Marketing Data Audit Checklist - 50 Points

What This Resource Is

A comprehensive, actionable 50-point checklist designed to systematically audit your marketing data infrastructure from end to end. This checklist helps you identify data quality issues, tracking gaps, integration problems, and governance weaknesses that could be undermining your marketing decisions.

Think of this as a "health check" for your marketing data ecosystem—from initial tracking implementation through data storage, processing, and final reporting. Each checkpoint includes clear pass/fail criteria and common issues to watch for.

Who Should Use It

Perfect for:

Marketing Operations Managers - Ensure data infrastructure supports team needs
Analytics Managers - Validate tracking accuracy and data quality
Marketing Directors - Assess confidence in reporting and decision-making
Data Engineers - Identify technical issues in marketing data pipelines
Growth Teams - Verify attribution and conversion tracking accuracy
Consultants & Agencies - Audit client data setups systematically

Use this checklist if you:

Lack confidence in your marketing reports
See discrepancies between different analytics platforms
Are preparing for a major campaign or product launch
Recently changed analytics tools or marketing tech stack
Have never formally audited your tracking setup
Need to justify investment in data infrastructure improvements

How to Use It

Step 1: Prepare for Your Audit

Before you begin:

Schedule 4-6 hours for a thorough audit (or spread over several days)
Gather access credentials for all marketing platforms
Assemble relevant stakeholders (analytics, marketing, IT)
Create a copy of this checklist in your preferred format (spreadsheet recommended)

Tools you'll need:

Access to GA4, Google Tag Manager, and advertising platforms
Browser developer tools (Chrome DevTools)
Tag debugging extensions (Google Tag Assistant, Facebook Pixel Helper)
Access to your CRM and marketing automation platform
Database or data warehouse access (if applicable)

Step 2: Work Through Each Category

The checklist is organized into 8 categories:

Website Tracking Foundation (8 points)
Event & Conversion Tracking (8 points)
Advertising Platform Integration (6 points)
Data Collection & Quality (7 points)
Data Integration & Warehouse (6 points)
Reporting & Attribution (6 points)
Privacy & Compliance (5 points)
Governance & Documentation (4 points)

For each item:

Mark as PASS (working correctly), FAIL (broken/missing), or PARTIAL (works but has issues)
Note specific issues in the comments field
Assign priority (High/Medium/Low) to each failure
Identify who should fix it (Analytics team, IT, vendor, etc.)

Step 3: Score Your Data Health

Calculate your overall score:

Each PASS = 2 points (total possible: 100 points)
Each PARTIAL = 1 point
Each FAIL = 0 points

Scoring interpretation:

90-100 points: Excellent data infrastructure
75-89 points: Good foundation with minor gaps
60-74 points: Functional but significant improvement needed
Below 60 points: Critical issues requiring immediate attention

Step 4: Create Your Action Plan

Prioritize fixes:

Critical (fix within 1 week): Broken conversion tracking, major data loss, compliance violations
High (fix within 1 month): Attribution gaps, integration issues, significant data quality problems
Medium (fix within quarter): Minor tracking gaps, reporting inefficiencies, documentation needs
Low (ongoing): Nice-to-have enhancements, advanced features, optimization

Create fix timeline:

List all FAIL and PARTIAL items
Group by owner (who's responsible)
Estimate effort (hours) for each fix
Schedule implementation sprints
Plan verification testing

Step 5: Re-Audit Quarterly

Establish ongoing data quality:

Re-run this full checklist every quarter
Run weekly spot checks on critical tracking (conversions, revenue)
Set up automated monitoring where possible
Document changes to tracking setup
Update checklist as your stack evolves

The 50-Point Checklist

Category 1: Website Tracking Foundation (8 Points)

1.1 Google Analytics 4 Implementation

Check: GA4 tracking code is installed on all website pages

Verify using Google Tag Assistant or real-time reports
Check that Measurement ID is correct
Confirm tag fires on page load

Common issues:

Missing on certain subdomains or sections
Duplicate tracking codes
Incorrect Measurement ID
Blocked by ad blockers (check rate)

1.2 Google Tag Manager Setup

Check: GTM container is properly installed and configured

Container code in <head> and <body>
Container version is published (not just saved)
No conflicting direct tracking code

Common issues:

Container code only in <head> or <body> (need both)
Draft container not published
Multiple GTM containers causing conflicts

