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How Observability with Dynatrace Can Improve Business Outcomes, Part 2

  Fred McHugh     Jun 15, 2021

advanced observability-dynatrace-ai-automationHow much are utilizing the cloud to support your business initiatives? Cloud environments offer immense benefit, especially as hybrid workforces gain traction. However, they also create unique challenges that legacy software, hardware and strategies are ill-equipped to handle.

One such challenge comes in the form of observability, or more specifically, the lack of it in cloud environments. Observability offers the chance to utilize collected data to improve user experience, reduce downtime, detect other issues that could negatively impact business, but traditional observability strategies just can’t keep up with today’s cloud environments.

In this second article in our two-part series on advanced observability, we’ll discuss how Dynatrace is addressing these challenges and what these solutions can do for your enterprise.

Learn more on this topic by checking out part one here.

Utilizing Automation For Scalability

In part one, we discussed some of the challenges associated with observability at scale. The problems can largely be boiled down to the following:

  • The complexity of cloud environments.
  • The ever-increasing volume of data and alerts.
  • The resource and time commitment associated with monitoring microservices and containers.
  • Siloed data.

According to a report from Dynatrace, “95% of applications in enterprise organizations are not monitored due to siloed tools and burdensome manual effort.”

A common solution is to try and tackle observability through adopting multiple siloed monitoring tools, but this approach only results in wasted resources and wasted time. Instead, enterprises must transform the way they collect and utilize data through artificial intelligence (AI) and automation.

Dynatrace is tacking this problem to offer enterprises continuous, automatic data collection and analysis, which translates to enterprise-grade scalability and end-to-end observability.

Dynatrace OneAgent, which collects all monitored data within the environment, also automatically detects all applications, containers, services, processes, and infrastructure on start-up and in real-time. Instrumentation is also automatic, with zero configuration or code change. Data collection, including high-fidelity data like metrics, logs, and user experience data, begin as soon as the system component becomes available.

Auto-baselining is also included, with Dynatrace’s smart baselining adapting dynamically to environment changes. Finally, and perhaps best of all, updates are automatic as well, reducing ongoing maintenance through continuous, automatic, and secure updates throughout the entire environment.

Getting Context From Your Data

In environments where data is siloed, assessing the health of the system as a whole can be next to impossible. Alerts that may have a common cause can go unnoticed and the underlying issue unaddressed. For this reason, Dynatrace has prioritized offering contextual metadata to help administration teams understand what the raw data is telling them.

Using this metadata, Dynatrace creates a real-time topology map, which captures the relationships and dependencies for all system components up and down the stack, as well as horizontally between services, processes, and hosts. This map reveals the actual causal dependencies for the collected data, and also acts as a key foundational piece that enables the strategic use of AI in observability.

AI Offers The Answers IT Teams Need

Dynatrace’s AI engine, Davis, takes the burden off of IT teams and automates anomaly root-cause analysis, reducing the manual efforts required for advanced observability.

To set it apart from other AI platforms, Dynatrace prioritized the following when designing Davis:

  • Precise code-level root-cause analysis, which allows Davis to pinpoint malfunctioning components in milliseconds.
  • Identification of bad deployments to offer the exact deployment or configuration change that caused an anomaly.
  • Looking beyond the unknown. Davis looks beyond predefined anomaly thresholds to detect any unusual “change points” in the data.
  • Automatic hypothesis testing before making real-time decisions.
  • Removing repetitive model learning or guessing to move beyond machine learning approaches.

All in all, Dynatrace is reducing the manual aspects of advanced observability, making it simpler and easier for enterprises, regardless of the scale or complexity of the IT environment.

Ready for Advanced Observability?

As a leader in software intelligence, Dynatrace is simplifying cloud complexity and accelerating digital transformation for enterprises around the world. Instead of just more data and more time spent gathering it, Dynatrace offers solutions that help enterprises use the data they collect and offer improved business outcomes. Find out what you could be missing from your data and processes -- contact WEI today to leanr more about what's possible with the Dynatrace platform and how you can leverage it for your business.


NEXT STEPS: Find out how Automation and AI is helping companies accelerate innovation for their customers and for their business. Check out our tech brief below to learn more.

Read the paper: Accelerate Business Transformation with DevOps and Automation


Tags  cloud strategy DevOps AI Multi-cloud Automation Dynatrace application performance monitoring cloud monitoring

Fred McHugh

Written by Fred McHugh

Fred McHugh designs and deploys virtualization and cloud solutions for WEI clients. As WEI’s Virtualization, Cloud, and Automation Practice Manager, Fred is one of our subject matter experts on vSphere, vRealize Automation suite, and NSX software defined networking. Fred’s certification include VCAP5-DCA (VMware advanced professional), VCP6-Cloud management and Automation, VCP6-Network Virtualization, Microsoft certified Solution expert-Server infrastructure 2012. Fred also holds a Masters in Computer Information Systems from Boston University.

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