For technology teams, connecting telemetry has traditionally been seen as a route to greater visibility. The assumption is straightforward: the more information available about what is happening across an environment, the easier it should be to understand problems and respond to them.
But as modern technology environments have grown in scale and complexity, that equation no longer looks quite so simple and teams have found themselves dealing with fragmented telemetry and unclear signals. It now takes longer to understand what’s gone wrong, and even longer to fix it. And to add to the challenge further, the cost of storing and processing all this telemetry and data continues to be on the rise.
Vice President of Sales for Log Management at Dynatrace.
This is prompting organizations to reconsider where the problem should be addressed. Rather than collecting everything and attempting to make sense of it later, attention is shifting towards improving telemetry earlier in its lifecycle. Filtering, enriching and directing it from the outset can make the information reaching teams more relevant and ultimately easier to act on.
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More data doesn’t always mean better insight
The volume of telemetry available isn’t the fundamental issue. Its quality is. As AI-driven capabilities become deeply embedded in software delivery and operations, the quality of observability telemetry becomes even more important. Collecting telemetry without filtering out what isn’t needed can leave employees spending more time managing information than extracting value from it.
Fragmentation makes this even more challenging. With data sitting across several tools and agents, each of which generates varied forms of signal and alert. When an outage occurs, teams may then need to piece together inconsistent information from across those tools before they can understand the root-cause of the problem. Consuming valuable time while the impact on the business continues to grow.
There is also a significant financial cost attached to this approach. Processing and storing telemetry and data at scale requires considerable resources, particularly when organizations are retaining information that contributes more noise than insight. In the traditional sense, this isn’t a technology problem. It’s a telemetry and data quality concern, and the longer it’s ignored, the worse it usually gets.
Rethinking where telemetry is processed
One way organizations are responding is by moving intelligence further upstream in the technology stack. In practice, that means making decisions about telemetry earlier – filtering out what isn’t useful, enriching important information and routing meaningful telemetry before it reaches downstream systems.
Teams are beginning to collect, process and route relevant observability telemetry in real-time before it is ingested into downstream systems. As a result, there is a less volume to handle, reduced expenses and cleaner telemetry to deal with since only the relevant signals are sent downstream.
Open standards such as OpenTelemtry are making it increasingly possible to take this approach across complex environments. They allow teams to gather telemetry consistently without being dependent on a particular vendor or set of tools. That flexibility is particularly valuable when working with intricate, distributed systems that span several clouds, security and storage platforms.
Furthermore, as environments continue to evolve, it makes adapting to them that much easier.
AI needs reliable information to make reliable decisions
The implications become particularly important as organizations introduce more AI-powered capabilities into their operations. AI systems depend on accurate, contextualized telemetry and data to produce insights and automate decisions.
Teams are not only slowed down by incomplete or cluttered telemetry, but it’s this simple: poor quality telemetry leads to poor quality insights. When those insights are applied to large-scale automated decision-making, the margin for error drastically lowers.
The same principle applies to trust. Teams responsible for automated systems need to understand what has happened and explain why decisions about performance or security have been made. Doing so becomes much more difficult when those decisions are based on inconsistent information.
And this requirement for confidence matters far beyond just the technical teams. As senior management and board members are being increasingly asked to support AI-driven decision making, they need to trust the information those decisions are built upon. Credible, consistent telemetry and data can make that conversation much simpler.
The operational advantages are also evident. Teams can work quicker if they aren’t continuously battling erratic telemetry and data. They spend less time on manual research, detect issues early, and automate with greater confidence.
A strong telemetry and data foundation and a unified, context-driven platform not only enhance daily operations but also increases the successes of the AI-powered capabilities the organization invests in over time.
Quality has to be built in from the beginning
Ultimately, faster innovation and better customer outcomes depend on organizations having useful telemetry and signals available when they are needed. The good news is that it doesn’t require starting from scratch, it’s about making choices early in the telemetry lifecycle.
By introducing intelligence at that stage, teams can improve the quality of what ultimately reaches their observability platforms and ensure it can scale as their environments grow. The advantage will not come from simply collecting more. It will come from ensuring the telemetry an organization does collect is relevant, trustworthy and ready to support action.
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