Beginner

90. NULL Data Quality Analysis

Learn how SQL can be used to identify and analyze missing data.

📘 Lesson 90 of 728 ⏱ 9 min 🎯 Beginner

NULL data-quality analysis involves identifying missing values, measuring their frequency, understanding where they occur, and determining whether they require correction, replacement, or further investigation.

Missing information can affect business decisions and analytical results. Data Analysts should identify important NULL values before using datasets for reporting or decision-making.

An analyst can count employees with missing email addresses and calculate the percentage of records affected to assess data completeness.

SQL 
SELECT
    COUNT(*) AS TotalEmployees,
    COUNT(Email) AS EmployeesWithEmail,
    COUNT(*) - COUNT(Email) AS MissingEmails
FROM Employees;

NULL analysis is used in banking, insurance, HR, sales, customer analytics, compliance reporting, financial analysis, and dashboard preparation.

  • NULL values are common in real-world databases.
  • Analysts should measure important missing values.
  • COUNT can help identify missing information.
  • COALESCE can replace NULL when appropriate.
  • IS NULL and IS NOT NULL help identify incomplete records.
  • NULL analysis is an important data-quality activity.
Why is NULL analysis important?

It helps identify missing information that may affect reporting, analysis, and business decisions.

How can SQL identify missing email addresses?

Use WHERE Email IS NULL or compare COUNT(*) with COUNT(Email) to measure missing email values.

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