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.
The results are in from Data-Driven IT Automation: A Vision for the Modern CIO. We were overall very pleased with the data, which was consistent and sometimes even revelatory ...
The role of the CIO is evolving with more of a focus on revenue and strategy, according to the 2019 Global CIO Survey from Logicalis ...
Organizations face major infrastructure and security challenges in supporting multi-cloud and edge deployments, according to new global survey conducted by Propeller Insights for Volterra ...
Developers spend roughly 17.3 hours each week debugging, refactoring and modifying bad code — valuable time that could be spent writing more code, shipping better products and innovating. The bottom line? Nearly $300B (US) in lost developer productivity every year ...
While remote work policies have been gaining steam for the better part of the past decade across the enterprise space — driven in large part by more agile and scalable, cloud-delivered business solutions — recent events have pushed adoption into overdrive ...