1.3 Page View Tracking

Check: All page views are accurately tracked

Real-time view shows page views immediately
Page paths are clean (no session IDs or parameters)
Virtual page views for SPAs are tracked

Common issues:

Single-page app page views not tracked
URL parameters causing duplicate pages
Missing page views on AJAX-loaded content

1.4 Cross-Domain Tracking

Check: Tracking persists across domains (if applicable)

User sessions maintain same Client ID across domains
Referral exclusions properly configured
Test by navigating between domains

Common issues:

Missing domains in configuration
Self-referrals inflating traffic sources
Sessions breaking at domain boundary

1.5 Site Search Tracking

Check: Internal site searches are captured

Search queries captured in GA4
Search results count tracked
Can report on search behavior

Common issues:

Search parameter not configured
Search tracking not enabled
Results count not captured

1.6 404 Error Tracking

Check: 404 pages and errors are tracked

Custom event fires on 404 pages
Can identify broken links
Previous page path captured

Common issues:

404s not tracked at all
Can't identify referrer to broken link
No differentiation between 404 and other errors

1.7 Enhanced Measurement

Check: GA4 enhanced measurement is properly configured

Scroll tracking enabled (if desired)
Outbound click tracking works
File download tracking captures downloads
Video engagement tracked (YouTube)

Common issues:

All enhanced measurement disabled by default
Outbound links not excluding internal domains
File downloads not capturing all file types

1.8 Mobile App Tracking (if applicable)

Check: Mobile app events send to GA4

Firebase SDK properly integrated
Screen views tracked
App events appear in GA4 reports

Common issues:

Separate property instead of unified web+app
App user ID not aligned with web
Events not following naming conventions

Category 2: Event & Conversion Tracking (8 Points)

2.1 Conversion Events Configuration

Check: All key conversion events are defined and tracking

Events marked as conversions in GA4
Conversion events fire reliably
Can differentiate between conversion types

Required conversions:

Form submissions (lead forms, contact forms)
Purchases/transactions (e-commerce)
Sign-ups (newsletter, trial, account creation)
Key user actions (download, video watch, etc.)

Common issues:

Conversions not marked in GA4 admin
Events firing multiple times per conversion
Missing conversion types

2.2 E-commerce Tracking (if applicable)

Check: Full e-commerce funnel is tracked

Product views tracked
Add to cart events fire
Begin checkout event captures starts
Purchase event with transaction details
Revenue and product data accurate

Common issues:

Revenue not matching actual (wrong currency, missing decimal)
Product data incomplete (missing SKU, category, price)
Funnel steps missing (can't see drop-off)
Refunds not tracked

2.3 Form Tracking

Check: All forms have conversion tracking

Form submission events fire
Form ID or name captured
Successful vs. failed submissions differentiated
All forms on site covered

Common issues:

AJAX forms not tracked
Multi-step forms only tracking final step
Thank-you page not loading tracking
Can't identify which form converted

2.4 Phone Call Tracking

Check: Phone calls from marketing are tracked

Call tracking numbers on website
Source attribution captured
Call duration and outcome tracked (if possible)
Integrated with CRM

Common issues:

No call tracking at all
Can't attribute calls to source
Manual call logging not happening
Different number on different pages (tracking conflict)

2.5 Lead Source Tracking

Check: Lead sources are accurately captured

UTM parameters properly used
Source/medium captured in CRM
Can trace lead back to campaign
First-touch and last-touch attribution available

Common issues:

UTM parameters not passed to CRM
Source data overwritten in CRM
Inconsistent UTM naming conventions
Direct traffic overattributed

2.6 User Identification

Check: Users are identified when they convert

User ID implemented for logged-in users
Email or ID passed to analytics (hashed if needed)
Can connect anonymous to identified sessions
Cross-device tracking enabled

Common issues:

No User ID implementation
User ID only set after conversion
PII (unhashed email) sent to analytics
Can't connect pre and post-login behavior

2.7 Event Parameter Quality

Check: Events include necessary parameters

Standard parameters used correctly (value, currency)
Custom parameters provide needed context
Parameters consistently named
No PII in parameters

Common issues:

Events missing value parameter
Inconsistent parameter naming (form_id vs formID)
Too many custom parameters (hit limits)
PII like email in parameters

