Customers can easily lose their patience with digital applications that do not offer them the speed and intuitive experience that is customary with market leaders such as Chase, Amazon, or Google. This means that businesses really cannot afford to tolerate any issues that may arise within applications and services that directly affect the experience of their customers.
Despite the large number of monitoring tools within most enterprises today, high impact outages are still all too common. This is why we need to take a hard look at how we monitor – or rather – observe. Usually, systems are defined by three properties – functionality, performance, or testability. But now, it’s essential that we add observability to this list. In simple terms, observability entails linking applications performance through to business transactions and KPIs.
With digital transformation agendas demanding speed and stability, it’s crucial that digital-age applications be observable and disseminate information about their performance and functioning. Coding for digital applications is not complete until monitoring analytics is built into the code to attain a holistic view of the health of key business services. This is why observability is essential for a successful DevOps-based operating model.
Monitoring of enterprise networks, infrastructure, and legacy applications is still critical in the current environment. Gartner reports that many enterprises already have over 15 monitoring tools on average. So, there’s no dearth of data available from different domains to attain a clear, business-performance-connected state of observability.
Data already available with IT organizations can provide useful insights, however, it’s near impossible to deal with this torrent of data. Technologies such as AI and ML can help uncover useful insights from this data through reducing clutter from various events, minimizing false positives, detecting anomalies, and eventually applying root-cause analysis for expedited issue resolution.
For example, I was recently talking with an eCommerce business unit leader who was looking to pin down exact rates of cart abandonment stemming from system performance and slowness. So, we applied AI to attain deep insights into IT infrastructure and application performance in conjunction with user transaction journey to rapidly provide answers. Businesses are less concerned about CPUs and more interested in how their end customers and suppliers are impacted by business applications performance.
AI and ML-driven solutions give you the ability to extract, correlate, and baseline business outcomes from application performance – and this provides actionable insights to drive results and alert you on critical business metrics. Business-value-chain performance can be baselined at a more granular KPI level and can be monitored with unique approaches to AI technology. Additionally, AI makes user-experience mapping possible and can provide a holistic visual view of user experiences throughout an entire application.
To summarize, monitoring is essential, but digital applications following DevOps constructs require observability to get deep insights into application and business performance. Just as the sum of the whole should be greater than its parts, data aggregation and analysis from different IT domains will have much greater value when it is extracted – rather than sitting in silos – and AI can deliver this value.
ADMnext can help you formulate a successful strategy that looks to mining and analyzing data via advancements in Big Data, AI and ML algorithms, and visualization techniques. In part three of this series, I’ll delve into the orchestration pillar of the AIOps framework. In the meantime, please contact me here to get started on building your AIOps strategy – and be sure to visit us at ADMnext here.