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The business problem
Duplicates, missing values and orphaned relationships are discovered too late in reporting.
What you will be able to do
Write a suite of checks and produce an actionable anomaly report.
Measure completeness and uniqueness
Detect orphaned records
Document a rule
Prioritise a correction
Scope of this micro-course
This micro-course targets one focused skill. It does not replace a complete path or server administration training.
Prerequisites
Filters, aggregations and joins
Project and practice
9 verified exercises are available. A business project is included.
Focused path
Your programme, in order.
Content remains centralised: completing an activity here also updates the path that contains it.
Micro-lessons
01
Data quality is measured by rule
Quality data is not just a table without NULL. It must respect the rules necessary for its use: unique identifiers, mandatory fields, authorized values and consistency between sources.
In a professional environment, this technique is used in particular to secure an import, validate an indicator before publication or monitor consistency between two systems. However, a query is not reliable only because it executes: you must link each clause to a business rule, specify what a row represents and verify that the period, exclusions and missing values are processed voluntarily.
A status column contains `paid`, `PAID`, a trailing space, and an unknown value. An impossible date or a poorly formed email can be silently ignored by overly optimistic analysis.
Keep the raw value, create a separate normalized value, and classify each anomaly by rule. A quality report should measure coverage, uniqueness, validity and consistency across columns.