The next evolution of observability starts with telemetry

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.

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.

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.