There is a now classic definition of Application Performance Monitoring (APM) and it is the one proposed, and systematically updated, by Gartner. In its APM 2019 Magic Quadrant, the American consulting company defies APM suits as one or more software components that facilitate application monitoring to meet three main functional dimensions:
- digital experience monitoring (DEM)
- application discovery, tracing and diagnostics (ADTD)
- Artificial intelligence for IT operations (AIOps).
This description takes into account a complex contemporary context in which APM solution providers are called to expand their support to be able to monitor hybrid and multicloud infrastructures, business processes and automated systems dominated by machine learning.
Here is why APM tools will grow more and more
In terms of strategic planning, Gartner predicts an increasing weight of APM within companies that offer digital services. The latter, in fact, will quadruple the use of Application Performance Monitoring tools due to the incremental digitization of business processes. Between 2018 and 2021, the phenomenon should affect 20% of all business applications. An evolution that will be led by the top vendors in the segment.
As is known, the Gartner quadrant is divided into four fields within which companies are placed that are characterized by being "challengers, leaders, niche players and visionaries" with respect to the specific technology being analyzed. A classification to keep an eye on when deciding how to orientate yourself in choosing a partner that offers APM systems suited to the needs of your company. Fortunately, today the choice can be made between several options, given that the APM world has definitively got mature enough.
How impactful is the Application Performance Monitoring market
It is not only Gartner that foresees the growing diffusion of APM among companies in the future. Other researches assign the global market that refers to it a value of 4.629 billion dollars, with an expectation that should reach 8.773 billion dollars by 2025 and a CAGR, that is a compound annual growth rate, of 11.25%. An increase explained by the evolution of the digital transformation to corporate architectures. In fact, born as service level monitoring systems in an era prior to the pervasive adoption of multicloud, mobile and IoT devices, today APM solutions continue to maintain the same tasks, but they are based on greater infrastructural complexity. To ensure a quality experience for all those who use the application park, whether internal (employee experience) or external (customer experience), APMs must monitor in real time, in addition to apps, servers, storage, networks and, in the case of custom applications or those difficult to monitor, even logs.
The end-to-end vision required for a modern APM solution
Log management and monitoring, in particular, represent added value with which the collected data can be made available for analysis, export or the creation of specific alerts. But here’s more. APM tools are also able to anticipate potential problems, making use of artificial intelligence and machine learning with a view to predictive maintenance. But the mutation of the APM does not only concern the technological side, as its traditional role has also changed. Today, in fact, APM software covers a strategic function, also focused on business and not exclusively, as in the past, on systems-related performance. For this reason, what is asked of an Application Performance Monitoring dashboard is an end-to-end vision that establishes a competitive advantage over the competition. This is because the customer's approach is now not limited to evaluating metrics such as response time and application availability, but also measures the level of usability of the application and, therefore, the overall experience that derives from it in terms of productivity, increase in customer engagement, and full satisfaction of expectations.
The optimization of internal processes with APM monitoring
The constant control of the application park also generates optimization in the management of internal processes. In fact, the ability to monitor a service along the entire chain, from the user experience to the code, makes it possible to facilitate and speed up the exchange of information between the various company levels, helping to improve communication between them. To achieve this result, the analysis of APMs must govern the variety and fluidity of the IT infrastructures used, in which on-premise and cloud-based platforms coexist in its variants (Public, Private and Hybrid) and to which is added a growing adoption of microservices and containers. The latter, among other things, since they only virtualize the operating system, further complicate the discovery of the link between infrastructure and measurement of application performance. A difficulty that can be overcome thanks to the modern APM systems that cross the entire application chain, managing to isolate any criticality taken individually, without forgetting its interdependencies. The same ones that Gartner's list refers to when it talks about the ADTD dimension to be met, that is, the "search for applications, tracking and diagnostics".
Machine learning and speed of response to application problems
Tracking and diagnostics focus on millions of concatenations, which only navigation with advanced analytics tools can examine. Indeed, as anticipated before, the amount of data requires that the Application Performance Monitoring have a machine learning engine so as to automate the predictive analysis processes. To this end, the possible integration between APM and other systems such as those relating to IT service management (the platform offered by the cloud provider ServiceNow, for example, is one of these) strengthens the transversal and inter-company monitoring capacity. In this way, the speed of finding problems, which even with artificial intelligence can be identified in advance, pairs with the collection of the information necessary to solve them. This is probably the central role that APM has the task of playing today: shortening the time between detecting a critical element that negatively impacts the user experience and remedying it before it affects business productivity levels or before it becomes a cause of churn and abandonment of the application by the customer.