2.8 Offline Conversion Tracking

Check: Offline conversions are imported to ad platforms

Store visit conversions tracked (if retail)
Phone call conversions uploaded
Sales CRM data imported
Match rate is acceptable (>50%)

Common issues:

No offline conversion import
Low match rates due to data quality
Long delay in upload (stale data)
Can't connect online ad to offline sale

Category 3: Advertising Platform Integration (6 Points)

3.1 Google Ads Conversion Tracking

Check: Google Ads conversions are accurately tracked

Conversion actions defined for each goal
Google Ads tag fires on conversion pages
Conversion values are accurate
Enhanced conversions enabled (if possible)

Common issues:

Using imported GA4 goals only (less accurate)
Conversion tag not firing
Wrong conversion values
Missing conversion category settings

3.2 Facebook Pixel Implementation

Check: Meta Pixel is installed and working

Base pixel code on all pages
Standard events fire (ViewContent, AddToCart, Purchase)
Event parameters include required fields
Pixel Helper shows no errors

Common issues:

Pixel fires multiple times per page
Purchase event missing value or currency
Content_ids not matching product catalog
iOS 14+ attribution gaps not addressed

3.3 LinkedIn Insight Tag

Check: LinkedIn tracking is properly configured

Insight Tag installed on all pages
Conversion tracking set up for leads
Can build matched audiences
Conversions attributed to campaigns

Common issues:

Tag only on homepage
No conversion events defined
Poor match rate for audience targeting
Can't see conversion paths

3.4 Ad Platform to Analytics Integration

Check: Advertising platforms are linked to GA4

Google Ads linked to GA4 property
Meta CAPI (Conversions API) implemented (if using FB ads)
Can see ad platform data in GA4
Cost data imports successfully

Common issues:

Platforms not linked at all
Wrong property or account linked
Cost data not importing
Attribution discrepancies not understood

3.5 UTM Parameter Consistency

Check: UTM parameters follow consistent naming conventions

utm_source values are standardized (e.g., "google", not "Google" and "google.com")
utm_medium follows standard taxonomy (cpc, social, email)
utm_campaign naming is logical and consistent
All paid campaigns have UTM tags

Common issues:

Inconsistent capitalization causing duplicates
Freeform UTM values (no standard list)
Missing UTM parameters on some campaigns
Auto-tagging disabled in Google Ads

3.6 Attribution Model Configuration

Check: Attribution model is appropriate and understood

Attribution model selected intentionally (not just default)
Model aligns with sales cycle (last-click for short, data-driven for long)
Stakeholders understand which model is used
Attribution reporting is accessible

Common issues:

Using default last-click without consideration
Different attribution in different tools (GA4 vs. Ads)
No understanding of multi-touch attribution
Can't see assisted conversions

Category 4: Data Collection & Quality (7 Points)

4.1 Data Sampling

Check: Reports are not based on sampled data

GA4 reports show "unsampled" or low sampling rate (<5%)
Exploration queries don't trigger sampling
Data volumes are within platform limits

Common issues:

High-traffic sites triggering sampling
Complex explorations always sampled
No awareness of sampling impact
Not using BigQuery for unsampled analysis

4.2 Bot and Spam Traffic Filtering

Check: Non-human traffic is filtered out

Bot filtering enabled in GA4
Referral spam domains excluded
Internal traffic excluded (office IPs)
Anomalous traffic spikes investigated

Common issues:

Bot filtering disabled
Known spam referrers (semalt, buttons-for-website) in reports
No internal IP exclusion
Ghost spam not filtered

4.3 Data Consistency Across Platforms

Check: Metrics align across tools within acceptable variance

GA4 vs. Google Ads conversions reconciled (<20% difference)
CRM leads vs. analytics submissions match
Revenue in GA4 vs. actual revenue aligned
Discrepancies have known explanations

Common issues:

Major discrepancies (>50%) without explanation
No process to reconcile data
Different definitions of "conversion" in each tool
No single source of truth

4.4 Data Freshness

Check: Data is available in timely manner

Real-time reports show current activity
Standard reports updated within 24-48 hours
No unexplained data delays
Data import automation runs on schedule

Common issues:

72+ hour data latency
Imports failing silently
Can't make real-time optimizations
Delayed data causes missed opportunities

4.5 Data Completeness

Check: No significant data loss or gaps

Historical data available for benchmarking
No unexplained drops in traffic or conversions
All sources feeding into analytics
Backup data exists

