Improving MTTR with Elastic Observability
A fintech company significantly reduced incident resolution times thanks to the adoption of Elastic Observability, avoiding customer loss caused by prolonged outages. Before implementation, the company had lost some customers due to the duration and recurrence of outages.
Quick Response
Elastic Observability allowed companies to reduce incident resolution times (MTTR) through a proactive approach. The system integrates logs, metrics, events, and traces in a single platform, reducing complexity and improving operational efficiency.
The Situation Before Elastic
The companies interviewed by Forrester Consulting operated in a fragmented environment, with siloed telemetry and reactive incident management. The expansion of applications, through organic growth, acquisitions, and the adoption of AI workloads, made the existing observability model unsustainable. Organizations found themselves managing six or more overlapping platforms, with increasing data storage costs and incomplete observability coverage.
Critical Needs of Organizations
Companies sought a platform that could unify logs, metrics, events, and traces in one place, scale observability coverage without exploding data storage costs, provide easily usable dashboards for non-technical teams, and include AI and agentic capabilities. Elastic Observability met all these fundamental needs.
Key Results of the TEI Study
For the composite organization analyzed, the shift to a resilient operating model with Elastic Observability led to substantial improvements. Using features such as [ML-driven anomaly detection](https://www.elastic.co/observability-labs/blog/ai-driven-incident-response-with-logs), [root-cause analysis](https://www.elastic.co/observability-labs/blog/reduce-mttd-ml-machine-learning-observability), [application performance monitoring (APM)](https://www.elastic.co/observability/application-performance-monitoring), and the [AI assistant](https://www.elastic.co/elasticsearch/ai-assistant), teams transitioned to a proactive approach, detecting signals before they become incidents and closing the loop more quickly when incidents occur.
Reduction of Downtime and Improved Efficiency
One of the main benefits observed was a reduction in systemic downtime by up to 75% by the third year, protecting $15.2 million in revenue at risk over three years. Additionally, there was a 95% reduction in the time SREs (Site Reliability Engineers) spent on monitoring and incident resolution, freeing up $3.5 million in engineering capacity.
Savings for Developers and Cost Reduction
Developers saved 55% of the time spent on testing, deployment, and debugging by the third year, totaling 263,983 hours saved, equivalent to $6.6 million over three years. Additionally, observability infrastructure costs were reduced by 70% through data tiering, compression, and tool consolidation from six to two.
Improvement in Customer Retention
The adoption of Elastic Observability led to a 0.6% improvement in customer retention by the third year. This result underscores the positive impact of the platform not only on internal operational efficiency but also on customer satisfaction and loyalty.
Reduction in Log Storage Costs
A software engineering director in the telecommunications sector highlighted how Elastic reduces log storage costs. Unlike other vendors that force indexing, Elastic provides models to analyze logs, deciding whether to index them or not. This approach helps reduce log storage costs with other expensive providers.
Building a Business Case for Elastic Observability
Organizations that have adopted Elastic Observability not only solved observability problems but also unlocked compounded returns in terms of engineer productivity, infrastructure costs, and business resilience. To delve deeper into the methodology, composite data, and customer stories behind each number, you can read the full Forrester TEI study and build your own business case.
Impact on Engineer Productivity
The reduction in time spent on monitoring and incident resolution freed up significant resources for engineers, allowing them to focus on more strategic activities. This had a direct impact on productivity and innovation within organizations.
Scalability and Adaptability of the Platform
Elastic Observability proved particularly effective in managing application expansion, including AI workloads. The platform is able to scale observability coverage without exploding costs, making it an ideal solution for growing companies.
Accessibility for Non-Technical Teams
One of the key advantages of Elastic Observability is its ability to provide easily usable dashboards even for non-technical teams. This enabled greater collaboration between different departments and an overall improvement in operational efficiency.
Integration with AI Features
The platform includes AI and agentic capabilities, offering advanced analytical capabilities and improving the ability to proactively detect incidents. This allowed organizations to anticipate problems before they become critical.
