Relational vs Non-Relational Database: Choosing the Right Data Store

Choosing between relational and non-relational databases is one of the most consequential...

Choosing between relational and non-relational databases is one of the most consequential...

In context

• Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
• Source: Databricks
• Published: 24/08/2026

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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.

My criterion is that data only helps if it arrives in time for the decision it is meant to support. A perfect report that lands on the eighth for a decision taken on the third is a useless report, however correct it is. Timeliness beats precision more often than people admit.

What usually goes wrong

What I see fail most is dashboard maintenance. It gets built carefully, used for three months, and then a business rule changes that nobody told the data team about. The number still comes out, it no longer means the same thing, and decisions keep being made on data that became false.

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

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.