How to unlock AI-powered observability insights in your Boomi integration
Tags: aiops, observability, machinelearning, monitoring
Published: 2024-02-12 10:18:29

This article delves into the world of observability, exploring the challenges it addresses within diverse and complex distributed systems. It discusses core observability concepts and unveils how powerful AIOps observability tools like [Eyer](https://www.eyer.ai/) unlock valuable insights from data flowing through your [Boomi](https://boomi.com/) integration. 

The allure of scalable, agile, modular systems is undeniable, offering adaptability and rapid development. Yet, for modern organizations, this shift breeds new complexities, as a more complex ecosystem translates to an explosion of data. Managing and gleaning insights from this vast, diverse landscape becomes daunting. While agility reigns supreme, visibility often gets lost in the shuffle, leaving organizations constantly chasing issues instead of proactively preventing them.

Unfortunately, while you're stuck playing this high-stakes game of operations roulette, the world keeps spinning. Your application throwing errors translates to frustrated users, lost revenue, and dwindling customers. All while you're left scrambling in reactive firefighting mode, incurring additional costs.

But imagine a world where you ditch the firefighting and gain deep insights into your system's health. That's precisely what the observability tool Eyer empowers you to do.

## Understanding the landscape 
While "[observability](https://dev.to/hackmamba/adding-next-gen-observability-with-boomi-and-eyerai-46fm)" might seem recent, different organizations have expressed their core ideas diversely over time. Simply put, observability helps us answer the question: how deeply can you understand your system's internal state by observing its external outputs?

While traditional monitoring tools played a role in understanding systems, they ultimately fell short with increasingly complex distributed systems. Their reliance on pre-defined metrics and siloed data analysis created roadblocks to answering valuable questions. These included:

- **Finding the right balance:** How do you set alert thresholds that catch issues early without triggering an overwhelming stream of notifications?
- **Uncovering root causes:** What context can you gather to understand why an event is happening, enabling effective root cause analysis?
- **Democratizing insights:** How can you empower individuals across various expertise levels to glean meaningful insights and take action based on monitoring data?

Frustrated by unanswered questions about their systems, organizations sought alternatives. Enter observability tools and practices, which harnessed a more comprehensive range of data sources like logs, traces, and metrics. This broader view painted a more holistic picture of the system's inner workings. By providing answers to previously unanswered questions, observability goes beyond the limitations of its predecessors, empowering users to shift from reactive monitoring to a proactive approach.

But even as impressive as this sounds, Eyer takes it further by integrating machine learning principles and lessons learned from integration markets and solutions like Boomi to offer powerful insights.

## Unveiling Eyer’s observability magic

[Eyer](https://www.eyer.ai/), an AI-powered observability tool, seamlessly integrates with your existing Boomi connections, empowering you with transparency across your entire integration landscape. It leverages [Boomi's superpower](https://boomi.com/) in uniting diverse applications and data sources to unlock transformative insights that shatter data silos and streamline operations.

Using Eyer for your heterogeneous, distributed system can be likened to having a real-time pulse on your interconnected systems, anticipating bottlenecks before they arise, and proactively resolving issues that might otherwise impede efficiency.

But how does Eyer do all of these?

**An overview of Eyer operations**
Unlocking insights from your data starts with, well, the data itself! Eyer simplifies this process by using [Telegraf](https://www.influxdata.com/time-series-platform/telegraf/), an open-source server agent. Telegraf seamlessly collects and filters metrics from systems in your Boomi integrations, feeding them directly into the machine-learning pipeline for powerful analysis.


![Boomi Eyer architecture high level](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/slvakvjo06l4wzjrsk85.png)
	    

Once you feed data into Eyer, generating anomaly alerts typically takes around seven days. This period allows the machine learning algorithms to analyze the data and establish baselines for each monitored metric. These baselines represent normal behavior patterns, acting as a key reference point to identify any deviations or "anomalies" that signal potential issues.

Daily and weekly cycles are also considered within this period to capture regular fluctuations in your data. Additionally, more training data leads to better baseline accuracy, reducing false positives (non-existent issues flagged as problems) and false negatives (real issues missed ).



![Eyer Machine learning pipeline overview](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/prbd8q6q3pao1i5v1p9v.png)
	

Take a closer look at these diagrams, showcasing Eyer's architecture. They reveal two distinct approaches to anomaly detection, each working within its branch:

- The **upper purple branch** represents the univariate anomaly detection. It analyzes each time series data individually, looking for points that deviate significantly from the expected behavior based on historical data and learned patterns. 
- The **lower yellow branch** handles correlation calculations and grouping. It analyzes how different time series data points move together or diverge. This helps identify groups of related metrics that behave similarly, representing potential shared causes for anomalies.

By combining the information from these two branches, an Eyer alert would not just point out an anomaly in a specific time series but also tell you which other related time series are experiencing similar anomalies. It provides a more comprehensive picture of the issues, suggests potential root causes and impacted areas instead of isolated data points, and empowers you to make decisions and take action in your Boomi processes. 

Additionally, grouping these alerts helps reduce the number of alerts a user receives, minimizing "alert fatigue." Eyer prioritizes user experience, understanding that bombarding users with alerts can desensitize them, leading to missed critical notifications. Beyond this, Eyer offers several advantages:

- **Lightweight:** Eyer is designed to run in the background without significantly impacting the user's experience or device performance.
- **Easy to use:** It does not require complex setup or configuration and can start collecting and analyzing data immediately.
- **Headless & API-driven:**  Eyer’s headless architecture and rich API support enable flexible integration within your Boomi processes.
- **Automated actions in Boomi:** Enhance your security posture by triggering automated actions within Boomi upon detection of an anomaly. This can involve blocking suspicious connections, sending alerts, or initiating remediation procedures, significantly streamlining your response time and mitigating potential risks.
- **Unsupervised learning:** Eyer doesn't need labeled data; that is, data that has been manually classified as normal or abnormal. It can learn and identify patterns on its own. Check out the [official documentation](https://customer.support.eyer.ai/servicedesk/customer/topic/16a02c98-aeef-4564-9866-27d5652c08ca) to learn more about Eyer's anomaly detection process.


## Summing up

True observability isn't a luxury in a world powered by interconnected systems – it's your lifeline. Can you afford to leave the foundation of your business, the platform upon which your success rests, shrouded in mystery? Hoping and praying you're addressing the right issues, gambling with lost revenue and frustrated users? 

With AIOps observability tools, you can dismantle that black box, empowering you to understand your systems truly. No more guesswork, no more wasted resources chasing phantom problems. Gain a deep contextual understanding of your system. Correlate anomalies with root causes, gain a holistic view of your system's behavior and pinpoint the "why" behind disruptions.

But Eyer doesn't stop there. The next wave of features promises seamless integration with data visualization tools like Grafana, allowing you to transform Eyer's insights into visually compelling dashboards and reports, empowering data-driven decision-making and a new level of clarity.

And the future holds even more. Eyer's machine learning core isn't just analyzing your past; it's actively learning and evolving. This opens the door to predictive analysis, where Eyer studies metric patterns to anticipate anomalies before they occur.

So, why Eyer? Because your business deserves more than just a glimpse into its inner workings. It deserves a partner that empowers true understanding, proactive problem-solving, and a foundation for resilience and success. Start your journey towards better observability today. Join the [Eyer-Boomi integration](https://eyer.ai/) waiting list and unlock the full potential of your systems now and in the future.