Business data monitoring versus observability: What is the difference?

Find out how an advanced, automated form of business process monitoring can extract insights and solutions from every problem scenario.

By now, many organizations have gone digital, with most teams familiar with the concept of remote working. With that achieved, teams should now shift their focus on workflows and identifying different trigger points that could impact performance.

To do so, managements will need to push further and understand the various activities happening within the system to reduce bottlenecks that could impact internal and external processes.

This process is called observability, and it allows organizations to focus on the process in action, rethink monitoring techniques and adapt to the latest technology paradigm.

So why is observability essential for business resilience, especially in today’s digital-first age?

Observability basics

Firstly, organizations need to understand what observability is, and how it is different from monitoring—a common misinterpretation among executives.

• Observability expands on monitoring and enables the correlation and inspection of raw data to provide deeper insights. It helps an organization identify and reveal the true root cause of a problem, helping teams to remediate the problem. For example, monitoring generates an alert when a node fails in a cluster. Typically, you would run a test on the node itself to monitor whether the node is responding. If the node does not respond, an alert will be triggered accordingly. On the other hand, with observability, the node failure would involve the gathering of data across different sources: the node and the various areas where the node is interacting with.
• The correlating of these data sources will then help determine why something failed. It is all about identifying the root cause, which monitoring on its own may not facilitate easily. Sometimes, the problem could be as simple as a human error.

Knowing something has gone wrong is one thing but understanding what went wrong is more important to preempt recurrences. Instead of concentrating on the defense mechanisms, organizations need to regularly analyze their operations to preempt issues and ensure smooth business operations.

With the avalanche of data coming from increasing numbers of digital sources, digitally transformed firms may end up segmenting that data in silos. This is where operational visibility can help. When this is achieved, ITOps teams, site reliability engineers, developers and management, to name a few, will be able to tap the entire architecture to generate insights that can truly differentiate them against the competition.

In one case, a digital wallet firm was unable to solve its platform issues or locate root causes in time, resulting in payments and transactions not being completed, leading to a loss of funds. This impacted not just its customers, but also the company and its financial institution partners, which incurred monetary loss due to reimbursements and the loss of their customer’s trust. When the firm improved its observability processes with AI, it was able to detect and comprehend the issues found within its platform within 30 minutes—almost 20 times faster than before.

Automating observability management

With the help of artificial intelligence, observability can be automated, and the process can continuously learn and deliver insights.

Through AI-driven observability tools, abnormalities that the human eye can miss will be spotted quickly, and the system will automatically adapting to new settings and conditions quicker than human analysis. Specifically, a good AI-driven observability solution has the following benefits and features:

• The process starts with the successful conversion of metrics, traces and logs into events.
• Next , the events are correlated, identifying potential connections. At the same time, an additional layer of causation analysis focuses on identifying and implementing interventions in the system automatically to rectify any mistakes revealed in the earlier stages of analysis.
• Since AI is involved, an AI-driven observability platform can also raise alerts and compile relevant solutions for selection by human operators to automate the intervention. The process then continues automatically and evolves along the way.

With or without AI, observability management can help organizations understand why things happen by providing insights into how the system behaves and, at times, guide the tweaking of monitoring techniques for continual improvement.

The resultant improvements in agility for problem diagnosis and solutions will help teams build stronger relationships and foster both talent and business growth for maintaining business resilience.

In short, for any organization to understand what could work best for their operations—from business objectives, investment capacity, to development velocity—implementing observability will lead to a better grasp of what is happening at every level.