Emerging observability trends to watch out for
Tags: observability, monitoring, aiops, ai
Published: 2024-03-18 19:33:48

Observability, which refers to the ability to understand the internal state of a system solely by examining the data it generates, is rapidly garnering attention from organizations across the globe. It's promise? To empower organizations with the visibility they need to ​​deliver a superior user experience and ensure their critical systems' overall stability and reliability.

Although observability has already come so far from traditional monitoring tools, it's continually changing with respect to modern software development to cater to your organization's needs. This ongoing evolution, fueled by software engineering trends, constantly pushes the boundaries of what is possible. Let's explore some of these trends that are shaping the future of observability:

**1**.**Unsiloed tools** 
With modern software development relying on disparate systems across different services and providers to ensure agility and flexibility, observability has become more critical than ever.

This increasing importance of observability has led to organizations building their own monitoring or observability tools. However, these tools often fall short, forcing companies to use multiple monitoring tools due to diverse IT infrastructure needs and specialized use cases.

Data confirms this challenge, as studies show that [72% of IT organizations rely on up to nine different monitoring tools](https://dev.to/eyer-ai/the-building-blocks-of-aiops-150k). Another report, the [Grafana 2023 report](https://grafana.com/observability-survey-2023/), indicated that 11 percent of the organizations that participated in the survey used 16 or more monitoring tools. This situation presents glaring problems, including:
* Tools sprawl and Data overload
* Data fragmentation preventing a holistic view of the system's health
* Cost and Resource Drain
* Alert Fatigue and so much more

But the future of observability promises a solution: a one-stop shop for all your monitoring needs. Tools like [Eyer](https://eyer.ai/), with its headless and API-driven architecture, offer seamless integration with existing systems, eliminating complex setups. 

Additionally, [Eyer uses its anomaly detection capabilities](https://eyer.ai/) to learn about your systems and provide deep insights without requiring additional tools. This streamlined approach promises to revolutionize how organizations monitor and manage their complex systems.


**2**.**Conversion of observability, security, and data analytics due to AI**
Artificial intelligence is driving some of the most significant changes in the software engineering landscape, and that, of course, includes the observability landscape. 

Traditional operations struggle with the vast amount of data generated by modern, multi-cloud, and cloud-native environments. This data explosion demands a more automated, efficient, and intelligent approach to data management and analysis. Sounds familiar? Well, yes, AI.

With [artificial intelligence for security and operations (AISecOps),](https://nsfocusglobal.com/wp-content/uploads/2023/10/NSFOCUS-AISecOps-Whitepaper.pdf) organizations can sift through large amounts of data and figure out what is happening in our systems and why, both from a site reliability engineer (SRE) perspective and a cybersecurity professional perspective.

With observability and cybersecurity converging into one tool, organizations can eliminate needless data silos and the proliferation of observability and cybersecurity tools in their ecosystem, unlocking complete context without eating deep into the company's pockets. 

**3**.**Distributed tracing**
While microservices and composable architectures brought significant benefits over monolithic systems for large organizations, they also introduced a new hurdle: efficiently tracing requests across services to identify the root cause of failures.

Modern applications, composed of numerous interacting services, make it challenging to pinpoint exactly where a request fails within its lifecycle, and this is where distributed tracing comes in.

[Distributed tracing](https://aws.amazon.com/what-is/distributed-tracing/#:~:text=A%20distributed%20trace%20represents%20the,the%20initial%20request%20interacts%20with.) is a method for tracking requests through distributed systems, providing a holistic view of a request's journey. It has become especially important with the rise of container orchestration tools that constantly scale and deploy environments. Distributed tracing allows you to understand how these changes impact the overall system and facilitates troubleshooting within these dynamic environments.

Imagine an e-commerce purchase: user authentication, inventory check, payment processing, and order confirmation might all involve separate microservices. If the purchase fails, traditional monitoring wouldn't reveal the specific culprit. Distributed tracing, however, would map the entire request journey, highlighting the exact service where the failure occurred. This empowers developers to pinpoint the root cause quickly and efficiently, ensuring a smoother user experience and faster resolution times.