Common issues:

Historical data lost during migration
Tracking outages not noticed for days/weeks
Some traffic sources not captured
No data retention plan

4.6 Custom Dimension & Metric Implementation

Check: Custom dimensions and metrics are properly configured

Custom dimensions capture needed business data
Scope is correct (event, user, session)
Values populate correctly
Used in reporting

Common issues:

Wrong scope causing data issues
Custom dimensions not populating
Hit limits on custom definitions
Defined but never used

4.7 Data Validation Process

Check: Regular data quality checks are performed

Automated alerts for tracking failures
Weekly manual spot checks on key metrics
QA process for new tracking implementation
Data quality dashboard exists

Common issues:

No validation process
Tracking breaks and no one notices
New features deployed without tracking QA
Reactive (only catch issues when reported)

Category 5: Data Integration & Warehouse (6 Points)

5.1 CRM Integration

Check: Marketing data flows to CRM bidirectionally

Lead source data captured in CRM
Conversion events send to CRM
CRM data available for segmentation in ad platforms
Attribution connects marketing to closed deals

Common issues:

One-way integration only (CRM to marketing, not reverse)
Lead source not captured or overwritten
No closed-loop reporting (can't see which leads closed)
Manual data entry instead of automation

5.2 Marketing Automation Integration

Check: Email and automation platform data is connected

Email engagement syncs to analytics
Website behavior triggers automation workflows
Can attribute conversions to email campaigns
Unified customer view across platforms

Common issues:

Siloed email data (can't see in analytics)
No behavioral triggers (email separate from web)
Can't attribute email assists to conversions
Duplicate contacts across systems

5.3 Data Warehouse Implementation

Check: Centralized data warehouse aggregates marketing data

GA4 exports to BigQuery or warehouse
Ad platform data imports regularly
CRM and other sources integrated
Historical data retained

Common issues:

No warehouse (data trapped in silos)
Manual exports instead of automation
Data freshness issues (weekly instead of daily)
No data retention strategy

5.4 Data Transformation & Modeling

Check: Raw data is transformed into usable models

Marketing attribution model built
Customer journey data modeled
Data quality rules applied
Business logic documented

Common issues:

Using raw exports without transformation
No unified customer ID across sources
Inconsistent business logic across reports
Undocumented transformations (tribal knowledge)

5.5 API Connectivity

Check: Systems can communicate via APIs

APIs used for real-time data sync (not batch)
Authentication and credentials secure
Error handling and retries configured
API rate limits understood and managed

Common issues:

Batch file transfers instead of APIs
Hardcoded credentials in scripts
Failed API calls not monitored
Hit rate limits causing data loss

5.6 Data Export & Backup

Check: Critical marketing data is backed up

Regular exports of GA4 data (BigQuery or backup)
CRM data backed up
Campaign configurations documented
Can restore data if needed

Common issues:

No backups (relying on platform retention only)
No way to recover historical data
Campaign setups not documented
Data locked in platforms with no export

Category 6: Reporting & Attribution (6 Points)

6.1 Automated Reporting

Check: Key reports are automated and delivered regularly

Dashboard or report scheduled to stakeholders
Reports arrive on time
Data is accurate and up-to-date
Reporting cadence matches business needs

Common issues:

Manual report creation (time-consuming, error-prone)
Reports delivered late or inconsistently
Data issues in automated reports not caught
Over-reporting (too many unused reports)

6.2 Dashboard Accessibility

Check: Stakeholders can access data when needed

Self-service dashboards available
Appropriate permissions granted
Dashboards load quickly and reliably
Mobile access available

Common issues:

Only one person can access data
Dashboards too complex for non-analysts
Slow load times discourage use
No mobile option for executives

6.3 Multi-Touch Attribution

Check: Multi-touch attribution is measured and understood

Can see assisted conversions
Attribution model goes beyond last-click
Understand contribution of awareness channels
Top/middle/bottom funnel performance tracked

Common issues:

Last-click attribution only (ignores assists)
No visibility into full customer journey
Over-crediting or under-crediting channels
Can't justify upper-funnel spend

6.4 Custom Report Availability

Check: Custom analysis can be performed when needed

Analysts have explore/query access
Ad-hoc questions can be answered quickly
SQL access to data warehouse (if applicable)
Historical data available for analysis

Common issues:

