Score four quality dimensions on the raw customer file

short · 35 min · Objective 5.4

Task

Turn four data quality dimensions into numbers for the raw customer file, each defined precisely enough that anyone would calculate the same score.

Steps

  1. Completeness: rows with a non-empty email, divided by all rows. Validity: rows whose signup_date is YYYY-MM-DD, divided by all rows.
  2. Consistency: rows whose region is exactly North, South, East or West, divided by all rows. Uniqueness: distinct lower-cased emails divided by non-empty emails.
  3. Record them in lab/quality/scores.txt as completeness: N, validity: N, consistency: N and uniqueness: N, percentages to one decimal place.

Verify

These checks run in a POSIX shell with Python 3: Terminal on macOS or Linux, and on Windows Git Bash (it comes with Git for Windows) or WSL. On Windows, python3 is often a Microsoft Store placeholder that prints “Python was not found”; run alias python3=py in Git Bash first.

grep -E '^(completeness|validity|consistency|uniqueness): ' lab/quality/scores.txt

Completeness 93.6, validity 90.4, consistency 87.3 and uniqueness 95.2. Each is one planted defect measured: 20 blank emails, 30 day-first dates, 40 region variants and 14 duplicated customers. A score is useful only with its definition; "90% valid" means nothing until it says valid how.

This is an independent study companion for CompTIA Data+ DA0-002 and is not produced by or endorsed by CompTIA.