Data Hygiene & Governance: Why Your Numbers Are a Mess (And How to Fix It)

Your marketing team has a spreadsheet. They call a campaign “Q4_PROMO_2024.”

Your sales team has a CRM. They call the same campaign “Q4 Promotion.”

Your finance team has an accounting system. They call it “Q4_PROMO.”

When it comes time to reconcile revenue, nobody can match them up. The spreadsheet doesn’t talk to the CRM. The CRM doesn’t talk to accounting.

You spend three hours manually mapping them together. Your analytics automation breaks because the naming changed slightly.

This is a Data Hygiene problem.

And it’s costing you thousands of dollars in wasted time and broken dashboards.

What Is Data Governance?

Data Governance is a set of rules about how data gets created, stored, labeled, and used.

It’s boring. It’s not sexy. It’s the opposite of “growth hacking.”

But it’s the difference between analytics that work and analytics that constantly break.

Here’s what good governance looks like:

• Naming conventions: Everyone calls a “campaign” the same thing
• Data quality standards: We check that data is accurate before it enters the system
• Documentation: When something changes, we document it
• Access controls: Not everyone can edit everything (preventing accidental deletions)
• Archiving: Old data gets moved somewhere safe, not deleted

The Naming Convention Problem

Let me show you why this matters.

You have a campaign promoting a webinar. Different teams call it:

• Marketing: “Webinar_Aug2024_Growth”
• Sales: “Growth Webinar August”
• Analytics: “growth-webinar-august”
• Finance: “GWA-082024”
• Your creative team: “The Growth Thing”

When you try to report on “webinar performance,” you have to manually match these five different names.

Now imagine you have 100 campaigns. You’re spending hours mapping names.

The Fix: Establish a naming convention that everyone uses.

Example standard:

[Channel]_[Product]_[Month]_[Year]_[Type]

So the webinar becomes: EMAIL_GROWTH_AUG_2024_PROMO

Everyone uses this format. No exceptions. Your automation can now match it across systems automatically.

Building Your Naming Convention

We work with you to build a system that makes sense for your business.

Here’s how we approach it:

Step 1: Identify Your Key Dimensions

What characteristics define your campaigns?

• Channel: Email, Facebook, Google, LinkedIn, Organic, Direct
• Product: Growth, Pro, Enterprise, Bundle
• Vertical: SMB, Mid-Market, Enterprise
• Type: Promo, Educational, Nurture, Re-engagement
• Month/Quarter: JAN, FEB, Q1, etc.

Step 2: Establish the Order

The sequence matters for searchability and readability.

We typically recommend: [Channel]_[Product]_[Type]_[Month]_[Year]

This way, if you search for “EMAIL_” you get all email campaigns. If you search for “EMAIL_GROWTH_” you get all growth email campaigns. And so on.

Step 3: Create a Reference Doc

We build a “Campaign Naming Convention” document that lives in your shared drive.

CAMPAIGN NAMING CONVENTION

Format: [CHANNEL]_[PRODUCT]_[TYPE]_[MONTH]_[YEAR]

CHANNEL codes:
- EMAIL: Email campaigns
- FB: Facebook paid ads
- GA: Google Ads (search)
- GD: Google Display
- LI: LinkedIn
- TW: Twitter/X
- ORG: Organic (SEO)

PRODUCT codes:
- GROWTH: Growth product line
- PRO: Pro tier
- ENT: Enterprise tier
- BUNDLE: Bundled offers

TYPE codes:
- PROMO: Promotional offer
- EDU: Educational content
- NUR: Nurture sequence
- REENG: Re-engagement
- LAUNCH: Product launch

MONTH codes:
JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC

YEAR codes:
24, 25, 26

EXAMPLES:
- EMAIL_GROWTH_PROMO_AUG_24 (Email campaign promoting Growth product in August 2024)
- FB_ENT_LAUNCH_SEP_24 (Facebook campaign for Enterprise product launch in September)
- GA_PRO_REENG_DEC_24 (Google Ads campaign re-engaging Pro tier customers in December)

Step 4: Enforce It in Your Tools

We integrate this naming convention into:

• Ad platform automation (auto-tags campaigns based on naming standard)
• CRM system (default campaign names in the naming standard)
• Spreadsheets (data validation rules to prevent non-standard names)

If someone tries to create a campaign called “The Big Sale Thing,” the system rejects it and asks them to use the standard format.

Data Quality Checks

Beyond naming, we set up automated checks that flag bad data.

Type 1: Completeness Checks

Is the required data present?

• Campaign name: Required (can’t be blank)
• Start date: Required
• Budget: Required
• Expected audience: Required

If any of these are missing, the data doesn’t enter the system. Someone has to fill it in.

Type 2: Validity Checks

Does the data make sense?

• Start date is before end date? (Campaign can’t start after it ends)
• Budget is a positive number? (Can’t have -$5,000 budget)
• Campaign name follows naming convention? (Must match the standard)

Invalid data gets flagged. It doesn’t automatically break things; it just creates a “review queue” for your team.

