Otto Dettmer/Ikon Images Imagine that, as an operations manager at a global retailer, you have invested heavily in data science. Your data science team has built sophisticated models that produce demand predictions for the company’s products with impressive accuracy.
Otto Dettmer/Ikon Images Imagine that, as an operations manager at a global retailer, you have invested heavily in data science. Your data science team has built sophisticated models that produce demand predictions for the company’s products with impressive accuracy.
In context
- Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
- Source: MIT Sloan Management Review
- Published: 19/10/2026
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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.
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
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
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.
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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.
