Data Quality Patterns — Identifying the Missing Values Problem Each option shows example data from a table. Which option illustrates the missing values problem (i.e., required data absent for some rows)?

Introduction:
Missing values undermine analytics and operations by obscuring required attributes. Recognizing the pattern in raw data is the first step toward remediation. This question asks you to pick the example that clearly demonstrates missing data in a column where a value is expected.

Given Data / Assumptions:

• NULL stands for the absence of a value.
• Required columns should hold meaningful entries for all rows.
• We are diagnosing from small examples.

Concept / Approach:
Scan for placeholders of absence such as NULL or empty strings in contexts where a value should exist. The presence of a NULL amidst legitimate values in the same column is a textbook illustration of the missing values problem.

Step-by-Step Solution:
1) Inspect (a): multiple values across columns → multivalue, multicolumn.2) Inspect (b): token order inconsistency → inconsistent values.3) Inspect (c): the middle row has NULL where a color is expected → missing values.4) Inspect (d): free text → general-purpose remarks issue.5) Therefore, choose (c).

Verification / Alternative check:
COUNT(*) vs COUNT(column) or filtering WHERE column IS NULL quantifies the extent of missing data across the table.

Why Other Options Are Wrong:

• (a): Multiple attributes across columns, not missing data.
• (b): Token order differences, not absence.
• (d): Long remarks; different issue entirely.
• (e): Inconsistent values, not missing ones.

Common Pitfalls:
Confusing empty string ' with NULL. Establish clear rules and constraints to avoid silent missing values.

Final Answer:
Three rows have the values Brown, NULL, and Blue in the same column.