**4**.**Financial Observability (FinOps)**
Observability already delivers significant financial benefits. Some of these are:
* Consolidating all your company's monitoring tools into one tool and saving up on tools sprawl
*  Reporting anomalous behaviors in our systems before they snowball into outages that cause burn revenue trying to fix
* Saves the time and energy of talented engineers so that they can focus on creating innovative solutions

However, the financial advantages of observability extend even further. [Richard Hartmann of Grafana Labs](https://www.infoq.com/news/2024/02/observability-2024-predictions/#:~:text=Financial%20Observability%20(FinOps)%3A%20Richard,cutting%20in%20revenue%2Dgenerating%20areas.) predicts a data-driven approach to[ FinOps](https://www.finops.org/introduction/what-is-finops/), enabling companies to correlate costs with profit centers. This granular visibility allows for strategic decision-making, preventing situations where cost-cutting measures inadvertently harm revenue-generating areas.

Organizations can track and analyze cloud resource utilization by adopting a data-driven approach to financial management. This empowers them to identify areas for cost optimization and ultimately improve their cloud financial health. Additionally, observability tools will facilitate cost allocation across different departments or projects within an organization, providing even greater transparency into cloud spending.



**5**.**Democratization of observability**
Traditionally, specialized SRE (Site Reliability Engineering) teams have primarily used observability tools and data. However, a new trend, the democratization of observability, is making them accessible to an organization's wider range of teams.

There are several reasons why democratization is gaining traction:
* **Complexity of Modern Systems**: Modern applications are complex and distributed, making it difficult for a single team to have complete visibility.
* **Data-Driven Decision Making**: Many teams, not just SRE teams, benefit from access to observability data to make informed decisions.
* **Improved Collaboration**: When everyone has access to the same data, it fosters better collaboration between teams.

Organizations can achieve democratization through user-friendly interfaces like intuitive dashboards and visualizations. Additionally, self-service analytics empower teams to access data independently, reducing reliance on SREs.

By democratizing observability and its data, we can have:
* **Faster problem resolution:** Issues can be identified and resolved more quickly when more people have visibility.
* **Improved innovation:** Access to observability data can spark new ideas and lead to better product development.
* **Increased accountability**: When everyone has a stake in system health, teams are more accountable.

**6**.**OpenTelemetry**
[OpenTelemetry](https://opentelemetry.io/), often abbreviated as OTel, is an open-source framework for observability. It's a set of tools and standards that helps developers collect data about how their software is running. This data, called telemetry data, includes metrics, logs, and traces. Analyzing this data lets you gain insights into your application's performance and health.

Here are some of the key reasons OpenTelemetry is gaining traction:
* **Vendor-neutral**: It works with a variety of monitoring backends, from open-source tools like Jaeger to commercial offerings. We are not locked into a specific vendor.
* **Focus on data collection and export**: OpenTelemetry is not a monitoring platform; it focuses on collecting and exporting telemetry data. After collecting this data, we can use observability tools like Eyer to analyze this data.
* **Gaining traction**: OpenTelemetry is a CNCF incubating project, and it's becoming a popular choice for observability because of its standards-based approach.


## Conclusion
The field of observability is undergoing a period of significant transformation. Organizations seek new approaches to gain comprehensive visibility and insights as IT infrastructures become increasingly complex. This article explored six key trends that are shaping the future of observability. These trends, including the rise of distributed tracing, the consolidation of monitoring tools, the integration of observability data with business data, and the leverage of AI for automation and anomaly detection, all point towards a future where observability is not just a technical practice, but a strategic business driver. By embracing these trends, organizations can ensure they have the tools and insights they need to optimize performance, improve user experience, and drive innovation.

To get started with your observability journey, join the [Eyer-Boomi mailing list](https://eyer.ai/) and unlock the full potential of your systems now and in the future.