Top 10 Observability Trends to Watch Out For in 2023

With the rapid adoption of cloud-native technologies like Kubernetes and contains, observability has already begun evolving to meet the unique requirements of these environments. Cloud-native observability solutions provide native integration with container orchestration platforms, dynamic service discovery, and automated monitoring of ephemeral resources. This trend allows organizations to gain visibility into the dynamic nature of cloud-native deployments and effectively monitor applications at scale.

In cloud-native architectures, services can be dynamically created, scaled, and terminated. Observability tools leverage service discovery mechanisms provided by the container orchestration platform to automatically discover and monitor these services. This dynamic service discovery capability ensures that observability remains intact even as services change and evolve over time. 

In 2023, the integration of security with observability has become an integral part of cloud operations. As cyber threats continue to evolve and grow in sophistication, organizations cannot afford to treat security and observability as separate silos. By bringing these two domains together, IT leaders can gain a comprehensive understanding of their system's security posture and proactively detect and respond to potential threats.

One of the key benefits of integrating security and observability is the ability to detect anomalies and potential security breaches early on. Observability tools provide real-time insights into system behaviour, allowing IT teams to monitor for any deviations from normal operations. By leveraging security-specific data and threat intelligence within the observability platform, organizations can detect indicators of compromise, unauthorized access attempts, or suspicious activities that may indicate a security breach. The integration of security and observability also enables organisations to perform forensic investigations more effectively. When a security incident occurs, having access to detailed observability data allows security teams to trace the origin of the attack, identify affected systems, and assess the extent of the damage. By combining security logs, network traffic data, and application performance metrics, IT leaders can gain a comprehensive view of the incident, facilitating a faster and more accurate response.

Traditionally, IT teams focused on collecting vast amounts of monitoring data without a clear understanding of how it directly impacted the organization's goals. However, with the rise of complex and distributed systems, there is a growing realization that metrics-driven observability is essential for effective decision-making and improving overall business performance. One of the primary reasons why metrics-driven observability is gaining prominence is its ability to bridge the gap between technical metrics and business outcomes. Instead of drowning in a sea of data, organizations can focus on monitoring the specific metrics that directly impact customer experience, revenue generation, operational efficiency, and other key performance indicators (KPIs). By aligning monitoring efforts with these business metrics, IT leaders can gain actionable insights into the health and performance of their systems and make data-driven decisions.

Metrics-driven observability also enables organizations to measure the impact of changes and optimizations on business outcomes. By tracking relevant metrics before and after implementing a change or improvement, IT teams can assess the effectiveness of their actions and understand how they contribute to business goals. This iterative approach allows organizations to fine-tune their systems and processes continuously, leading to better overall performance.

Distributed tracing has gained significant traction in recent years, and in 2023, it is set to become common practice. As organisations adopt microservices architectures and cloud-native technologies, the need for end-to-end visibility across distributed systems has become paramount. Distributed tracing addresses the challenges posed by highly distributed and interconnected systems. In traditional monolithic architectures, it was relatively easier to trace the flow of requests and understand the behaviour of the system. However, with the rise of microservices and containerized environments, applications are composed of numerous services that communicate with each other. This complexity makes it difficult to identify the root cause of performance issues or failures. Distributed tracing provides a solution by capturing and correlating request traces across various services, enabling IT teams to visualize the entire path of a request and pinpoint issues more effectively.

The increasing adoption of cloud-native technologies and dynamic orchestration platforms like Kubernetes has fueled the need for distributed tracing. These environments are highly dynamic and elastic, with services constantly being scaled up or down, deployed or decommissioned. Distributed tracing helps IT teams understand the impact of these changes on the overall system and facilitates troubleshooting in these dynamic environments. By providing insights into the performance of individual services, latency bottlenecks, and dependencies between services, distributed tracing empowers organizations to optimise their cloud-native architectures and enhance application performance.

In 2023, contextual observability is poised to gain significant popularity as organizations increasingly recognise the importance of capturing and leveraging contextual information to gain deeper insights into their systems. Contextual observability is the practice of enriching monitoring data with relevant contextual information such as user context, business context, and application-specific metadata. By incorporating user context into monitoring data, organisations can gain insights into how system performance affects the end-user experience. This includes understanding how latency, errors, or other issues impact user interactions, conversion rates, or customer satisfaction. 

Contextual observability also allows organizations to align technical metrics with business goals and objectives. By capturing business context and mapping it to monitoring data, stakeholders can directly link system performance to key performance indicators (KPIs) and strategic objectives. This enables data-driven decision-making by providing a clear understanding of how technical metrics impact revenue, operational efficiency, or other critical business metrics. IT leaders can leverage this information to prioritise improvements that have the greatest impact on the organisation's bottom line.

