Think you’ve got your cloud migration covered with observability? Think again.

Over the past few years, vendors from across IT have introduced “observability” tools or platforms. But what’s it really mean? And is it enough?

“People-watching” is something we all do intuitively. Most of us are naturally curious and want to understand and explain how people operate around us. When visiting a new city, for example, people-watching at an outdoor cafe helps us observe and learn about the behavior, social processes and culture of the local ‘system’ of residents.

Not unlike people-watching, the concept of observability is defined as a way to measure how well internal states of a system can be inferred by knowledge of its external outputs. But as systems become more complex, observing these multi-layered systems makes it difficult to accurately understand fully how a system operates and what it means when it comes to making changes.

Looking across the IT landscape today, it seems that many vendors have caught on to the observability term and use it as the latest buzzword. But they reference observability differently. In fact, Gartner states in its report Innovation Insight for Observability, “Many vendors are using the term “observability” to differentiate their products. However, little consensus exists on the definition of observability and the benefits it provides, leading to confusion among I&O leaders purchasing tools”.

We see three major areas of focus as these vendors use the term:

1. IT ops – vendors are addressing the massive amount of data that is being generated by performance and monitoring tools, and the need to better access and understand the data by introducing AI into IT Ops. These tools often talk of the three pillars of observability – metrics, logs and traces.
2. Distributed systems – the goal is to identify and understand their many potential points of failure. Many observability tools with this focus are targeting DevOps to provide a common platform and data model to accelerate the ability for both ops and developers to understand the state of systems
3. Project management – these tools focus on both a) the ability for anyone in an organization to quickly understand the status of any project workflow in an organization as well as the details about the workflow, and b) creating an easy to use UX to building these project workflows and integrate with other systems to automate processes such as email notifications or updating tickets in systems like ServiceNow.

Observability isn’t enough.

Beyond the confusion about the term, while observability is important, it is just one component for successful digital transformation projects. To succeed requires a deeper, more active understanding of systems that goes far beyond metrics, logs and traces, or the ability to understand data.

At TDS, we understand that as organizations push to accelerate their digital transformation, the complexity of IT environments continues to accelerate as well. And so does the need for observability AND insight to understand how systems work or don’t work together, the impact of any change that is made, and the ability to take action and define the path to restore service when things break and build resilience to mitigate the likelihood of future disruption.

Even beyond observability and insight, teams need to have data that is actionable – accurate, relevant, and current.

As experts who manage the most high-risk transformation projects around the world – data center and cloud migrations – we know that at every stage, teams need access to accurate and actionable data to make decisions — and there is no room for error. When working with customers, we must determine what to migrate, where and when. Our team must aggregate technical, business, compliance, regulatory, performance and other data from a variety of platforms, tools and files – none of which were designed to work together, and most of which are not in a consistent format.

We need to understand how each asset relates to other assets, and how each dependency on other assets or constraints based on business requirements impacts our planning. Many tool vendors talk about ‘advancing observability’ to turn data into answers.

This is exactly why we built TransitionManager. With key features such as a rules engine that enables teams to query data and do rapid what-if analysis to identify potential conflicts and blind spots, and the dependency analyzer which provides a visual map of assets along with 1-click access to all the business and technical data that would otherwise be hidden in silos.

Executing complex migrations of multiple servers to different cloud and on-prem targets at scale requires observability in real time, but also the ability to quickly take action. With its real-time task management, TransitionManager enables teams to easily identify potential bottlenecks and take action to remove the obstacle, and to automate processes

In summary, there are three key factors that are necessary for advanced observability, insight and taking decisive action in data center and cloud migrations – and any digital transformation project

1. Ability to capture and normalize business and technical data for every asset, and visually display the relationships for easy observability and decision making
2. Collaborative planning across IT and business units that includes an iterative process for testing multiple scenarios and on-the-fly change requirements
3. Orchestrated automation for executing the configuration, migration, and decommissioning process – all of which can be monitored in real-time to ensure accuracy and succes

Bottom line, observability is a frequently used term these days and the latest one that has piqued interest, but it is used many different ways and, most important, is too passive and only part of the solution. It’s only one element of what it takes to prepare, plan and orchestrate your complex IT transformation.

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