How to Build a Single Source of Truth for Marketing Data

How to Build a Single Source of Truth for Marketing Data

I’ve seen the same problem at 20+ companies. Marketing data lives in seven different places. Google Ads shows one cost. Meta shows another. HubSpot says the lead came from organic. Salesforce says it was a paid click.

Everyone argues. No one trusts the numbers.

You need a single source of truth.

Not a spreadsheet. Not a weekly export. A data warehouse that connects every platform. A system where you query once and get the real answer.

I’ve built these systems for B2B SaaS companies spending $50k to $2M per month on ads. Here’s exactly how to do it.

Why Marketing Data Unification Matters

Every marketing team I work with has the same complaint: “We don’t know what’s working.”

Without a single source of truth, you get:

• Duplicated metrics – cost appears in two platforms, you double count.
• Missing data – offline conversions never get back to ad platforms.
• Delayed reports – someone pulls data on Monday, another on Wednesday. Numbers differ.
• Faulty attribution – last-click vs first-touch vs multi-touch. Without shared data, you guess.

According to a 2023 report by Gartner, 62% of marketing leaders say data fragmentation is their top barrier to measuring ROI. That’s not opinion. That’s survey data.

Marketing data unification solves this. You bring all raw data into one place. You model it. You report on it. Everyone uses the same numbers.

The Cost of Not Doing It

Let’s be specific. At a client with $500k monthly ad spend, bad data cost them $40k per month in wasted budget. Why?

• They were retargeting users who already converted (cost $12k).
• They double-counted leads, inflated ROAS, and scaled a losing campaign (lost $18k).
• They missed a high-performing channel because raw data didn’t flow to their attribution model ($10k).

After building a unified data warehouse, they saved $480k per year.

Step 1: Choose Your Data Warehouse

The single source of truth starts with a home for your data. Three options dominate the market.

My pick for most marketing teams: BigQuery.

Why? Integration with Google Ads, Google Analytics 4, and Looker. Zero maintenance. Pay per query. For a mid-sized company spending $100k/mo on ads, BigQuery storage runs about $20–$50 per month. Queries cost pennies.

What About Data Warehousing Architecture?

Don't overthink this. You need three layers:

• Raw data – exact copy from APIs.
• Transformed data – cleaned, unified, deduplicated.
• Reporting data – aggregated for dashboards.

Tools like dbt Labs handle the transformation layer. We'll cover that in step 3.

Step 2: Extract Data from Every Marketing Source

You can’t unify what you don’t have. You need to pull data from every platform you use.

Common sources:

• Google Analytics 4
• Google Ads
• Meta Ads Manager
• LinkedIn Ads
• HubSpot or Salesforce
• Shopify or other ecommerce
• Segment (customer data platform)

There are two ways to extract.

Option A: Use a Managed Connector (Easiest, 2–3 hours setup)

Tools like Fivetran, Airbyte, and Segment have pre-built connectors. You log in, authorize, and they pull data on a schedule.

Fivetran pricing: starts at $100/month for up to 2 connectors. Scales to $1000+/month. Worth it if you have 5+ sources.

Airbyte: open source. Free if you self-host. Cloud version starts at $0.5 per sync run. Very cost-effective.

Segment: good for event data (page views, clicks). But expensive for marketing attribution. Starts at $120/month for limited events.

My recommendation: Start with Airbyte open source. It’s free. Run it on a small cloud server ($20/mo on DigitalOcean). Many connectors are well-maintained.

Option B: Build Your Own with APIs (Cheap, 2–3 weeks dev time)

Write Python scripts that call each platform’s API. Store the JSON responses in cloud storage, then load into BigQuery.

Cost: zero software fees. But you need a developer.

Time: 5–10 days for first platform, then 1–2 days each for additional.

Maintenance: APIs change. I’ve seen Meta Ads API break twice in one year. If you don’t have a data engineer, go with managed.

Step 3: Transform and Unify the Data

Raw data is messy. Google Ads reports cost at the campaign level. HubSpot reports leads at the contact level. Meta gives impressions at the ad level.

You need to build a unified model.

The Core Unified Table

Create one table that has:

• date
• source (platform name)
• channel (paid_search, paid_social, organic, email, etc.)
• campaign_name
• ad_group (if applicable)
• impressions
• clicks
• cost (in same currency)
• conversions (based on your shared definition)
• revenue (if trackable)

Then, for each platform, write a SQL transformation (using dbt) that maps their fields to your unified schema.