Many companies operating in the observability sector are introducing new work for IT and security teams, regardless of their specific subcategory. While new application performance monitoring (APM) platforms offer a slight improvement in monitoring APM's golden signals, implementing and deploying such a platform can be a significant burden for already overworked IT and security professionals. Despite the software being available for purchase, the effort required to implement it may not seem justified in terms of the perceived return on investment (ROI), resulting in the software being left unused.

Observability products that alleviate the workload for operators will continue to surpass market growth rates. Given the scarcity of skilled personnel in IT departments, companies are compelled to search for software solutions that automate tasks requiring high effort but providing little value. These solutions act as force multipliers for existing teams, allowing them to accomplish more with limited resources. Subcategories such as AIOps (Artificial Intelligence for IT Operations) and observability pipelines cater to these needs by automating processes and streamlining operations. The success of observability products will depend on their ability to reduce toil for IT and security operators. By minimizing manual tasks and offering automation capabilities, these products enable organizations to overcome the challenges posed by staffing shortages and resource constraints. As a result, observability solutions that prioritize easing the burden on operators will continue to experience significant growth in the market, meeting the pressing demands of organizations striving for efficiency and effectiveness in their IT and security operations.

Observability complexity issues are expected to persist in 2023 as organisations continue to grapple with the challenges posed by modern, complex IT environments. While observability offers valuable insights into system behaviour, the increasing scale, diversity, and interconnectedness of applications and infrastructure introduce new layers of complexity that can impede effective observability. As organisations decompose monolithic applications into smaller, independently deployable services, the number of components and interactions within the system increases exponentially. This distributed nature of applications makes it challenging to trace the flow of requests, identify performance bottlenecks, and understand the overall system behaviour. 

Meanwhile, the increased adoption of containerisation technologies, such as Docker and Kubernetes, adds another layer of complexity to observability. Containers are ephemeral and dynamic, making it difficult to track and monitor their lifecycle and interactions, whilst Container orchestration platforms introduce additional complexities, including auto-scaling, service discovery, and load balancing, which must be accounted for in observability strategies. Extracting meaningful insights from the vast amount of monitoring data generated by these complex architectures requires sophisticated tools and approaches.

As macroeconomic tensions tighten in 2023 and more organisations recognise the importance of observability for efficient operations, CFOs will be looking to optimise the financial aspects of implementing and maintaining observability platforms. For one, they will need to focus on consolidating and rationalising the number of observability tools used across the organisation. This involves identifying redundant or overlapping tools and eliminating them to reduce licensing costs and streamline operations. By conducting a thorough assessment of the tool landscape, CFOs can ensure that the organization is investing in the most cost-effective and efficient observability solutions, and negotiate better pricing and licensing agreements with vendors by consolidating tool usage and demonstrating the potential for long-term cost savings.

CFOs will need to actively engage with IT and operations teams to prioritise observability investments based on business goals and objectives. They must collaborate with these teams to understand their specific needs and determine the most crucial areas where observability can drive cost savings and operational improvements. By aligning observability initiatives with strategic priorities, CFOs can ensure that resources are allocated efficiently, avoiding unnecessary expenses in non-essential areas.

Open standards provide organisations with a framework for seamless integration to ensure that organisations can leverage observability data effectively across their entire technology stack. A number of observability vendors already claim to offer support for open standards, but often this support is limited to marketing. As macroeconomic headwinds worsen, however,  more and more businesses will start demanding full support for open standards to avoid vendor lock-in. This demand will make open standards the standard within any observability product. 

In the dynamic and rapidly evolving technology landscape, organizations need the flexibility to adapt and incorporate new tools and technologies seamlessly. Organisations often have a diverse system comprising various applications, databases, cloud platforms, and infrastructure components. By choosing an observability platform that adheres to open standards, organisations can ensure that their observability solution can easily integrate with their existing systems. This interoperability eliminates data silos and enables a holistic view of the entire technology stack, enabling efficient monitoring and troubleshooting across different components.

The explosion of data coming from multi-cloud and cloud-native environments, paired with the increased complexity of technology stacks, will lead organisations to seek new, more efficient ways to drive intelligent, AI-wired t automation in 2023. Organisations will realise that manual processes alone are no longer enough to handle the huge amounts of data that can be harnessed for better observability and enhanced security. They will move from a myriad of isolated and hard-to-manage tools, to multi-use, AI-powered analytics platforms that offer business, development, security, and operations teams the insights and automation they need. This will lead to observability, security, and business analytics converging as organisations consolidate their tools. 

Organisations have historically struggled to effectively manage their data due to the increasing complexity of dynamic cloud architectures and distributed digital journeys, which have led to a proliferation of data and disparate analytics tools. However, organisations are beginning to shift their focus from consolidating tools for efficient AIOps to embracing platforms that support advanced AISecOps (AI for security and operations). These platforms will break down the silos between observability, business, and security data and integrate them with topology and dependency mapping. As a result, teams will be able to preserve the relationship between different data streams and unlock the complete context required to drive more powerful and precise automation and deliver seamless digital experiences.