Objective

Now that you’ve surfaced your data sources, it’s time to test their truth.
Today’s mission is to check whether your data actually reflects reality - because if it’s wrong, every report, forecast, or AI model built on it will be wrong too.
Accuracy turns information into intelligence.

Why This Matters

Even the cleanest-looking spreadsheet can quietly drift off course.
Typos, manual entries, outdated forms, and system sync errors can all distort what your business believes to be true.
By auditing small samples of data today, you’ll uncover the gaps and patterns that erode trust—and start building the evidence you’ll need to fix them later.

The Exercise

Step 1 - Choose 3–5 Key Data Sources (5 mins)

Pick your most critical datasets from yesterday’s inventory - the ones that influence key decisions (e.g., client database, sales CRM, project tracker).
You don’t need to review everything - just the data that matters most.

Step 2 - Spot-Check for Errors (10–15 mins)

Open each selected dataset and review a random sample of entries. Look for:

• 🔡 Misspellings or inconsistent formatting (names, dates, currencies)
• ⚠️ Missing or incomplete fields
• 🔁 Duplicate or conflicting records
• 🔍 Numbers that don’t match reality (totals, invoice amounts)
• 📅 Outdated information (old job titles, inactive clients, obsolete statuses)

Step 3 - Record the Findings (5 mins)

Create a simple accuracy log with:

• 🗂 Dataset name
• #️⃣ Number of records checked
• ❗ Number of issues found
• 🧩 Types of issues (typos, duplicates, outdated entries)
• 🎚 Severity (Low / Medium / High)

Step 4 - Flag for Correction (Optional)

Mark any critical errors that could impact reporting or customer experience.
Add a tag or comment so they’re easy to address during your Day 3 cleanup session.