Example dbt model for Google Ads:

SELECT
    date_date AS date,
    'Google Ads' AS source,
    'paid_search' AS channel,
    campaign_name,
    ad_group_name,
    impressions,
    clicks,
    cost / 1e6 AS cost, -- Google returns micros
    conversions,
    conversions_value AS revenue
FROM raw_google_ads.campaign_performance

Repeat for Meta, LinkedIn, etc.

Handling Discrepancies

Platforms report different conversion windows. GA4 counts a conversion if click happened within 90 days. Meta uses 7-day click, 1-day view. This mismatch is the #1 reason for data fights.

Rule I use: Define your own “attribution window” in the warehouse. For example, 7-day click-through attribution. Then apply that same window to all platforms. This makes numbers comparable.

Use dbt to apply these rules. You can change the window any time and rerun the entire history.

Deduplication

If a user clicks a Google ad and later a Facebook ad, each platform claims the conversion. Your unified table must handle this.

Method: Hash the user ID + conversion event + timestamp. Keep the earliest touchpoint for each unique conversion. This is called “strict deduplication.” It reduces attributed conversions by 15–25%, but it’s the truth.

Step 4: Build a Reporting Layer

Now you have clean, unified data. But raw tables are too granular for stakeholders.

Create aggregated views:

• daily_channel_performance – by channel, date.
• monthly_campaign_overview – by campaign, month.
• weekly_roas_summary – by channel, week, with cost and revenue.

Then connect a visualization tool.

Popular options:

• Looker – builds on BigQuery natively. Excellent for embedded analytics.
• Tableau – great for interactive dashboards.
• Google Data Studio – free, but limited. Fine for 5–10 dashboards.

My recommendation: Use Looker if you have budget ($3k+/year). Use Data Studio if you’re starting small.

What a Great Dashboard Looks Like

One page. Top row: total cost, total conversions, cumulative ROAS. Below: a bar chart of cost per channel, a line chart of weekly ROAS, a table of campaign-level performance.

No more than 10 metrics. Stakeholders must be able to find the answer in 10 seconds.

Step 5: Maintain and Iterate

Data pipelines break. APIs change. New campaigns start.

Assign a data owner. For small teams, that’s one person 5 hours per week. For larger teams, a data engineer.

Maintenance checklist (weekly):

• Check last sync timestamp for each source.
• Compare total cost in warehouse vs platform UI.
• Review dbt test failures (if you set them up).
• Update attribution rules if needed.

Automation ideas:

• Send a Slack alert if BigQuery cost exceeds $50/day.
• Use dbt snapshots to capture changing dimension tables (e.g., campaign names).
• Schedule a daily refresh of Looker dashboards.

Real Results from One Client

I worked with a B2B SaaS company spending $750k per month on ads. Before unification, they had six people manually reconciling data every week. After:

• Time saved: 20 hours/week (worth about $2000/week in salary).
• Data accuracy: from 60% to 98% agreement with ad platforms.
• ROAS improved by 17% because they stopped scaling low-performing channels.
• They discovered that LinkedIn Ads contributed 30% more leads than originally thought, simply because data was now correctly attributed.

Common Mistakes (And How to Avoid Them)

Mistake 1: Trying to Be Perfect on Day One

Don’t model all 20 dimensions upfront. Start with three essential dimensions: date, source, channel. Add campaign and ad_group later.

Mistake 2: Ignoring Offline Conversions

If you have call tracking or offline events, they must flow back. Use Segment or custom API integrations.

Mistake 3: Not Involving Stakeholders

Finance wants cost. Sales wants lead quality. Marketing wants ROAS. Get their requirements before building the dashboard. Otherwise, you’ll rebuild twice.

Mistake 4: Over-Engineering

You don’t need real-time streaming. Daily sync is enough for most marketing analysis. Batch processing costs less and is simpler.

Your Next Step

Building a single source of truth for marketing data isn’t optional anymore. Your competitors are already doing it. They have 15% lower cost per lead and 20% better ROAS because they trust their numbers.

You can do this yourself using the steps above. But if you want a proven system in place in 2 weeks without hiring a full-time engineer, we can help.

At DG10 Agency, we specialize in marketing data unification and data warehouse projects. We’ve built for clients spending $50k to $2M monthly.

We start with a 2-hour audit of your current data stack. Then we design a pipeline that works for your exact sources.

Want a free 30-minute consultation? We’ll show you a sample unified dashboard using your own data.

👉 Contact DG10 Agency about data consulting

No sales pitch. Just a walkthrough of how we’d unify your data and a roadmap you can follow.

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