Observability has always been a core DevOps tenet, but achieving and maintaining it is challenging. Most DevOps teams today have been able to achieve some level of continuous monitoring using a set of pre-configured metrics that are relatively easy to track. Observability, however, takes monitoring to the next level by making it simpler to discover the root cause of IT issues before services are disrupted. There is no shortage of observability platforms today; the challenge is determining the best practices that should be put in place to employ them most effectively.

The divide between business and IT bedevils enterprises in many ways. One persistent and growing gap relates to operations observability – the monitoring and optimization of IT systems. Many enterprises fail to capture the full business value of this discipline due to siloed datasets, tools and teams. Customer engagement, efficiency and profitability suffer as a result.

Digital business observability programs help close this IT-business divide and create insights that foster innovation. These programs help ITOps and cloud operations (CloudOps) teams collaborate with data analysts, data scientists and business owners in creative ways. They can combine IT data with business data to deepen their understanding of business opportunities and risks, enabling cross-functional teams to win revenue, improve processes and gain competitive advantage.

On February 28, 2023, join this Techstrong Learning Experience to explore challenges and requirements and see several use cases that demonstrate the benefits of pursuing a digital business observability program.

Building modern applications that provide flexibility and portability can present all kinds of management challenges across the entire IT landscape. But it doesn’t have to be this way. Building modern applications with observability in mind can help streamline the entire software development life cycle by emphasizing transparency, portability, flexibility and data management from the very beginning.

In this session, we will explore the principles and underlying use cases for shifting observability left with a practice called observability-driven design.

Join us as we discuss:

• Adopting open standards toolchains
• Shifting left the activities required for observability, and
• Digital transformation through process improvement

Observability data volume is exploding across digital services and environments. It can give a unique competitive advantage to organizations that seek to gain insights from it and use it to drive the most critical decisions.

A large subset of observability data is in the form of telemetry data. However, telemetry data is complicated and isn’t readily available in most analysis tools. ITOps teams are tasked to effectively control this data and deliver it across teams to serve a range of use cases, from troubleshooting issues in development to responding quickly to security threats, and beyond.

So how do ITOps teams tame this complex problem? The answer is in observability pipelines.

Join Joshua Scott, Senior Product Manager at Mezmo, and learn how observability pipelines help teams better control their telemetry data at scale. You’ll learn how it can achieve a number of outcomes, such as:

• Controlling large data volumes, ensuring that teams can derive insights they need
• Managing costs associated with storing data
• Making data more actionable by shaping it, so teams can cut down the time it takes to act on insights
• You’ll leave with an understanding of how observability pipelines help companies use telemetry data as a competitive advantage within the broader sphere of observability to accelerate your business

Monitoring has been used for decades by IT teams to gain insight into the availability and performance of systems. However, teams today require a deeper understanding of what is happening across their IT environments. Modern infrastructure and applications can span multiple domains, are more dynamic, distributed and must support ongoing change. In this atmosphere, it is more difficult than ever to consistently maintain SLOs. Further, many enterprises are using more than 10 monitoring tools running as siloed solutions. The result? IT is unable to proactively detect and quickly diagnose and address issues, especially when they cross boundaries.

Based on research and conversations with enterprises from various industries, StackState created the Observability Maturity Model. This model defines the four stages of observability maturity. The ultimate destination is level four, Proactive Observability with AIOps. However, even moving from level one to two, or from level two to three, is a huge improvement in your ability to get essential insights into your IT environment.