Type 3: Duplicate Checks

Are we entering the same campaign twice?

• Campaign name: “EMAIL_GROWTH_PROMO_AUG_24”
• Two versions in the system

We flag duplicates and ask: “Are these the same campaign or different campaigns?”

This prevents the problem where the same campaign gets counted twice in revenue reports.

Type 4: Outlier Checks

Does the data look reasonable compared to historical averages?

• Historical average campaign budget: $5,000
• New campaign budget: $500,000
• Outlier detected

We flag it. Maybe it’s a new strategic initiative. Maybe it’s a typo. Either way, someone reviews it.

Documentation: The Boring But Crucial Part

Here’s where most teams fail.

A campaign runs. Someone makes a change to the landing page URL. Nobody documents it.

Six months later, your analytics team is analyzing campaign performance and wondering why the conversion rate changed dramatically. They dig for two days before discovering: “Oh, they changed the landing page.”

This wastes time and erodes trust in analytics.

The Fix: Mandatory documentation of changes.

We set up a “Campaign Change Log” for each campaign:

CAMPAIGN: EMAIL_GROWTH_PROMO_AUG_24

Aug 1, 2024: Campaign launched. Initial audience: 50,000 people
Aug 3, 2024: Landing page updated (changed CTA button color from blue to red). Owner: Jane (Marketing)
Aug 5, 2024: Audience expanded to 75,000 after strong early performance. Owner: John (Growth)
Aug 7, 2024: Email subject line changed due to spam filtering issues. Owner: Jane
Aug 15, 2024: Campaign paused for 48 hours (competitor launched similar offer). Owner: John
Aug 17, 2024: Campaign resumed with new targeting (excluded competitors' customers). Owner: John

Now, when someone analyzes this campaign, they see: “Ah, the conversion rate changed on Aug 3 because the landing page changed. The decline on Aug 15-17 was intentional (competitive pause).”

No confusion. No wasted investigation time.

Access Controls: Preventing Accidental Disasters

Here’s a scenario we see constantly:

A junior analyst gets access to the analytics database. They’re trying to understand how a column works. They accidentally delete six months of campaign data.

Nobody has a backup. The data is gone.

The Fix: Role-based access controls.

• Analysts: Can read and analyze data. Cannot edit or delete.
• Data Engineers: Can edit and delete. But deletions require a second approval.
• Marketing Team: Can create campaigns and edit their own campaigns. Cannot edit other teams’ campaigns.
• Admins: Can do anything. But all actions are logged.

This prevents accidents. And it creates an audit trail (if someone deletes data, we know who and when).

Archiving Old Data

You have campaigns from 2020. They’re completed. They’re cluttering your database.

You could delete them. But that destroys historical context (you might want to compare current campaign performance to 2020 baselines).

Instead, we archive them.

Archived data is:

• Moved to cold storage (cheaper than active database)
• Still searchable and accessible (but slower)
• Protected from accidental changes
• Kept for 7 years (for compliance reasons)

Active campaigns (current and recent) stay in your main database. Old campaigns go to archive.

The Rollout

We typically implement data governance in phases:

Phase 1 (Week 1): Assessment

• Audit your current data
• Document all the ways naming is inconsistent
• Calculate the cost (time wasted, errors made)

Phase 2 (Week 2-3): Design

• Build naming conventions
• Design data quality checks
• Create documentation templates

Phase 3 (Week 4): Training

• Train your team on the new standards
• Update all your tools to enforce the standards
• Publish the guidelines in your wiki

Phase 4 (Week 5+): Enforcement

• Monitor compliance (are people using the naming standard?)
• Fix the 20% of data that doesn’t comply
• Adjust the standard if it’s not working

The Boring But Real ROI

You might be thinking: “This is all very organized, but what’s the financial benefit?”

Here’s the math:

• Time savings: Your team spends 5 hours per week manually mapping data between systems. That’s 260 hours per year. At $75/hour (loaded cost), that’s $19,500 per year.
• Error reduction: Bad data causes wrong decisions. You approve a campaign that shouldn’t scale, or you cut a channel that actually works. This costs you 5-10% of ad budget wasted. On a $500k budget, that’s $25-50k per year.
• Faster decision-making: Instead of spending 2 days investigating anomalies, you spend 30 minutes (because the change log documents everything). That’s 40+ hours per year of analyst time freed up.

Total benefit: $50-70k per year.

This system costs about $5-10k to implement (tooling + setup).

Payback period: Less than 2 months.

The Takeaway

Data governance isn’t exciting. It won’t get you promoted.

But it’s the foundation that makes everything else work.

Without good governance, your analytics are constantly breaking. Your team wastes time fixing problems that shouldn’t exist. Your board makes decisions based on data you don’t trust.

With good governance, your analytics work. Your team focuses on insights, not firefighting. Your decisions are fast and confident.

We help you build this. It’s unsexy. But it’s essential.