While many global tech teams are paddling through a sea of false positives, Singapore’s tech teams are drowning in a sea of alerts – many of which are false alarms. Splunk’s State of Observability 2024 report stated that a staggering 31 percent of respondents in Singapore describe the sheer volume of false positives as “highly problematic”, compared to just 13 percent globally.

This is not just an inconvenience; it’s wreaking havoc. The constant barrage of noise has led to costly downtime and is pushing local tech teams to their breaking point. Alarmingly, the report found that 82 percent of Singapore respondents have seen critical team members leave due to burnout. This echoes broader trends, with more than four out of five IT professionals reporting fatigue and burnout, driven by overwhelming workloads and a lack of resources.

It’s not contentious to say that poor observability tools play a significant role in this cycle, as they contribute to the surge of false positives, which in turn increases workload and ultimately leads to burnout.

Moreover, the financial stakes of ineffective observability are clear: downtime costs organizations an average of $540,000 per hour, yet leading organizations—those with better observability practices—are 2.3x more likely to resolve incidents in just hours or minutes, compared to days or months.

So how can organizations prevent alert fatigue and unnecessary downtime costs? It starts with better observability.

Cultivating effective observability: Go beyond the basics

Effective observability isn’t about deploying as many tools as possible. In fact, too many can result in an overwhelming volume of alerts — our findings show that Singaporean organizations manage an average of 28 tools, resulting in alert fatigue and, ultimately, talent burnout. Many are now ready to streamline their toolsets to alleviate bandwidth issues, recognizing that when teams can’t distinguish between critical and less important notifications, downtime becomes inevitable — bringing serious repercussions that erode customer loyalty and damage public perception.

Nonetheless, building a leading observability practice goes beyond deploying the right tools; it’s about fostering a mindset and culture that strives for excellence. The best teams don’t just aim to avoid poor digital experiences – they have a desire, knowledge, and ability to excel in their endeavor to create exceptional ones. These teams embrace continuous learning about observability strategies and apply that knowledge through tools, training, and processes.

As organizations progress towards leading observability practice, they’ll naturally converge security and observability data and tools. Breaking down silos and sharing dashboards accelerates problem-solving, as SecOps, ITOps, and engineering teams all benefit from the same context to address root issues quickly.

In other words, observability isn’t something you simply possess; it’s something you actively practice across teams — from engineering to security, IT to development.

Observability is not new – but leaders still struggle

As observability continues to evolve, flexibility becomes crucial as organizations face increasing data residency requirements, new data sources, and a diverse array of tools — and OpenTelemetry (OTel) has emerged as the industry-standard solution for collecting observability data, enabling integrations across languages and platforms.

To effectively manage the rising volume of telemetry data, organizations must look at prioritizing their data management strategies, such as data transformation and redaction, data tiering, and aggregation. Best-in-class observability teams prefer integrated solutions to avoid tool sprawl, emphasizing that effective telemetry pipeline management relies on capabilities that streamline operations and deepen insights without the complications of managing separate tools.

Embrace AI for observability

Yes, AI is everywhere these days, but it’s stepping up to deliver genuine solutions in this context. AI-powered observability — particularly through AIOps — is able to intelligently pinpoint and remediate the root causes of incidents with greater automation. The fact that 97 percent of organizations are now using AI/ML-powered systems to enhance their observability operations, a significant jump from just 66 percent last year, underlines this trend.

Leaders are fully embracing AI for observability in all its forms – ML, AIOps, and Gen AI. They are adopting these technologies at significantly higher rates than their peers, with 65 percent extensively using AIOps in their toolsets – over 10 times the rate of organizations that are just beginning their journey. Leaders who are leveraging AI for observability experience a myriad of benefits, including reduced downtime, as faster detection leads to quicker resolution.

Take Singapore Airlines (SIA) for example — to support its digital transformation and enhance the passenger experience, the nation’s flagship airline needed continuous high-service availability across its complex systems. By achieving full-stack visibility, the airline can swiftly detect and resolve issues, maximize service uptime, improve customer satisfaction, and uphold its stellar reputation. Since integrating observability into its operations, SIA has reported over 75 percent faster issue detection and a 90 percent reduction in backend issues.

Staying afloat in a sea of alerts

Despite launching an average of 13.5 new digital products in the past year — more than any other country surveyed — Singapore organizations are grappling with false positives and alert fatigue from their tools. This makes it even more crucial for leaders to adopt AI technologies in order to maintain operational resilience and stay competitive.

As alert volumes overwhelm even the most seasoned tech teams, effective observability is imperative to protect both productivity and talent.

#Observability #AIOps #DigitalTransformation #AlertFatigue #TechInnovation