Open ecosystems are powering the future of intelligent observability
Open source has been a driving force behind this shift for over two decades. However, as organisations transition to emerging software-led technologies, open source will be fundamental in what comes next for observability and how it supports modern organisations to achieve greater flexibility, scalability, and competitiveness.
Growing complexity of modern applications
Cloud-native technologies and DevOps have transformed software delivery, enabling faster innovation and better user experiences. But this shift has also increased complexity, with teams managing more tools, systems, and manual processes. The result? Wasted time, a higher risk of errors, and slower decision-making.
According to New Relic's 2024 Observability Forecast report, 56% of respondents in Australia and New Zealand (ANZ) were most likely to use more than five tools, well ahead of their peers in Europe (43%) and the Americas (35%).
Rather than accelerating innovation or improving metrics like mean time to detect (MTTD) and mean time to resolution (MTTR), the fragmented, piecemeal approach often introduces new challenges such as creating data silos, blind spots, poor data correlation, and added friction from licensing and costs, among other issues.
In fact, 39% of organisations in ANZ identified the volume of monitoring tools and siloed data as the key barrier to achieving full stack observability. The stakes are higher than ever, too, with a US$2.2 million median hourly cost for high-business-impact outages.
A unified ecosystem is the way forward
Additionally, organisations are relying on observability to achieve greater operational efficiency. Over a third (31%) of ANZ respondents indicated that integrating business apps like enterprise resource planning (ERP) and customer relationship management (CRM) into workflows was a key driver for observability in their organisations. It is evident that the traditionally fragmented view of systems from using isolated tools for monitoring leads to significant effort and costs in troubleshooting and preventing poor performance. By consolidating various data sources into a single platform, IT teams gain critical, contextual visibility into system performance, allowing them to understand what's really happening and address problems before they escalate.
An application-agnostic approach to observability enables all software engineers to instrument, create dashboards, and set alerts across the entire technology stack.
Unlocking true intelligence for AI
With AI adoption in full swing, IT teams need to address additional complexity as AI tools bring with them intricate data pipelines, model training and inference processes, and dynamic scaling based on real-time data.
While observability practices of the past focused on gathering and analysing telemetry data to understand and resolve performance issues, the integration of AI technologies will require observability to evolve and expand its capacity to track the specific behaviours and performance of AI components in high volumes.
To fully capitalise on AI, the future of observability will revolve around an open ecosystem of interconnected agents that communicate through natural language APIs. These agents will empower users to automate research and complete complex tasks, driving higher productivity. The system will also provide intelligence within the appropriate context, offering relevant, accurate insights, and recommendations to support better business decision-making.
Predictive analytics fuelled by machine learning can analyse trends in telemetry data to foresee potential system failures or performance bottlenecks before they occur. By identifying these issues in advance, teams can take proactive steps to ensure continuous system performance and reliability, such as scaling resources or adjusting configuration.
The next generation of open, intelligent observability will empower organisations to unlock deeper insights and greater value. An observability platform that integrates with best-in-class technologies will enable organisations to drive growth and accelerate developer productivity by seamlessly connecting workflows and delivering insights.
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Techday NZ
7 days ago
- Techday NZ
Open ecosystems are powering the future of intelligent observability
Observability has come a long way. What began as a smart way to keep systems and software performing well has evolved into AI-strengthened platforms that accelerate innovation, improve productivity, and streamline operations. Open source has been a driving force behind this shift for over two decades. However, as organisations transition to emerging software-led technologies, open source will be fundamental in what comes next for observability and how it supports modern organisations to achieve greater flexibility, scalability, and competitiveness. Growing complexity of modern applications Cloud-native technologies and DevOps have transformed software delivery, enabling faster innovation and better user experiences. But this shift has also increased complexity, with teams managing more tools, systems, and manual processes. The result? Wasted time, a higher risk of errors, and slower decision-making. According to New Relic's 2024 Observability Forecast report, 56% of respondents in Australia and New Zealand (ANZ) were most likely to use more than five tools, well ahead of their peers in Europe (43%) and the Americas (35%). Rather than accelerating innovation or improving metrics like mean time to detect (MTTD) and mean time to resolution (MTTR), the fragmented, piecemeal approach often introduces new challenges such as creating data silos, blind spots, poor data correlation, and added friction from licensing and costs, among other issues. In fact, 39% of organisations in ANZ identified the volume of monitoring tools and siloed data as the key barrier to achieving full stack observability. The stakes are higher than ever, too, with a US$2.2 million median hourly cost for high-business-impact outages. A unified ecosystem is the way forward Additionally, organisations are relying on observability to achieve greater operational efficiency. Over a third (31%) of ANZ respondents indicated that integrating business apps like enterprise resource planning (ERP) and customer relationship management (CRM) into workflows was a key driver for observability in their organisations. It is evident that the traditionally fragmented view of systems from using isolated tools for monitoring leads to significant effort and costs in troubleshooting and preventing poor performance. By consolidating various data sources into a single platform, IT teams gain critical, contextual visibility into system performance, allowing them to understand what's really happening and address problems before they escalate. An application-agnostic approach to observability enables all software engineers to instrument, create dashboards, and set alerts across the entire technology stack. Unlocking true intelligence for AI With AI adoption in full swing, IT teams need to address additional complexity as AI tools bring with them intricate data pipelines, model training and inference processes, and dynamic scaling based on real-time data. While observability practices of the past focused on gathering and analysing telemetry data to understand and resolve performance issues, the integration of AI technologies will require observability to evolve and expand its capacity to track the specific behaviours and performance of AI components in high volumes. To fully capitalise on AI, the future of observability will revolve around an open ecosystem of interconnected agents that communicate through natural language APIs. These agents will empower users to automate research and complete complex tasks, driving higher productivity. The system will also provide intelligence within the appropriate context, offering relevant, accurate insights, and recommendations to support better business decision-making. Predictive analytics fuelled by machine learning can analyse trends in telemetry data to foresee potential system failures or performance bottlenecks before they occur. By identifying these issues in advance, teams can take proactive steps to ensure continuous system performance and reliability, such as scaling resources or adjusting configuration. The next generation of open, intelligent observability will empower organisations to unlock deeper insights and greater value. An observability platform that integrates with best-in-class technologies will enable organisations to drive growth and accelerate developer productivity by seamlessly connecting workflows and delivering insights.
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