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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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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

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...