Market Trends: The Evolution of Enterprise Observability
The use case analyzed fits into a broader trend of consolidating observability platforms. According to Gartner, the global IT observability market will reach $6.2 billion by 2026, with a CAGR of 14.5%. Elastic positions itself as one of the leading vendors in this sector, with a 12% market share in 2023. Growth is driven by the need to integrate hybrid and multi-cloud environments, with 68% of companies managing at least three different infrastructure environments.
Implications for Cyber Insurance
The operational efficiency demonstrated by Elastic Observability has direct implications for the cyber insurance market. Insurance companies are increasingly evaluating the maturity of observability and incident response capabilities when setting premiums. According to a Marsh study, companies with incident resolution times of less than 4 hours can obtain 15-20% reductions in cyber policy premiums. The 75% reduction in systemic downtime observed in the TEI study could therefore translate into significant savings on cyber insurance premiums.
Impact on NIS2 and DORA Compliance
The platform proves particularly relevant for companies subject to the NIS2 Directive and the DORA Regulation. Both regulations require advanced incident monitoring and detection capabilities. Elastic Observability, with its anomaly detection and root-cause analysis features, helps organizations meet NIS2 compliance and digital operational resilience requirements, while reducing the risk of non-compliance penalties.
Challenges in Adopting AI Features
Despite the benefits, integrating AI features presents some challenges. According to Forrester, 42% of the companies interviewed reported difficulties in maintaining the necessary skills to fully leverage the platform's machine learning capabilities. This raises questions about the long-term effectiveness of AI-based solutions, especially in sectors with shortages of qualified technical talent.
Future Prospects: Integration with Data Observability
A potential area of development is the integration between Elastic Observability and Data Loss Prevention (DLP) solutions. With the increase in breaches of sensitive data, combining application performance monitoring capabilities with advanced data protection techniques could offer unprecedented levels of security. This evolution could position Elastic as a provider of comprehensive solutions for data security and observability.
Considerations for SMEs
Small and medium-sized enterprises could benefit from adopting Elastic Observability, but they must overcome barriers to entry. According to an IDC study, 73% of SMEs cite implementation costs and technical complexity as the main obstacles to the adoption of advanced observability platforms. SOC as a Service or Managed Detection and Response (MDR) solutions could represent a bridge to the adoption of these technologies.
Impact on the API Ecosystem
Elastic Observability's ability to integrate logs, metrics, events, and traces into a single platform has significant implications for API development. According to Postman, 61% of API developers cite observability as one of the main challenges in API management. The adoption of a unified platform could therefore accelerate API development and deployment, while improving their reliability and security.
Forecasts for 2026
By 2026, it is predicted that 70% of large companies will have adopted unified observability platforms. Elastic is well positioned to capitalize on this trend, thanks to its ability to scale observability coverage without exploding costs. However, competition from other vendors such as Splunk, Datadog, and Dynatrace will remain intense, with each provider seeking to differentiate itself through advanced AI and machine learning features.
Towards a Resilient Future
The adoption of Elastic Observability represents a significant step towards building more resilient IT infrastructures. Organizations that invest in these technologies not only improve operational efficiency but also position themselves to address the future challenges of the ever-evolving technological landscape. With the integration of AI features, scalability, and the ability to unify different sources of telemetry, Elastic Observability offers a comprehensive solution for modern observability needs.
Editorial Note and Disclaimer
The guides and content published on GoYou are the result of independent research and analysis activities, for informational, educational, and in-depth purposes.
GoYou does not constitute a journalistic publication nor an editorial product pursuant to Law No. 62/2001 and does not provide real-time information.
The GoYou project does not provide professional, technical, legal, or financial advice and disclaims any liability for the misuse of the information published.
In the Crypto sector, every investment involves risks: readers are invited to always inform themselves independently before making any decision.