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State of Observability 2021: Early Investments in Observability Improve Performance, Customer Experience and Bottom Line

With every organization now being a digital organization, observability should be viewed as a core competency, not a cutting-edge differentiator, according to The State of Observability 2021, a report from Splunk in collaboration with Enterprise Strategy Group.

The research finds that observability delivers tangible, essential results and high maturity observability practices are correlated with:

■ Much greater visibility across hybrid, multi-cloud infrastructures, resources and performance areas. Mature observability users are 2.9 times as likely to report better visibility into application performance and enjoy almost 2 times better visibility into public cloud infrastructure.

■ Accelerated root cause identification, meaning complex, service-crashing crises are fixed much more quickly, or averted entirely. Leaders are 6.1 times likelier to have accelerated root cause identification (43% of leaders versus 7% of beginners).

■ Faster digital transformation, with more successful results. Organizations with the most advanced observability practices are 4.5 times more likely to report successful digital transformation initiatives.

■ Exploding innovation, with leaders reporting 60% more new services, products and revenue streams than organizations with beginner-level observability.

"The pandemic accelerated digital transformations this past year and observability simply is no longer optional in a real-time economy where multicloud complexity has become standard," said Sendur Sellakumar, SVP, Cloud and Chief Product Officer, Splunk. "Having a robust observability practice means fewer service disruptions, better customer experiences and more successful digital transformations. Observability means full fidelity data visibility not only at the infrastructure level, but also at the application and service level, with end-to-end transaction visibility no matter the technologies involved."

A significant percentage of respondents also say they have suffered material consequences for service failures that better observability practices could have prevented:

■ Lower customer satisfaction (45%)

■ Loss of revenue (37%)

■ Loss of reputation (36%)

■ Loss of customers (30%)

Additionally, gaps in observability hurt the bottom line and customer satisfaction:

■ 53% of leaders reported that app issues have resulted in customer or revenue loss.

■ 45% reported lower customer satisfaction as a result of service failures.

■ 30% reported losing customers as a consequence.

The report also highlights concrete recommendations for organizations as they look to improve their observability practices, including prioritizing data collection and correlation, as well as making use of AI, ML and automation.

Methodology: The global survey was conducted from mid-February through mid-March 2021 and in partnership with the Enterprise Strategy Group. The 525 respondents, IT and ITOps leaders and practitioners, were drawn from nine global regions and from organizations with more than 500 employees and an existing observability practice.

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APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

State of Observability 2021: Early Investments in Observability Improve Performance, Customer Experience and Bottom Line

With every organization now being a digital organization, observability should be viewed as a core competency, not a cutting-edge differentiator, according to The State of Observability 2021, a report from Splunk in collaboration with Enterprise Strategy Group.

The research finds that observability delivers tangible, essential results and high maturity observability practices are correlated with:

■ Much greater visibility across hybrid, multi-cloud infrastructures, resources and performance areas. Mature observability users are 2.9 times as likely to report better visibility into application performance and enjoy almost 2 times better visibility into public cloud infrastructure.

■ Accelerated root cause identification, meaning complex, service-crashing crises are fixed much more quickly, or averted entirely. Leaders are 6.1 times likelier to have accelerated root cause identification (43% of leaders versus 7% of beginners).

■ Faster digital transformation, with more successful results. Organizations with the most advanced observability practices are 4.5 times more likely to report successful digital transformation initiatives.

■ Exploding innovation, with leaders reporting 60% more new services, products and revenue streams than organizations with beginner-level observability.

"The pandemic accelerated digital transformations this past year and observability simply is no longer optional in a real-time economy where multicloud complexity has become standard," said Sendur Sellakumar, SVP, Cloud and Chief Product Officer, Splunk. "Having a robust observability practice means fewer service disruptions, better customer experiences and more successful digital transformations. Observability means full fidelity data visibility not only at the infrastructure level, but also at the application and service level, with end-to-end transaction visibility no matter the technologies involved."

A significant percentage of respondents also say they have suffered material consequences for service failures that better observability practices could have prevented:

■ Lower customer satisfaction (45%)

■ Loss of revenue (37%)

■ Loss of reputation (36%)

■ Loss of customers (30%)

Additionally, gaps in observability hurt the bottom line and customer satisfaction:

■ 53% of leaders reported that app issues have resulted in customer or revenue loss.

■ 45% reported lower customer satisfaction as a result of service failures.

■ 30% reported losing customers as a consequence.

The report also highlights concrete recommendations for organizations as they look to improve their observability practices, including prioritizing data collection and correlation, as well as making use of AI, ML and automation.

Methodology: The global survey was conducted from mid-February through mid-March 2021 and in partnership with the Enterprise Strategy Group. The 525 respondents, IT and ITOps leaders and practitioners, were drawn from nine global regions and from organizations with more than 500 employees and an existing observability practice.

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The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...