This is part two of a three-part blog series on Observability—the challenges and the solutions.

The average organization can have ten or more monitoring or observability tools in their IT stack. These tools keep generating an overwhelming amount of noise. IT Ops, NOC and DevOps teams drown in this noise and can’t focus on real incidents until it’s too late. Your organization’s alerts don’t have to turn into an untameable tsunami with no end in sight—there’s a better way forward.

Breaking down cloud management platforms and hybrid/multicloud management In our recent Whiskey and Wisdom session, we discussed how ITOps teams are coping with the evolution of cloud management. Whiskey and Wisdom is a monthly executive-only forum where IT operations leaders can network independently and discuss high-level AI operations and ITOps strategies with their industry peers.

Organizations are on a path to increased and improved digitization, with hundreds of applications, services, microservices and servers running in a multi-cloud or hybrid environment. As IT looks to move away from operations, they are turning to AIOps to ensure compliance, cybersecurity and threat management.

ScienceLogic’s SL1 is engineered to excel in today’s hybrid IT environments, discovering legacy gear buried in your on-premises data center as well as services and applications that live out in the cloud. SL1 is serious AIOps for IT operations teams that are serious about getting the most out of their investments in IT.

Good people over at Splunk explain that the platform “removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.” Splunk is a unified security and observability platform that allows companies to go from visibility to action quickly and at scale.

There’s a familiar saying: garbage in, garbage out. For ITOps, this directly applies to data engineering. BigPanda’s Area Vice President of Value and Adoption, Craig Ferrara, says the importance of data hygiene—putting good data in to get good data out—is the core of data engineering, and it requires ITOps to take a look at their data before integrating with an AIOps solution.