AI-powered data pipeline observability: Stop monitoring and start preventing
Devops tools are making it increasingly easy to monitor data pipeline failures. But in many cases, those alerts are already too late. Take marketing campaigns, for example. Every mistake in data for marketing and sales has a trickle-down effect on revenue. William Flaiz, founder of CleanSmartLabs, shares one example.
Devops tools are making it increasingly easy to monitor data pipeline failures. But in many cases, those alerts are already too late. Take marketing campaigns, for example. Every mistake in data for marketing and sales has a trickle-down effect on revenue. William Flaiz, founder of CleanSmartLabs, shares one example.
There is a need to catch key data errors before they reach downstream, and AI makes it possible.
In this article, I’ll cover the gaps in the current data pipeline monitoring system and how using AI can catch failures before the damage is done.
Today’s data pipeline monitoring systems need an overhaul. There are three important ways that current systems fall short.
Data teams are drowning in alerts, often up to 20 alerts per day. There is no clear visibility of impact of each alert to better prioritize them. The ones that get escalated get immediate attention. And when that happens, everything else takes a back seat. So even if an alert exists, it’s sometimes waiting way too long in the queue. By the time the issue gets noticed, it has already caused harm that could be avoided.
Currently, alerts are set with static thresholds or conditions. Keeping these thresholds or conditions up to date requires regular input from business stakeholders and involvement of data teams. That’s an additional maintenance activity. On top of it, the threshold needs to be temporarily tweaked for some business use cases. For instance, a retail company can expect traffic spikes during the holiday season. This static nature of alerts can lead to false alarms that waste the support team’s time or to missed issues that impact the business.
In context
- Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
- Source: InfoWorld
- Published: 08/10/2026
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Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
In data, the problem is almost never the tool. It is that two areas mean different things by customer and neither wants to give ground. You can install the most modern platform on the market and still have three sales figures in the same meeting.
When I assess a data initiative, the first question is which concrete decision gets made with this and who makes it. If the answer is that it provides visibility, I already know the dashboard will be abandoned within a year. Visibility is not an objective, it is a side effect.
What usually goes wrong
Where it usually breaks is capture. A lot gets invested in processing and nothing at the point where a person fills in the form, in a hurry, with no validation and no idea what it is for. No model fixes data that was born wrong; it only propagates it with more authority.
What to watch
- When the number arrives relative to when the decision is taken, which is what defines its usefulness.
- Who answers for the quality of the indicator when somebody challenges it in a committee.
- Which concrete decision changes because of this and who makes it: without that, it is one more dashboard.
How I read this entry
What I would ask for is a business owner for each critical indicator, by name. Not an area: a person. Data without an owner degrades on its own, and when somebody asks why the number changed, there is nobody to ask.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
