Objective

Today’s mission is to test your work.
You’ve cleaned, merged, and standardised - now it’s time to see how your data performs under real-world conditions.
By reviewing a small, representative sample of your cleaned datasets, you’ll confirm what’s working, where errors persist, and whether your systems are ready for automation or reporting.
This is your quality control checkpoint before scaling. ✔️

Why This Matters

Cleaning data isn’t the same as trusting data.
True confidence comes from validation - knowing that your new standards, formats, and ownership structures actually hold up in practice. 🔍
A structured review helps you spot lingering inconsistencies, systemic issues, or process gaps that might only reveal themselves once everything’s been combined and reused.
Testing on a small scale now prevents large-scale headaches later. 🧠

The Exercise

Step 1 - Select a Representative Sample (5 mins)

Pick one or two key datasets from earlier in the sprint, ideally those most central to your operations (e.g., client list, sales pipeline, project tracker).
Select a sample of 25–50 records to review. The goal isn’t volume - it’s accuracy and variety.

Step 2 - Check for Alignment (10–15 mins)

Cross-check your sample against the standards you set earlier in the week:

• 📏 Fields match formats - names, dates, currencies, and statuses are consistent.
• 🔁 Duplicates removed - no overlapping or repeated entries.
• 👤 Owners identified - each record has a clear owner or source.
• 🔍 Data accurate - spot-check entries against real-world sources (emails, invoices, CRM entries).
• ⚠️ No missing values - required fields are complete.

Mark any issues you find and categorise them:
Formatting / Missing / Duplicate / Ownership / Accuracy

Step 3 - Measure Improvement (10 mins)

Compare your current sample to your earlier findings from Day 2 (Assess Accuracy).

Ask:

• 📉 How many fewer errors did you find?
• ♻️ Which issues remain recurring?
• 📊 What percentage of your data can now be trusted for automation or analysis?

Record your quick metrics - for example:
“Error rate reduced from 18% → 4%.” 🎯

Step 4 - Gather Team Feedback (Optional)

Share your reviewed sample with key stakeholders or dataset owners.

“Does this data now reflect reality? Would you trust it for decision-making?”

Collect their input - qualitative insights are often more revealing than the numbers alone. 💬