Observability Solution Suites Statistics


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Observability Solution Suites Statistics 2023: Facts about Observability Solution Suites outlines the context of what’s happening in the tech world.

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Top Observability Solution Suites Statistics 2023

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Observability Solution Suites “Latest” Statistics

  • High availability, reliable system-wide observation with a 99.99% uptime that doesn’t fall asleep at the wheel incident inquiry is simple to make sure you have all the information you need, gather and save all pertinent metrics.[1]
  • Reduce redundant notifications by up to 90% by automatically correlating events and alerts from any source.[2]
  • By automatically connecting alerts and events from any source, you may cut down on duplicate warnings by up to 90%.[3]
  • Compared to 15% of newcomers, 45% of observability leaders report introducing eight or more new products/revenue streams in the last year.[4]
  • 57% of respondents cited lack of context as the biggest issue with their current monitoring tools.[4]
  • The complexity of managing hybrid multi-cloud setups, according to 58% of executives, is their biggest difficulty.[4]
  • Only 31% of novices and 70% of observability leaders think they have outstanding visibility in their organization’s security position.[4]
  • 74% of CIOs think the managing performance might become challenging as complexity increases.[4]
  • In cloud-native architectures, 76% of CIOs claim that they lack total insight into application performance.[4]
  • Observability solutions provide information on service outages to 78% of software leaders compared to just 12% of laggards.[4]
  • 20% of C-suite executives expect budget increases that are large next year, according to 81% of C-suite executives. ?yed engineers employ hundreds or even thousands of compute instances, along with a lot of containers and microservices.[4]
  • 86% of respondents mention technological challenges, including poor legacy tools and a lack of platform alternatives.[4]
  • The architecture or observability solutions must function, according to 87% of respondents.[4]
  • 91% of data center facilities managers who were questioned reported having dealt with at least one disruptive occurrence in the previous 12 months, including downtime caused by an outage.[4]
  • 82% of businesses have a hybrid cloud approach, while 92% of businesses have a multi-cloud strategy.[4]
  • According to 94% of software executives, observability is essential for creating software.[4]
  • Due to fragmented technologies and laborious human work, business firms cannot monitor 95% of their applications.[4]
  • 99% of executives claim that their culture and observability technologies enable developers to act quickly without worrying about the consequences.[4]
  • 70% of CIOs claim that real-time monitoring of containerized microservices is almost hard.[4]
  • For 90% of surveyed developers, monitoring massively distributed applications presents issues that are orders of magnitude more than those that conventional monitoring solutions can handle.[4]
  • 57% of respondents said their present monitoring tools’ lack of context was their biggest problem.[4]
  • 86% of respondents to Splunk’s survey mention technological challenges, such as poor legacy tools, a lack of platform alternatives, worries about open source tools, and tool fragmentation, as a barrier to the adoption of observability.[4]
  • 87% of survey participants said it’s critical that observability solutions function independently of the architecture.[4]
  • 92% of engineers questioned think observability tools promote better decisions.[4]
  • The size of the worldwide cloud computing market is anticipated to reach 1,251.09 billion by 2028, growing at a CAGR of 19.1% from 2021 to 2028.[4]
  • 82% of businesses using cloud infrastructure workloads say they’ve spent money that wasn’t required.[5]
  • Because of severe data system performance concerns, 50% of imported data was often not processed for true digital.[5]
  • Because of this, the business could maintain 99.97% availability even while its data infrastructure grew by 2,000%.[5]
  • Cost overruns for public cloud services will affect 60% of infrastructure and operations directors.[5]
  • Acceldata could assist PubMatic in optimizing its HDFS cluster and decreasing its block footprint by 30% in such a large.[5]
  • Authors Sarah Wang and Martin Casado contend that businesses may be able to save anywhere between 30% and 50% of their overall cloud expenditures in that op.[5]
  • Business leaders are dissatisfied with data quality in 73% of cases, and just 61% of firms can use data to preserve a competitive advantage.[6]
  • On average, in 2022, data professionals will devote 40% of their day to problems with data quality.[7]
  • Kingold fixes 95% of its key problems before they influence consumers and can identify performance issues 10x quicker.[8]
  • Businesses may save up to 60% on operational management expenditures by using oracle enterprise manager.[8]

Also Read

How Useful is Observability Solution Suites

Observability solution suites typically include a combination of monitoring, tracing, and logging capabilities, allowing organizations to gain insights into their systems’ behavior and performance in real-time. By capturing data from various sources and correlating it to provide a holistic view of the entire system, these tools empower teams to identify and respond to issues more efficiently, ultimately reducing downtime and improving overall reliability.

One of the key advantages of observability solution suites is their ability to bridge the gap between different layers of the stack, providing end-to-end visibility that traditional monitoring tools often lack. This holistic approach enables teams to quickly pinpoint the root cause of problems, whether they stem from the application code, infrastructure, or external dependencies, and take appropriate action to resolve them.

Furthermore, observability tools enable organizations to proactively identify potential issues before they escalate into major incidents, by setting up alerts and automated actions based on predefined thresholds and anomaly detection algorithms. This proactive approach not only helps improve system reliability but also frees up engineering teams to focus on more strategic initiatives, rather than constantly firefighting reactive issues.

In addition to enhancing operational efficiency, observability solution suites also play a crucial role in supporting business outcomes. By gaining deeper insights into user behavior, performance trends, and system bottlenecks, organizations can make data-driven decisions to optimize their products and services, increase customer satisfaction, and drive revenue growth.

Moreover, observability solutions are becoming increasingly essential in the age of distributed and microservices architectures, where traditional monitoring tools fall short in capturing the full picture of complex, interconnected systems. With observability tools, organizations can monitor the health and performance of each microservice, track transactions across services, and correlate data to diagnose cross-service issues effectively.

However, despite their many benefits, observability solution suites come with their own set of challenges. Implementing and managing these tools can be complex and resource-intensive, requiring specialized skills and a substantial investment in infrastructure and training. Moreover, the vast amount of data generated by observability tools can overwhelm teams if not properly curated and analyzed, leading to alert fatigue and decreased effectiveness.

Despite these challenges, the value of observability solution suites in today’s digital landscape cannot be overstated. By providing end-to-end visibility, enabling proactive monitoring, and supporting data-driven decision-making, these tools are essential for organizations looking to stay competitive, resilient, and responsive in an increasingly complex and interconnected world. As technology continues to evolve at a rapid pace, investing in observability solutions will become imperative for businesses seeking to ensure the reliability and performance of their systems now and in the future.

Reference

  1. aiven – https://aiven.io/solutions/aiven-for-observability
  2. atlan – https://atlan.com/data-observability-tools/
  3. newrelic – https://newrelic.com/platform
  4. signoz – https://signoz.io/learn/why-is-observability-critical-for-cloud-native-applications/
  5. acceldata – https://www.acceldata.io/blog/data-observability-80x-roi
  6. ibm – https://www.ibm.com/blogs/journey-to-ai/2022/07/dont-let-your-data-pipeline-slow-to-a-trickle-of-low-quality-data/
  7. montecarlodata – https://www.montecarlodata.com/blog-data-observability-first-data-catalog-second-heres-why/
  8. oracle – https://www.oracle.com/manageability/

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