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Observability Survey 2023: Centralization Saves Time and Money

Organizations are challenged by tool sprawl and data source overload, according to the Grafana Labs Observability Survey 2023, with 52% of respondents reporting that their companies use 6 or more observability tools, including 11% that use 16 or more.

To combat this, 70% of respondents say their companies have centralized observability, and of those, 83% have saved time or money as a result.

Other key takeaways from the report include:

Tool and data overload varies across industry and company size

Larger organizations tend to use more data sources: 41% of companies with more than 1,000 employees pull in 10+ data sources, compared to just 7% for companies with 100 employees or fewer.

Industries leading in observability tool usage were financial services (31% use 10 or more tools) and government (27% use 10 or more tools).

Organizations are at different stages in their observability journey

Nearly one-third of respondents have not centralized observability yet, and some industries are further along than others.

For example, 70% of financial sector companies have adopted centralized observability and saved time and money as a result, compared to 58% across all sectors.

Not all ROI is the same

Different organizations have different objectives with their observability strategies. Yes, saving money is the overarching goal, but there are multiple paths to get there, including MTTx improvements, less toil and infrastructure maintenance, better adoption, increased developer productivity, less complexity, service level objectives (SLOs), better capacity planning, and better alerting and visibility.

Among all respondents, 37% say they prioritize capacity planning when correlating data, 25% prioritize cost control, and 9% prioritize profitability and margin calculation.

Accountability, market maturity comes to observability

The proper execution of SLOs is a good sign of a mature observability strategy. Most respondents say they are using them or moving in that direction, but they're not all at the same stage. Moreover, only slightly more are actively using SLIs/SLOs (28%) than those that don't have them on their radar (21%).

Methodology: Grafana Labs developed the survey — which included questions about the tools, strategies, benefits, and challenges around observability — and solicited responses through newsletters, live events, social media, and its own website. The company also conducted interviews with observability practitioners about how their companies are addressing some of the benefits and challenges presented in our key findings.

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Observability Survey 2023: Centralization Saves Time and Money

Organizations are challenged by tool sprawl and data source overload, according to the Grafana Labs Observability Survey 2023, with 52% of respondents reporting that their companies use 6 or more observability tools, including 11% that use 16 or more.

To combat this, 70% of respondents say their companies have centralized observability, and of those, 83% have saved time or money as a result.

Other key takeaways from the report include:

Tool and data overload varies across industry and company size

Larger organizations tend to use more data sources: 41% of companies with more than 1,000 employees pull in 10+ data sources, compared to just 7% for companies with 100 employees or fewer.

Industries leading in observability tool usage were financial services (31% use 10 or more tools) and government (27% use 10 or more tools).

Organizations are at different stages in their observability journey

Nearly one-third of respondents have not centralized observability yet, and some industries are further along than others.

For example, 70% of financial sector companies have adopted centralized observability and saved time and money as a result, compared to 58% across all sectors.

Not all ROI is the same

Different organizations have different objectives with their observability strategies. Yes, saving money is the overarching goal, but there are multiple paths to get there, including MTTx improvements, less toil and infrastructure maintenance, better adoption, increased developer productivity, less complexity, service level objectives (SLOs), better capacity planning, and better alerting and visibility.

Among all respondents, 37% say they prioritize capacity planning when correlating data, 25% prioritize cost control, and 9% prioritize profitability and margin calculation.

Accountability, market maturity comes to observability

The proper execution of SLOs is a good sign of a mature observability strategy. Most respondents say they are using them or moving in that direction, but they're not all at the same stage. Moreover, only slightly more are actively using SLIs/SLOs (28%) than those that don't have them on their radar (21%).

Methodology: Grafana Labs developed the survey — which included questions about the tools, strategies, benefits, and challenges around observability — and solicited responses through newsletters, live events, social media, and its own website. The company also conducted interviews with observability practitioners about how their companies are addressing some of the benefits and challenges presented in our key findings.

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In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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