# Data quality

> How far data can be trusted for the job it is used for.

- Level: Intermediate
- Topics: Data
- URL: https://tidbit.legios.com.ar/data-quality/
- In Spanish: [Calidad de datos](https://tidbit.legios.com.ar/es/data-quality/)

**Data quality** is the degree to which data is fit for its purpose: correct, complete and current enough that decisions built on it are sound. A customer table where a third of the emails are missing, the same person appears three times and half the addresses are from 2015 has poor quality, even if the database itself works perfectly.

It is usually broken into a few dimensions. **Completeness**: are required fields filled in? **Validity**: do values follow the rules, like a date that is a real date or an age that is not negative? **Uniqueness**: is each thing recorded once? **Consistency**: does the same fact match across systems? **Timeliness**: did today's data actually arrive today? **Accuracy**: does it match reality?

Teams protect quality with automated checks that run every time data moves through a pipeline: if yesterday's orders table suddenly has zero rows or a column full of nulls, the check fails and someone is alerted before a wrong number reaches a dashboard. Tools like dbt tests or Great Expectations do this. Fixing data downstream, through data cleaning, works, but fixing the form or system that produces the bad data is cheaper.

## An analogy

Like the ingredients in a kitchen. The best chef and recipe still make a bad dish with spoiled milk, so good restaurants check deliveries at the door instead of tasting the plate at the end.

## Keep in mind

- Means data is fit for its use: correct, complete and current enough.
- Common dimensions: completeness, validity, uniqueness, consistency, timeliness, accuracy.
- Automated checks in pipelines catch problems before they reach reports.
- Fixing the source beats cleaning the same errors again and again.

## Go deeper

- [Data quality on Wikipedia](https://en.wikipedia.org/wiki/Data_quality)

## Related terms

- [Database](https://tidbit.legios.com.ar/database/): An organized store of data that programs can search and update safely.
- [Dashboard](https://tidbit.legios.com.ar/dashboard/): One screen of charts and numbers that shows how something is doing at a glance.
- [dbt](https://tidbit.legios.com.ar/dbt/): A tool for writing data transformations as SQL files, with tests and docs, like software.
- [Data cleaning](https://tidbit.legios.com.ar/data-cleaning/): Fixing errors, gaps and duplicates in data before anyone trusts it.