Locked into pre-built reports only
Long turnaround for custom requests
No historical data for trending
Can't answer "why" questions, only "what"

6.5 Revenue Attribution

Check: Revenue is accurately attributed to marketing channels

E-commerce revenue tracked to source
Lead-to-revenue connection exists (B2B)
Can calculate ROI by channel
Attribution window appropriate for sales cycle

Common issues:

No revenue tracking at all
Can't connect marketing lead to closed deal
Attribution window too short (missing conversions)
Offline revenue not included

6.6 Benchmarking & Targets

Check: Performance measured against benchmarks and goals

Historical benchmarks established
Industry benchmarks used for context
Targets set for key metrics
Performance vs. target is reported

Common issues:

No targets or goals set
Don't know if performance is good or bad
No historical comparison
Industry context missing

Category 7: Privacy & Compliance (5 Points)

7.1 Cookie Consent Management

Check: Cookie consent is properly implemented

Consent banner displays before tracking
User choice is respected (tracking blocked if declined)
Consent persists across sessions
Compliant with GDPR/CCPA as applicable

Common issues:

Tracking loads before consent
"Accept" is the only option (not truly consensual)
Consent not stored (re-prompts every visit)
Not compliant with local regulations

7.2 Privacy Policy

Check: Privacy policy discloses data collection practices

Privacy policy exists and is accessible
Policy describes analytics tracking
Third-party data sharing disclosed
Policy updated recently

Common issues:

No privacy policy
Generic template not customized
Doesn't mention all tracking tools
Outdated (doesn't reflect current practices)

7.3 PII Protection

Check: Personally Identifiable Information is not improperly collected

No unhashed emails sent to analytics
No names, addresses, phone numbers in tracking
Form field data not captured in URLs
Credit card data never in analytics

Common issues:

Email addresses in GA4 parameters
PII in page URLs (form data, user info)
IP anonymization not enabled (if required)
No PII policy for data team

7.4 Data Retention Settings

Check: Data retention is configured appropriately

GA4 retention set intentionally (not default 2 months)
Balances business needs with privacy
Understand impact on reporting
Documented reasoning for retention period

Common issues:

Default 2-month retention (too short for most businesses)
Never reviewed retention settings
Historical data unexpectedly deleted
No policy on data deletion requests

7.5 Security & Access Control

Check: Analytics access is properly secured

Role-based access control implemented
Former employees removed promptly
Admin access limited to necessary users
Two-factor authentication enabled

Common issues:

Everyone has admin access
Shared login credentials
Former employees still have access
No access audit trail

Category 8: Governance & Documentation (4 Points)

8.1 Tracking Documentation

Check: Tracking implementation is documented

Event catalog exists (all events and parameters)
Tagging plan documents implementation
Changes to tracking are logged
Documentation is current

Common issues:

No documentation (tribal knowledge only)
Outdated docs don't reflect current state
Can't tell what events mean or how they're triggered
No change log

8.2 Naming Conventions

Check: Consistent naming standards are followed

Event names follow pattern (verb_noun or clear pattern)
Parameter names are standardized
UTM conventions documented
Campaign naming structure exists

Common issues:

Inconsistent event naming (camelCase, snake_case, mixed)
Freeform names make reporting difficult
Different teams use different conventions
No documented standards

8.3 Change Management Process

Check: Changes to tracking follow a process

Testing environment for tracking changes
QA checklist for new implementations
Approval required before deploying tracking
Rollback plan if issues arise

Common issues:

Changes deployed directly to production
No testing before launch
Broken tracking discovered after the fact
No way to roll back changes

8.4 Data Governance Ownership

Check: Clear ownership and accountability for data quality

Data owner assigned (person responsible)
Regular data quality review meetings
Escalation process for data issues
Budget allocated for data quality initiatives

Common issues:

No one owns data quality
Data issues never get fixed
No regular review process
Data quality is "someone else's problem"

Scoring Your Results

Calculate Your Score

Each PASS: 2 points
Each PARTIAL: 1 point
Each FAIL: 0 points
Maximum possible: 100 points

Score Interpretation

90-100 points - Excellent Data Foundation Your marketing data infrastructure is robust and reliable. Focus on:

Maintaining documentation as systems evolve
Continuous optimization and advanced analytics
Sharing best practices with other teams

75-89 points - Strong Foundation with Room for Improvement You have good data quality but some gaps. Priorities:

Fix high-priority failures first
Address partial implementations
Document current state before it becomes tribal knowledge

60-74 points - Functional but Needs Attention Your data works for basic reporting but has significant issues. Action plan:

Create 90-day improvement roadmap
Fix critical conversion tracking gaps
Implement data quality monitoring

45-59 points - Major Issues Impacting Decisions Data quality issues are likely affecting business decisions. Immediate actions:

Fix broken conversion tracking (critical)
Establish single source of truth
Get executive buy-in for data quality initiative

Below 45 points - Critical Infrastructure Problems Your data foundation needs significant work. Start here:

Audit what decisions are being made with this data (stop if critical)
Prioritize foundational tracking (GA4, conversions, revenue)
Consider bringing in external expertise
Create business case for investment in data infrastructure

Red Flags Requiring Immediate Attention

Stop everything and fix these:

Broken conversion tracking (can't measure ROI)
Revenue tracking errors (making wrong decisions)
PII violations (legal/compliance risk)
No data backups (at risk of total data loss)
Complete lack of data validation (flying blind)

Common Issue Patterns

Pattern 1: "We Track Everything but Understand Nothing"

Symptoms:

Strong scores in Categories 1-2 (tracking implementation)
Weak scores in Categories 4, 6 (data quality, reporting)
Data exists but isn't actionable

Fix:

Focus on data quality and reporting layers
Reduce tracking to what actually matters
Build dashboards for decision-making

Pattern 2: "Siloed Tools, Fragmented Data"

Symptoms:

Weak scores in Category 5 (integration)
Strong individual platform implementation
Can't answer cross-channel questions

Fix:

Prioritize CRM integration
Implement data warehouse
Build unified reporting

Pattern 3: "Shadow IT Analytics"

Symptoms:

Weak scores in Category 8 (governance)
Inconsistent naming, no documentation
Different teams using different tools

Fix:

Establish data governance
Create and enforce naming conventions
Centralize analytics ownership

Pattern 4: "Compliance Blindspot"

Symptoms:

Weak scores in Category 7 (privacy)
Strong technical implementation otherwise
Not thinking about legal/privacy implications

Fix:

Implement consent management immediately
Audit for PII in analytics
Consult legal team on compliance

Frequently Asked Questions

How long does a complete audit take?

Timing by organization size:

Small business (1-2 websites, <10 campaigns): 3-4 hours
Mid-market (multiple sites, 10-50 campaigns): 6-8 hours
Enterprise (complex infrastructure, 50+ campaigns): 12-20 hours over multiple days

Pro tip: Block dedicated time rather than trying to audit between other tasks. Switching contexts will slow you down.

Can I skip sections that don't apply to us?

Yes, but adjust scoring accordingly.

Examples:

No mobile app? Skip 1.8
No e-commerce? Skip 2.2
Don't use LinkedIn ads? Skip 3.3
No data warehouse? Skip relevant parts of Category 5

Recalculate your max score: If you skip 5 items, your max is 90 points (not 100), so a 75 would be 83% (good), not 75% (needs work).

How often should I run this audit?

Recommended cadence:

Full 50-point audit: Quarterly
Spot checks: Weekly (conversion tracking, key metrics)
Mini-audit: Monthly (Categories 1-3 only)
Ad-hoc: After major site changes, platform migrations, or new campaign launches

Set calendar reminders and assign ownership so audits actually happen.

What if I score poorly? Where do I start?

Priority order for fixes:

Critical (Week 1): Conversion tracking, revenue attribution
High (Month 1): Data quality issues, major integration gaps
Medium (Quarter 1): Documentation, governance, advanced tracking
Low (Ongoing): Nice-to-have features, optimizations

Don't try to fix everything at once. Pick top 5 issues and focus there.

Should I hire someone to help with this audit?

Consider external help if:

You score below 50 and don't know where to start
You lack technical expertise in analytics
You need executive buy-in (external audit adds credibility)
Your team doesn't have bandwidth for this

You can probably DIY if:

You have in-house analytics expertise
You score above 60 (have a foundation to build on)
Issues are specific and fixable with your current knowledge
You have time allocated for this project

How do I get buy-in to fix data quality issues?

