the SNS or SNP alternative. Also the Service retry system now faces some ticket communication problems. Several advance tracking apps will also contribute to the review of tickets.
What problems is the product solving and how is that benefiting you?
As already stated, noisy tickets (redundant alerts) will be reduced, saving now 50% of the human energy in manually closing them. Regarding calling characteristics it helped to integrate with the pagerduty and some of our personalized method, regarding which we never had a connection mechanism.
Recommendations to others considering the product:
If someone is looking for a solution to reduce tickets with a smart ticket correlation tool, i.e. switch or opt to big panda, I'm confident that this will help a lot with IT operations. It allows you to integrate all of your control tools in variety. Whether it is CatchPoint, Logic screen, Sites, personalized display, solarwind or non-monitoring, big panda offers tools for all your applications and integrations, such as slack or pager.
Good stuff!
What do you like best about the product?
BigPanda is allowing us to shift out effort in a more productive manner. Rather than giving our engineers busy work to close incidents, we are now able to focus on solving issues, performing root cause, and track down methods to automate.
What do you dislike about the product?
I dislike BigPanda's deduplicaiton method. While I understand there is a management piece to the alert storm and this is all part of the noise, it becomes a 'black box' and we cannot peer inside and see what happened to an alert. Adding a GUI element would improve this a lot
What problems is the product solving and how is that benefiting you?
We are using BigPanda to tame the alert storms from our noises platforms. We have also found there is going to be great benefit in the ETL pieces. I hope these continue to be refined and developed.
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