# Data quality checks — Data Warehousing & Modelling

Source: https://www.geekswithgeeks.com/en/data-warehousing/data-warehousing-11

> Apply Data quality checks with a clear goal and verifiable outcome.

## Understand Data quality checks

For **Data quality checks**, begin with the problem it solves. Identify the input, the constraint, and the observable result before selecting a tool or workflow. This makes your choice explainable and easier to review.

## See the working path

Use this path to practise **Data quality checks**: understand the context, make one focused change, verify the result, then record the lesson.

![A four-stage practice workflow for Data quality checks.](assets/figures/data-warehousing/section-3-map.svg) — Figure 3.1 — Understand, act, verify and improve.

## A useful comparison

**Data quality checks** is like a checklist before a journey: it cannot travel for you, but it prevents a small missed detail from becoming a costly surprise.

Check the reasoning behind the action.

**Quiz:** What is the strongest first step when using Data quality checks?

- [x] Define the problem, constraint and evidence for success.
- [ ] Copy the largest example without reading it.
- [ ] Skip verification to save time.

*Answer:* Define the problem, constraint and evidence for success.. Clear constraints and a verifiable result make a workflow reliable.