Context Is Key: Identifying the Signal In the Noise

Organizations of all sizes and across all industries have experienced unprecedented levels of change over the last few years. First, it was the pandemic, then the resulting “Great Resignation,” global supply chain challenges, and the rapid shift to hybrid and remote working. These historic shifts in how and where we work were compounded by staffing shortages and a critical need to digitally transform faster than ever before.

As a result, the strain on technical teams increased more than ever before as customers continue to require more reliable systems, and businesses raced to meet their new and evolving demands. As businesses and their systems become more digitized, their systems, processes, and tech stacks are becoming more complex, and the number of critical incidents is increasing. On the one hand, we see how businesses need to continue their digital evolution to avoid being left behind. On the other, the maze of applications, processes, and data results in added complexity for IT teams to have to navigate on a daily basis. This complexity is only compounded by overwhelming levels of noise across the enterprise, which leads to intense alert fatigue and added difficulties for the incident response process. 

Enter the Incident: How Businesses Can and Should Respond

This noise and complexity are causing DevOps, SRE, and IT teams to spend more time triaging incidents and fixing issues than should actually be needed. According to our recent report, across all industries, 54% of responders are being interrupted outside of their normal working hours, and 42% of participants said they worked more hours in 2021 compared to 2020. This leads to more turnover and higher levels of burnout. Recovering from this downward cycle proves difficult for organizations already contending with a tight labor market.

Complexity, noise, and incident volume are increasing and show no signs of slowing down. The ability to compress the noise, gather situational analysis, and turn event intelligence into context will be key to responding to incidents when they arise, resolve issues quickly, and getting the business back up and running before customers are affected. With the right operational processes and intelligent platforms in place, businesses can alleviate the pressure on teams and empower them to quicken mean time to resolution (MTTR).

Adding to these challenges is the need for constant maintenance of existing systems. Instead of building the next killer app or feature, teams are spending too much time responding to alerts and trying to fix existing software just to keep it running. This maintenance costs companies money in lost opportunities and costs employees time wasted. Complex systems tend to break more often than those that aren’t. As a result, organizations need a scalable, sustainable approach to managing real-time incident response that won’t burn out their employees.

See also: AIOps 2.0: Making Actionable Intelligence Actually Actionable

AIOps: The key to distilling the context from the noise

Businesses that lean into the power of AI and machine learning (ML) will come out on top. These capabilities, when combined with human expertise, provide the context and efficiency first responders need to react to and resolve incidents, both large and small. Effective AIOps solutions reduce noise generated by all the monitoring tools, create the context needed to isolate the probable cause, and apply automation to reduce toil and restore service quickly.

Not all alerts should turn into incidents. With AIOps, IT and developer teams are freed from spending so much time on unplanned maintenance and more time actually building innovative solutions that address business’ most complex and pressing problems. AIOps empowers teams to harness the power of AI and ML to keep them focused on the work that really matters–translating to less manual and complex jobs, and ultimately, happier customers.

See also: The Future of AIOps This Year and Beyond

Applying AIOps to Draw Out Context

Not all alerts should or will turn into incidents. With the right approach to AIOps, businesses can empower their teams to use AI and ML to catch the critical signals in a sea of noise and stay focused on what matters to the business. This is key for getting–and staying–ahead of the competition. This requires an automation-led, people-centric approach, one that reduces noise and drives down MTTR so technical teams can reduce the repetitive, time-consuming work that takes away from innovating and delivering against customer expectations.

The right approach to AIOps enables IT teams to collect and contextualize data from across the business to make smarter decisions faster. An automation-first, people-centric approach helps teams identify issues quicker, accelerate the diagnostic process, and remediate issues before they become detrimental to people and operations. Speeding up the diagnosis process is key to solving the problem faster. Too often, excess time and effort are spent gathering this data, but through automation, businesses will speed up MITTR meaningfully, getting their teams back to innovation. Additionally, teams can leverage automation as the “first responder” in their incident management process, helping to remove the manual toil that’s typically required. For problems when human intervention isn’t required, automated remediation can be applied. That means humans can spend more time on complex issues and use the power of automation as a second pair of hands to draw out needed context to make insights actionable for faster resolution.

AIOps is the next frontier for improving business outcomes by applying the best from AI with human capability to contextualize signals and drive automation across the incident response process. AIOps enables the business to become more adaptable, flexible, and responsive than ever before. The result? Shorter incidents, less time wasted, and minimized impact on operations. Businesses will be left with happier IT and DevOps teams–and, equally important, happier customers.