Data Quality & Reconciliation

Category: Data & Analytics

Subcategory: Data Accuracy & Governance

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Purpose

To ensure all Cashkr dashboards and reports show accurate, consistent, and reliable data by performing routine data quality checks and reconciliation across platforms.

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Scope

Covers reconciliation between:

• GA4 vs Google Ads

• GA4 vs Admin DB (Postgres)

• Campaign Data vs Actual Orders

• Manual CSV vs Dashboard Data

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Responsibilities

Primary Owner:

• Data Analyst (Ashwini Giri)

Support:

• Marketing (Rhea, Rabina)

• Tech Team (Resham / Shahid / Suraj)

• Ops Team (Order-related checks)

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SECTION 1 — RECONCILIATION FREQUENCY

Weekly Checks

• GA4 vs Google Ads conversions

• GA4 vs Admin DB lead counts

• Event tracking issues

Monthly Checks

• Overall traffic alignment

• Conversion funnel accuracy

• Admin DB vs Dashboard order counts

• New event taxonomy audit

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SECTION 2 — DATA COMPARISON CHECKLIST

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A. GA4 vs Google Ads (Weekly)

Compare:

Metric	GA4	Google Ads
Clicks	Should match within ± 3–5%	Ads
Sessions	GA4	Not available
Conversions	GA4 Events	Ads Conversions
Cost / Conv	Calculated	Ads

What differences are acceptable?

• Click difference up to 5% (tracking protection, ad blockers).

• Conversion difference up to 10% (attribution model differences).

What differences require investigation?

• Differences > 10%

• Missing conversions entirely

• Sudden drop in GA4 events

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B. GA4 vs Admin DB (Weekly)

Compare:

Metric	GA4	Admin DB
Leads created	begin_checkout / generate_lead	Lead table count
Orders created	create_order event	Orders table
City selection	event parameters	DB fields

Acceptable margin:

• 0–3% difference due to time lag, filters.

Investigate if:

• Difference > 5%

• City/order counts mismatch

• Missing events

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SECTION 3 — HOW TO LOG DISCREPANCIES

Create a log sheet (Google Sheet) with:

Columns

• Date

• Source Comparison (GA4 vs Ads / GA4 vs DB etc.)

• Metric

• Expected Value

• Actual Value

• % Difference

• Severity (Low / Medium / High)

• Possible Reason

• Assigned To

• Status (Open / In Progress / Resolved)

• Final Fix Notes

Severity Levels

• Low: <5% difference (monitor only)

• Medium: 5–15% difference

• High: >15% difference or data missing

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SECTION 4 — ROOT CAUSE INVESTIGATION STEPS

After logging the discrepancy, follow this systematic flow:

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Step 1: Check Tracking Setup

• Open GA4 DebugView

• Check if the specific event is firing

• Validate parameters

• Verify GTM tags/triggers

If missing → GTM/GA4 issue.

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Step 2: Check Data Filters

• Check if GA4 filters are excluding traffic

• Check if dashboard filters exclude data

• Check date ranges

If mismatch → dashboard issue.

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Step 3: Check Attribution Differences

• Ads uses last click (platform-based)

• GA4 uses data-driven attribution

If mismatch within 10–20% → attribution issue.

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Step 4: Check DB Table Values

• Query DB:

  SELECT COUNT(*) FROM masterlead WHERE created_at BETWEEN X AND Y;

• Compare with GA4 event count

If mismatch → App/Web tracking vs DB mismatch.

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Step 5: Check for Tech Bugs

• Event not firing on certain OS/device

• API failure

• DB write issue

• App version not updated

Assign to Tech if needed.

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Step 6: Document Root Cause

Examples:

• “GA4 event missing for iOS due to Firebase bug”

• “GTM container not published correctly”

• “Admin DB had stale data due to cron delay”

• “Attribution difference between Ads and GA4”

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SECTION 5 — FIXING THE ISSUE

Based on the root cause:

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1. Tracking Fixes (GTM / GA4 / Firebase)

• Update GTM tag

• Fix event parameters

• Publish new version

• Test in DebugView

• Backfill data (if possible)

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2. Data Source Fix (Looker Studio)

• Reconnect broken sources

• Update expired credentials

• Refresh fields

• Rebuild chart if corrupted

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3. Database Fixes

• Inform Tech team

• Fix query

• Repair API

• Restart data cron jobs

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4. Ads Fixes

• Enable auto-tagging in Google Ads

• Restore disabled conversion action

• Re-import conversions from GA4

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SECTION 6 — DOCUMENTING THE RESOLUTION

Every resolved discrepancy must include:

✔ Root cause

✔ Action taken

✔ Date resolved

✔ Owner (Ashwini / Tech / Marketing)

✔ Prevention steps

Example entry:

Date	Issue	Source	Root Cause	Fix Done	Owner	Status
15 Dec	GA4 vs DB leads mismatch (20%)	GA4 vs DB	Missing parameters in generate_lead	Updated GTM tag, tested	Ashwini	Resolved

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SECTION 7 — MONTHLY AUDIT SUMMARY

At month-end, Data Analyst must prepare a Data Quality Report covering:

📌 Summary of discrepancies found

📌 Platforms affected

📌 Fixes completed

📌 Open issues

📌 Recommendations

This report goes to:

• CEO (Ibrahim)

• Marketing Head (Pranjal)

• Tech Lead (Resham / Shahid)