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In context
- Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
- Source: Databricks
- Published: 20/10/2026
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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
What gives data value is not having it, it is somebody changing a decision because of it. It sounds obvious and it is the test most projects fail: the report gets built, gets published, nobody changes anything, and six months later the project is recorded as a success because it shipped on time.
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
- Which concrete decision changes because of this and who makes it: without that, it is one more dashboard.
- Whether definitions are agreed across areas or each one brings its own to the meeting.
- Where the data comes from and what happens when the source system changes or fails.
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.
