How to Centralize Your Marketing Data for Better Insights & Growth

by Scott Zakrajsek •

Many brands struggle with messy, disorganized marketing data spread across multiple platforms. This fragmentation makes it nearly impossible to conduct sophisticated analyses like measurement, reporting, and forecasting. If you’ve ever wondered how to consolidate your marketing data for cleaner, more actionable insights, you’re not alone.

The good news? There’s a clear, structured approach to solving this problem. 

In this post, we’ll break down the essential steps to centralizing your marketing data, from inventorying your sources to building an automated data pipeline. This process follows the modern data stack playbook and is best viewed as a data engineering project, not just a marketing ops initiative.

The Goal: Unified, Clean, and Automated Marketing Data

Before we dive in, let’s set a simple goal: get all of your marketing data in one place—clean, accurate, and automated. This ensures better reporting, more reliable insights, and faster decision-making.

Step 1: Inventory Your Data

Start by identifying all the sources of marketing data your team relies on. This typically includes paid media platforms, analytics tools, CRM/CDP systems, and transaction data.

To ensure smooth operations:

  • Assign a data owner for each source.
  • Ensure data teams have the right access to relevant systems.
  • Document the types of data collected from each platform.
  • Establish data hygiene protocols to ensure quality and consistency over time.

Without a clear inventory, data integration becomes much harder down the line.

Step 2: Centralize the Data in a Warehouse

Working with scattered data across platforms, spreadsheets, and tools is inefficient. Instead, all data should be stored in a centralized data warehouse, allowing for historical tracking, automated updates, and seamless transformations.

Choosing a Data Warehouse:

Popular options include:

  • BigQuery (Google Cloud)
  • Snowflake (Independent, multi-cloud)
  • AWS Redshift (Amazon)

Additionally, you’ll need an ETL (Extract, Transform, Load) tool to sync data from various sources into your warehouse on a regular schedule (hourly, daily, etc.). Some options include:

  • Fivetran, Funnel, Airbyte for automated syncing.
  • Native APIs for custom integrations via cloud functions.
  • Reverse ETL tools (like Hightouch, Census, or CDP platforms) to push cleaned data back into marketing tools.

Spreadsheets may work in the short term, but they are manual and break at scale. Investing in a proper warehouse + ETL setup ensures long-term scalability.

Step 3: Transform the Data for Usability

Once your raw data is stored in the warehouse, the next step is making it usable and actionable. This requires a data pipeline to clean and blend the data into standardized datasets.

Examples of Data Transformations:

Instead of working with fragmented raw data, transformations allow you to:

  • Normalize impressions across platforms (Google, Meta, TikTok).
  • Filter out draft, wholesale, or internal orders from transaction data.
  • Build customer segments using first-party data.
  • Perform currency conversions for global reporting.
  • Enrich data with third-party sources for deeper customer insights.

To accomplish this, teams often use dbt (quickly becoming the industry standard), or other tools like Airflow, Dataform, or SQLMesh, depending on preference.

Step 4: Assign the Right People

Implementing this strategy requires technical expertise. You’ll need:

  • Data/Analytics Engineers – to handle ETL, warehousing, and transformations.
  • Analytics Specialists – to ensure data quality and validation.
  • Marketing Analysts – to translate data insights into strategic recommendations.

How to Resource This:

  • Internal Team: Do you have technical marketers or engineers who can own this?
  • Borrow from IT/Data Teams: Can your internal analytics team assist?
  • Partner with a Vendor: Consultancies like fusepoint specialize in data strategy and infrastructure.
  • Hybrid Approach: A mix of internal resources and external partners can offer flexibility.

Not sure what expertise you need? fusepoint can help you assess your team’s strengths and gaps to build the right resourcing strategy.

Summary: The Core Process

  • ETL pulls raw data daily.
  • Warehouse stores historical data (both raw and transformed).
  • Transformation layer standardizes key metrics.
  • Analytics processes turn data into actionable insights for business impact.

Cost & Timeline Considerations

The cost and setup time depend on:

  • The number of data sources.
  • Your team’s technical skillset.
  • The complexity of required transformations and reporting needs.

To maximize value, start with a focused set of high-priority data sources before scaling up.

Common Pitfalls & How to Avoid Them

  • Inconsistent Data Naming: Establish clear naming conventions across all platforms.
  • Lack of Ongoing Maintenance: Regularly audit data pipelines to ensure accuracy.
  • Underutilizing Insights: Ensure marketing and leadership teams are trained on how to interpret and act on the data.

Next Steps: Let’s Build Your Data Infrastructure

If you’re unsure where to start or need help optimizing your marketing data stack, fusepoint is here to help. As a data and strategy consultancy, we specialize in streamlining marketing operations, ensuring data accuracy, and unlocking growth opportunities through better measurement.

Book a free consultation to assess your current data setup.