Data Quality Checks & Simple Analysis

In this lesson you will

Run quick data quality checks
Compare performance fairly

Analysis built on bad data produces confident, wrong answers. Before drawing conclusions, check that the underlying data is trustworthy.

Completeness: Are there missing values that would skew the results?
Consistency: Is the same thing recorded the same way every time (e.g. "Juba" vs "juba" vs "JUBA")?
Plausibility: Do the numbers make sense? A reported "500 sales in one day" for a small shop deserves a second look.
Simple analysis techniques: comparing periods (this month vs last month), comparing groups (region A vs region B), and looking at trends over time.

Practical Skill: Review a small sample dataset for completeness, consistency, and plausibility issues before analyzing it, listing every issue found.

TemplateData Quality Check Template

Worksheet

=== DATA QUALITY CHECK === Dataset: [e.g. "March Sales Records"] [ ] Any missing values in key columns? Where?[ ] Any inconsistent spelling/formatting of the same category?[ ] Any numbers that look implausibly high or low?[ ] Are dates in a consistent, correct format?[ ] Are duplicate entries present? === SIMPLE COMPARISON ANALYSIS ===  This month vs last month:  ______ vs ______  (% change: ____)  Region A vs Region B:      ______ vs ______  Trend over last 3 months:  [ ] Rising [ ] Falling [ ] Flat

Practice exercise

Count records per branch per week in a data set, note any gaps and compare the branches on a rate rather than a total.

Key takeaways

Check data before analysing it
Compare like with like
Segment results to see where change comes from

In the full program Module 3: Data Quality and Comparison Analysis See how this topic is taught in depth, with practical activities and an assessment.

Free self-study lesson — no certificate. For the full instructor-led program, see registration options.