Unstructured data are trying to tell you something – try to listen to it: the human side of the newspapers

It's Friday afternoon and your dashboards look great. The graphics are green. The use of the processor is stable. Database query times are in your SLA. You feel good and ready for the weekend.

But you do not know, there is an important problem neglected by all your measures – and it is about to ruin your weekend.

Unfortunately, you don't know the problem yet. This is because there is a disparity between your measures and the real user experience. Your dashboards could look Great, but your users tell another story.

It even happens to the best of us. April 17, 2025, for example, The Walmart website has experienced an important breakdown. Users were unable to add articles to their carts or access certain pages. Income has been lost, user complaints increased on X and Broken-downAnd undoubtedly someone was woken up after deep sleep to connect and help repair it.

Critical signals do not always appear in CPU graphics or 5xx error graphics. They surface in cat threads, downdter complaints and even the connection newspapers have failed. The first signals often come from people, no probes.

For what? Because traditional monitoring tools are focused on structure Data, such as the use of the processor, memory consumption, the use of the database and the network flow. And although these measures are essential, they can miss the nuances of user behavior and experience.

But not structured data– as error messages, user comments and newspapers – can tell a more in -depth story. It can provide critical information on the system problems that structured data can ignore and that you do not know it otherwise – until it is too late.

The signs are there – if you listen.

In this article, I will explore why the unstructured data is important, where you should look for unstructured data, what signals to monitor and how observability platforms can help you use unstructured data without drowning in noise.

Why unstructured data is important

Structured data The data that is in the formats you expect – the ways, the columns, the numbers, the statistics – and tells you everything about what has happened logically. It is the duration of an API call, the Code of the CPU or the CPU load on a node.

Not structured dataOn the other hand, is the messy data. And it's everywhere. It can be found in assistance tickets, bug reports, chat threads, error messages and disabled complaints. These are the data that often arrives in natural language, not digital values. It is disorderly, not always clear in the sense, and as its name suggests, it is not structured.

But the unstructured data is essential. He tells you where confusion lives, where intention is breaking down and where the user's mental model comes up against what the software does. He captures the emotions, intentions and frustrations that users feel when the systems are bad.

And when ingested, interpreted and aggregated, important models can emerge. For example, unstructured data can start painting an image if your application see:

• An increase in attempts to reset password
• Rage click after a user interface version
• A sudden drop in engagement
• Support tickets have gathered around a broken trip

Sometimes your best clues are not in metrics – they are in this unstructured data. Structured observability gives you the dashboard. Unstructured data give you history. And if you don't read both, you miss half the plot.

What signals should you watch?

So where should you look for unstructured data-and what should you watch? There are many sources. Here are a few to start:

• Be careful Session newspapers This shows users who repeatedly try the same action. It is not only to try again – it is friction.

• To watch Free form error messages Who are never killed in your dashboard. This is often where the real context is seen behind a failure.

• Do not ignore the Chat on Slack, Jira, or even on social networks. When three engineers complain about the same “slow page”, there is a good chance that there is a regression of performance in your smoothed latency graph.

• Even Vague user can be invaluable. A peak in the “Can't Connection” support tickets can be attributed to the handling of session expiration, rather than a failure of the infrastructure. You would only catch it if you collect and analyze the whole story: system newspapers, yes, but also what people say and do when something does not work as they expect.

• To watch Safety anomalies. Failure of connection attempts, a stuffing of identification information, token offsets – these may not trigger alarms if the thresholds are not raped, but the models buried in raw beads can report a threat from weeks before your SIEM lights up.

How observability platforms can demolish unstructured data

One of the biggest false ideas on unstructured data is that it must be cleaned, labeled and modeled before it is useful.

Yes, it was true in the past. The teams often spend hours writing regular expressions or building brittle analyzers just to extract a few fields from a disorderly newspaper line.

But this is no longer the case.

Modern observability platforms are designed to ingest large-scale unstructured data without requiring perfect formatting or predefined diagrams. You can pump gross error messages, user reviews, support tickets and slack threads and the platform manages the rest. Automatic learning, natural language treatment and model recognition do most of the work.

This means that you don't need a data wrangler to find value. A modern observability platform can:

• Surface connection peaks automatically in connection failures by IP address block or geographic location.
• Cluster a similar feedback in themes using Analysis of feelingsEven if the wording varies.
• Correct failed transactions to specific deployments, even when the newspapers do not follow the conventions of strict denomination.

To be effective, you need an observability platform that can ingest both structured and not structured data. Whoever offers a complete vision of system health. The one who, by analyzing unstructured data, helps you identify and solve problems proactively – before your weekend is ruined.

For example, here is a screenshot of Sumo logic Displaying a declaration of analysis of an unstructured journal data section and how it helps you give meaning to the data.

\ With a modern platform like this, you can use a “reading scheme” approach. You simply store the data as it is, then analyze it if necessary. And if you can get friendly priceYou will not have to worry about the amount of ingestion; You can ingest everything – system newspapers, application traces and behavioral data – and explore it later without shortcomings.

Example of use cases

For example, let's say that you are working in an electronic commerce company which has a sudden increase in negative comments on social networks and customer opinions, which all mention difficulties with the payment process. Traditional monitoring tools, focused on structured data such as transactions success rates, show no anomaly.

But using Analysis of feelings On unstructured data sources, you identify a model: customers are frustrated by a recent update of the payment interface. This idea means that you can quickly solve the problem, improve Customer satisfactionAnd stop any potential loss of income.

When observability platforms treat large -scale behavioral signals, the value is not only technical – it is operational and financial.

• Electronic commerce teams can identify and resolve the friction in the payment flow before singing the conversion rates.
• SaaS platforms can correlate an increase in the volume of support with a regression in a recent version and measures before increasing unsubscribe.
• SRE and platform teams can detect configuration errors or silent failures earlier, which means a duration of reduced incidents and a reduction in the costs of downtime.

This type of model recognition transforms what was previously general costs in a strategic overview.

Conclusion

You already follow the figures – latency, error rates, the use of the processor. But it is only half of the story. The other half lives in disorderly hustle and bustle: elbow, support tickets, newspaper messages and user reviews that do not enter a tidy scheme.

Unstructured data is where behavioral signals live. He can show you Why Things break, not just What is broken. He captures confusion, intention and frustration long before structured telemetry lifts a red flag.

If you are responsible for user experience, reliability or security, you cannot afford to ignore what people say – or how they interact – with your product. The tools exist to make data not structured useful. Now it's about putting them to work. The human side of the newspapers speaks. Start listening.