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Backend to the Future: How AIOps Is Transforming Application Monitoring

Antonio Piraino

The modern enterprise's IT ecosystem is highly complex and ephemeral. When IT performance lags, and when incidents arrive, IT operations teams need complete visibility across their system infrastructure to address issues properly and efficiently.

Application backend monitoring is the key to acquiring visibility across the enterprise's application stack, from the application layer and underlying infrastructure to third-party API services, web servers and databases, be they on-premises, in a public or private cloud, or in a hybrid model. By tracking and reporting performance in real time, IT teams can ensure applications perform at peak efficiency — and guarantee a seamless customer experience.

How can IT operations teams improve application backend monitoring? By embracing artificial intelligence for operations — AIOps.

Discovery: Separating the Good Data from the Bad

The foundation of effective application monitoring and management is quality data. But to identify "good data," it helps to have a good idea of what constitutes its opposite. "Bad data" is either inaccurate, incomplete, irrelevant, or inconsistent. What every enterprise needs for effective application monitoring is, above all, quality data that can yield actionable insights.

But what kind of data is most essential? Enterprises should approach monitoring with an eye towards both breadth and depth. That means first gathering data across the enterprise's network and infrastructure to take stock of its potential impact on applications, and then taking a "top-down" approach to gain insight into individual applications, their operational environments, and their business functions.

Context: So What Does It All Mean?

Once you have good operational training data — accurate, complete, relevant, and consistent data — it must be contextualized to deliver insights that drive recommendations and automated actions. An unclean "data swamp" that is full of unstructured garbage is of little help to an IT team that must expend significant resources in order to convert it into a "data lake," filled with clean, usable data. No matter how much analytics get thrown at a data swamp — poorly defined data will inevitably yield flawed results, liable to negatively impact the enterprise's bottom line.

The incredible amount of data produced by applications is both a blessing and a curse for the modern enterprise. A blessing, because the more available data there is, the more insight-fueled operational capabilities an enterprise has to work with; a curse, because data must be properly contextualized to be useful. In other words, IT teams don't just need the bare-bones information that data provides, they need metadata to illustrate the relationships among disparate data points to understand the impact of the underlying phenomena and pinpoint the root causes of those phenomena. The AIOps-driven process of applying "context to chaos" is central to providing an all-encompassing view of an application's health.

Transformation: Acting on Data-Driven Insights

Application monitoring solutions that reside in the operating system and provide code-level performance, tracing, application topology mapping, and tracking can provide both incident automation and data-driven recommendations that enable IT teams to prevent issues and preempt the occurrence of potential backend outages. Furthermore, by helping IT teams differentiate between normal occurrences and those that require attention and remediation according to degree of priority, AIOps gives IT teams the insight they need to act, rapidly and efficiently. This "noise reduction" functionality also routes alerts to appropriate teams, reducing inefficiencies and streamlining workflows.

Who Can Benefit?

Which enterprises most stand to gain from application monitoring? While workloads are gaining in complexity and ephemerality across the board, application monitoring is meant for enterprises that most require code-level visibility — those that have either developed many custom applications and/or those that prioritize understanding code function and its impact on applications central to the business' bottom line.

AIOps is facilitating a new era in application monitoring by giving IT teams the tools they need to gain visibility across the breadth and depth of their application stacks. As enterprise workflows become ever more complex and ephemeral, the costs of not adopting AI for operations will become ever more apparent as the benefits of AIOps continue to be felt — from the application end-user to the enterprise's bottom line.

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Backend to the Future: How AIOps Is Transforming Application Monitoring

Antonio Piraino

The modern enterprise's IT ecosystem is highly complex and ephemeral. When IT performance lags, and when incidents arrive, IT operations teams need complete visibility across their system infrastructure to address issues properly and efficiently.

Application backend monitoring is the key to acquiring visibility across the enterprise's application stack, from the application layer and underlying infrastructure to third-party API services, web servers and databases, be they on-premises, in a public or private cloud, or in a hybrid model. By tracking and reporting performance in real time, IT teams can ensure applications perform at peak efficiency — and guarantee a seamless customer experience.

How can IT operations teams improve application backend monitoring? By embracing artificial intelligence for operations — AIOps.

Discovery: Separating the Good Data from the Bad

The foundation of effective application monitoring and management is quality data. But to identify "good data," it helps to have a good idea of what constitutes its opposite. "Bad data" is either inaccurate, incomplete, irrelevant, or inconsistent. What every enterprise needs for effective application monitoring is, above all, quality data that can yield actionable insights.

But what kind of data is most essential? Enterprises should approach monitoring with an eye towards both breadth and depth. That means first gathering data across the enterprise's network and infrastructure to take stock of its potential impact on applications, and then taking a "top-down" approach to gain insight into individual applications, their operational environments, and their business functions.

Context: So What Does It All Mean?

Once you have good operational training data — accurate, complete, relevant, and consistent data — it must be contextualized to deliver insights that drive recommendations and automated actions. An unclean "data swamp" that is full of unstructured garbage is of little help to an IT team that must expend significant resources in order to convert it into a "data lake," filled with clean, usable data. No matter how much analytics get thrown at a data swamp — poorly defined data will inevitably yield flawed results, liable to negatively impact the enterprise's bottom line.

The incredible amount of data produced by applications is both a blessing and a curse for the modern enterprise. A blessing, because the more available data there is, the more insight-fueled operational capabilities an enterprise has to work with; a curse, because data must be properly contextualized to be useful. In other words, IT teams don't just need the bare-bones information that data provides, they need metadata to illustrate the relationships among disparate data points to understand the impact of the underlying phenomena and pinpoint the root causes of those phenomena. The AIOps-driven process of applying "context to chaos" is central to providing an all-encompassing view of an application's health.

Transformation: Acting on Data-Driven Insights

Application monitoring solutions that reside in the operating system and provide code-level performance, tracing, application topology mapping, and tracking can provide both incident automation and data-driven recommendations that enable IT teams to prevent issues and preempt the occurrence of potential backend outages. Furthermore, by helping IT teams differentiate between normal occurrences and those that require attention and remediation according to degree of priority, AIOps gives IT teams the insight they need to act, rapidly and efficiently. This "noise reduction" functionality also routes alerts to appropriate teams, reducing inefficiencies and streamlining workflows.

Who Can Benefit?

Which enterprises most stand to gain from application monitoring? While workloads are gaining in complexity and ephemerality across the board, application monitoring is meant for enterprises that most require code-level visibility — those that have either developed many custom applications and/or those that prioritize understanding code function and its impact on applications central to the business' bottom line.

AIOps is facilitating a new era in application monitoring by giving IT teams the tools they need to gain visibility across the breadth and depth of their application stacks. As enterprise workflows become ever more complex and ephemeral, the costs of not adopting AI for operations will become ever more apparent as the benefits of AIOps continue to be felt — from the application end-user to the enterprise's bottom line.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...