Build a business case:

Quantify the cost of bad data
- "We spent $50K on paid ads but can't measure ROI accurately"
- "Sales team wastes 10 hours/week on bad leads we can't trace"
- "We're making decisions on sampled data (only 20% of actual traffic)"
Show risk
- "We're not GDPR compliant - potential €20M fine"
- "Former employees still have admin access to all our data"
- "We have no backup - one platform outage and we lose all historical data"
Demonstrate opportunity cost
- "With better attribution, we can shift $100K/year to higher-ROI channels"
- "Fixing tracking would reduce wasted ad spend by estimated 15%"
- "Better data quality would reduce reporting time from 10 hours to 2 hours/week"
Propose clear ROI
- Investment: "2 weeks of dev time + $X/month for tools"
- Return: "Save Y hours/week, improve ad efficiency by Z%, reduce risk"

What tools do I need to complete this audit?

Minimum required:

Access to Google Analytics 4
Access to Google Tag Manager
Access to advertising platforms (Google Ads, Meta, etc.)
Browser with developer tools

Highly recommended:

Google Tag Assistant Chrome extension
Facebook Pixel Helper Chrome extension
GA Debugger Chrome extension
Spreadsheet for tracking results

Nice to have:

ObservePoint or similar automated tag auditing
SQL access to data warehouse
CRM admin access

Most of this audit can be done with free tools and platform access.

Can I automate any of this?

Yes, several areas can be automated:

Automated monitoring tools:

ObservePoint: Automated tag auditing and monitoring
Funnel.io: Data quality monitoring across platforms
Supermetrics: Automated data quality checks
Custom scripts: GA4 API checks for data anomalies

What to automate:

Tag presence and firing (1.1-1.7)
Data freshness monitoring (4.4)
Conversion tracking health checks (2.1)
Data discrepancy alerts (4.3)

What requires manual review:

Attribution model appropriateness (3.6)
Compliance and privacy (Category 7)
Governance and documentation (Category 8)
Strategic alignment with business goals

Start with manual audit, then automate ongoing monitoring.

What's the difference between this and a Google Analytics audit?

This checklist is broader:

GA-only audit: Focuses on GA4 configuration and setup
This audit: Covers entire marketing data ecosystem (GA4 + advertising + CRM + warehouse + governance)

Use this checklist if:

You use multiple marketing platforms
You need to connect data across tools
You're concerned about data governance and compliance
You want to audit your full data infrastructure

Use a GA-specific audit if:

You only care about GA4 implementation
You don't use other marketing platforms extensively
You need deep GA4 configuration details

This checklist includes GA4 but goes much further.

Next Steps After Your Audit

Immediate Actions (This Week)

Fix critical failures
- Broken conversion tracking
- Compliance violations
- Major data loss issues
Document findings
- Share audit results with stakeholders
- Create prioritized fix list
- Assign owners to each item
Set up monitoring
- Create alerts for conversion tracking
- Schedule weekly data quality spot checks
- Set calendar reminders for next audit

Short-Term (This Month)

Knock out high-priority fixes
- Integration gaps
- Major tracking issues
- Data quality problems
Establish baseline metrics
- Document current data quality score
- Set targets for next audit
- Define success criteria
Build the fix roadmap
- Sprint plan for next quarter
- Resource allocation
- Budget requirements if needed

Long-Term (This Quarter)

Address medium-priority items
- Documentation
- Advanced tracking
- Optimization opportunities
Implement governance
- Create data quality review process
- Assign permanent data owner
- Build change management process
Build data quality culture
- Train team on best practices
- Create data quality KPIs
- Make data quality part of launch checklist

Download Your Checklist

What You'll Get

Download this comprehensive audit checklist package:

Excel/Google Sheets template with all 50 checkpoints
Scoring calculator with automatic health score
Fix prioritization matrix to plan your remediation
Sample completed audit to see best practices
Video walkthrough of how to conduct each check (45 min)

Bonus Resources Included

Data quality monitoring dashboard template
Common issues troubleshooting guide
Executive summary template for presenting findings
ROI calculator for data quality improvement business case

Ready to Audit Your Marketing Data?

Stop making decisions based on questionable data. Download the Marketing Data Audit Checklist and get clarity on your data quality in one afternoon.

[Download Free Checklist]

What happens next:

Instant download of audit template
Email series: "How to fix common data quality issues" (5 emails)
Invitation to monthly "Data Quality Office Hours"
Access to our marketing analytics community

Need help conducting your audit? Book a free 30-minute consultation with our analytics team, or explore our data quality guides for more resources.