High network latency, memory leaks, slow page loads, heavy CPU usage, and unresponsive servers are all typical performance issues we’ve experienced at some point when using or accessing digital applications.

With how easy they occur in projects across verticals, you might be wondering whether the development teams behind these programs have done enough due diligence prior to the release. But human errors and oversight aren’t always the culprit.

The reality is that while developers can strive to develop a fully functioning program with virtually no apparent faults upon delivery, no software is truly error-free. Even the most rigorously tested applications will experience downtimes during their operations.

But why? From the outset, no amount of debugging and testing can truly prepare developers for how their applications would perform in the real world. Numerous development tools, including Chrome DevTools, Airbrake, and dbForge SQL Tools, may aptly provide key performance insights, detect early signs of software regressions, and offer rich contextual data to guide developers on how to patch any emerging problems. But ultimately, the development tools at our disposal cannot precisely simulate the unpredictable and frenzied nature of a live production environment, which abounds with tangled networks of IT configurations and hardware infrastructure of varying conditions.

With such limitations, it’s incredibly paramount to have a set of preventive measures to mitigate any potential errors that hinder user experience. Given that many things could potentially go off the rails when the finished software product goes live, robust performance management is highly integral to your application lifecycle.

Why is application performance management (APM) critical?

Your software development journey doesn’t end with a product release. In fact, as important as it is to acquire prospects and users upon the product’s initial rollout, a much greater priority is to tailor the software’s trajectory to the target audience’s changing preferences to optimize usability. Most often, this requires constant introduction and recalibration of more robust features.

While these modifications enable your company to remain apace with the market demand, your software architecture runs the risk of becoming unnecessarily complex, leading to lower speed and capacity. Left unabated, any further changes to your software can invite a host of reliability issues that dissatisfy users, which, in turn, hurts your profitability and brand reputation even more.

Though software bugs and defects are part and parcel of application development, it doesn’t necessarily mean that companies have their hands completely tied in keeping performance-related risks at bay.

With that said, here are four digital solutions you can adopt to address common performance issues in your application:

  1. Opt for a scalable APM tool

    The rise of modern development practices and cloud-based technologies (i.e., DevOps and Agile cycles, serverless computing, microservices, Kubernetes, and Docker containers) has facilitated concurrent and more frequent sprints across projects—resulting in faster deployment and adaptability to new business initiatives.

    This dramatic change in scope and approach has rendered periodic sampling and monitoring in traditional APM tools largely ineffective for applications operating on more flexible distributed environments with scalable requirements, such as those native to the cloud.

    Unlike their traditional and monolithic counterparts, modern distributed applications typically consist of portable components deployable at any given time across dependencies, frameworks, and programming languages. To accurately gauge software performance in such a fast-moving landscape, companies can invest in an APM tool with advanced analytical tooling known as observability.

    A relatively recent IT trend, observability offers deep insights into modern distributed applications by providing uninterrupted performance data collection and analysis to expedite problem identification and resolution. Unlike conventional monitoring, which heavily pivots on predetermined baselines, observability can actively detect patterns, properties, and deviations across data sets even with undefined benchmarks.

    This nifty instrumentation can greatly assist the development team during the early phases of code production in finding and rectifying any issues before they impact the live environment. In addition, observability can scale its operations within any node cluster as needed and automatically gather critical data upon deployment.

  2. Embrace big data

    With the production environment becoming more distributed to accelerate delivery, it is natural to assume that troubleshooting any high-priority or intermittent incidents will be just as fast. But that isn’t always the case.

    The main reason behind this is the inherent complexity of managing the database that underpins decentralized nodes (connected devices). Without proper documentation, it notably takes longer for your team to implement an appropriate configuration management procedure, much less isolate and fix identifiable issues. The problem worsens when the repairs for specific components require domain-specific tools, forcing the team to make substantial recalibration.

    Our proposed solution is to integrate big data into your APM strategy so you can obtain more refined insights when conducting a root-cause analysis. Having big data within reach will also significantly improve how your company’s incident alerting system notifies your team of any anomalies, high-risk actions, and system failures in the environment.

    With big data, you can process information much quicker since it eliminates the criticality of hypothesizing issues, designing variables sampling, and conducting preliminary experiments to test available theories. More importantly, big data helps businesses encapsulate the correct analytics from large data sets without the fear of bias marring the objectivity of the results.

  3. Automate your data analytics pipeline

    Parsing through enormous information in an instance is simply impossible with manual operations. Hence, besides bringing observability and big data onboard, utilizing automated analytics tools, such as artificial intelligence and machine learning, is just as crucial to performance telemetry and data crunching. These automated tools not only analyze massive troves of performance data you need but also capture the underlying framework that structures this information (metadata).

    What’s more, you can bolster your multipronged APM strategy with data visualization. This optic-based tool can aptly map out how each dependency in your application interacts and influences various external factors over a certain period.

    Combining all the instrumentations above can significantly enhance your team’s response time to an incident. Likewise, it allows you to formulate impactful solutions aligned with your business goals.

  4. Provide last-mile monitoring and a comprehensive knowledge base

    No matter how well your application performs on your end, any success indicators will remain immaterial if the end users fail to receive the same level of service. Therefore, knowing how to resolve and prevent technical issues from seeping through the client-side certainly pays dividends.

    Nonetheless, with how divergent network configurations of the clients are, it’s considerably harder to control and assess the variables that impact the application performance. According to a survey by ManageEngine, almost a third of businesses discover most errors in their software products from end users. This statistic suggests despite recognizing the importance of APM, most companies still have relatively inadequate end-user monitoring solutions.

    End-user monitoring affords companies extensive visibility into the application performance across environments from real users’ viewpoints. Unlike APM, which primarily focuses on the telemetry from server-side entry points, end-user monitoring leverages comprehensive data collection and support, especially on platforms accessible by clients, such as mobile, desktop browsers, and even IoT devices. This last-mile optimization empowers companies to acquire real-time insights into users’ preferences, motives, and actions on a given application, which can be used to elevate their overall digital experience.

    While this approach works wonders in improving the application’s response time and service delivery, maintaining transparency with stakeholders about what data you are using remains a priority. Consequently, this imparts a higher level of accountability on your end to keep the information protected and safe.

    To further guarantee end-user satisfaction, you can also allocate self-help resources to aid customers in troubleshooting minor problems quickly on their own. Often referred to as a knowledge base, these resources entail all kinds of materials for instructional purposes, from helpful tutorials and articles to community-led product forums.

The most effective APM strategy is one that covers all your bases by combining multiple technology solutions; these encompass benchmark monitoring and testing, automated data collection and analytics, and a detailed risk mitigation plan. Given that internal and external factors can sway the application performance in myriad possible outcomes, increasing the quality threshold of your project deliverables in every step of your application lifecycle will also immensely reduce the risk of unwarranted software issues.

Want to learn more about how to leverage the best APM strategy? Don’t hesitate to reach out to us below with your inquiries!

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