General-purpose AI agents are good at searching the web, reasoning, and writing code....
General-purpose AI agents are good at searching the web, reasoning, and writing code....
In context
- Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
- Source: Databricks
- Published: 05/10/2026
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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.
I look at lineage before I look at the chart: where each number comes from, who can change it, and what happens if the source system goes down. If nobody can walk that chain in two minutes, the dashboard is displaying confidence it did not earn.
What usually goes wrong
The frequent mistake is starting with the tool instead of the question. The platform gets bought, everything gets connected, and only then does somebody sit down to think about what they wanted to know. It almost always turns out the important question needed data nobody was capturing.
What to watch
- Where the data comes from and what happens when the source system changes or fails.
- 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.
How I read this entry
I would use it to review the full cycle, from where the data is captured to where somebody acts. The problem is almost never in the analytics but at the start: someone filling a field by hand, with no validation, in a hurry, not knowing what it is used for later.
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.
Comments
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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.
