So you need to migrate some or all of your marketing data, but not sure where to begin? Maybe you’re switching from Google Analytics to GA4, moving your CRM from HubSpot to Salesforce, or consolidating multiple ad platforms into a centralized data warehouse like Snowflake or BigQuery. Or perhaps you’re simply trying to clean up your reporting stack—merging duplicate datasets, streamlining data sources, or moving from messy spreadsheets to an automated dashboard.
Whatever the case, marketing data migrations can feel overwhelming. It’s not just about transferring numbers from one place to another—it’s about making sure your campaigns, reports, and analytics keep running smoothly without losing crucial insights along the way.
The tricky part? Migrations don’t always go as planned. Data can go missing, formats might not match, and suddenly your campaign performance reports don’t add up. But don’t worry—we’ve got you covered. In this guide, we’ll walk through the best practices for onboarding marketing data seamlessly, so you can avoid common pitfalls and make your migration as painless as possible.
(Speaking of pitfalls, if you want to know what not to do, check out our helpful guide on the most common migration mistakes!)
1. Start with a clear migration strategy
Before you start shifting data around, take a step back and ask: Why are we migrating, and what do we need to achieve? A successful marketing data migration isn’t just about moving numbers—it’s about ensuring the new setup serves your team’s needs without disrupting reporting, automation, or decision-making.
First, define what success looks like. What insights should your team be able to access once the migration is complete? Do you need a single dashboard that consolidates all campaign performance at a glance? More accurate attribution modeling? Faster, automated reporting? The goal isn’t just to move data—it’s to ensure the new system delivers the visibility, efficiency, and decision-making power your team needs. Once you have a clear vision of the end result, the steps to get there will fall into place.
Next, identify key stakeholders—not just IT, but also the marketers, analysts, and data teams who rely on this information every day. They’ll have valuable insights into what’s essential to keep, what can be left behind, and what improvements can be made along the way.
Finally, map out your dependencies. Which dashboards, automated reports, or campaign tools are connected to this data? Will renaming fields or changing structures break existing workflows? A well-planned migration ensures that when the switch happens, your marketing team isn’t left scrambling to fix broken connections.
2. Consider data quality before migration
Depending on where you are moving your data, you might need to think about the quality of your data prior to migration. If you’re moving to a tool that helps with data quality, this might not be necessary, but if you’re not, you will certainly want to make sure your data is clean first.
Think of your data migration like moving to a new house—you wouldn’t pack up broken furniture or expired pantry items, so why move outdated, duplicate, or messy data? Before transferring anything, audit and clean your data to ensure you’re only migrating what’s accurate, relevant, and necessary.
Start by identifying duplicates, missing fields, and inconsistencies in naming conventions. For example, if your CRM contains multiple variations of the same customer name, it could create confusion when syncing with your new system. Standardizing campaign names, UTM parameters, and customer records before migration will prevent data integrity issues down the line.
Also, consider compliance requirements. With regulations like GDPR and CCPA, some customer data might need to be anonymized or excluded from migration. Ensuring compliance from the start prevents headaches later.
3. Choose the right data integration and migration tools
Not all data migrations are created equal. The right approach depends on your data complexity, volume, and sources. Are you migrating structured data from a CRM? Moving campaign performance data from multiple ad platforms? Or consolidating everything into a central marketing data warehouse?
For smaller migrations, manual exports and imports (e.g., CSV files) might work, but they’re risky for large-scale migrations due to errors and inconsistencies. For more complex data pipelines, ETL (Extract, Transform, Load) tools like Adverity help automate and streamline the process.
Consider these factors when selecting a tool:
- Compatibility – Does it integrate with your existing platforms and tools (Google Ads, Meta, HubSpot, etc.)?
- Scalability – Can it handle increasing data volumes as your marketing efforts grow?
- Real-time vs. batch processing – Do you need live updates or periodic syncs?
For more information on selecting the right tool, check out our blog on the Top 7 Data Extraction Tools.
4. Test on a small subset of data before going live
Never migrate your entire dataset in one go—unless you enjoy the thrill of unexpected reporting errors and broken dashboards. Instead, start by migrating a smaller, controlled portion of your data—such as a single market, business unit, or campaign type.
This allows you to map out your ideal setup, ensuring the structure, naming conventions, and reporting formats align with your goals. Onboard only this subset of data first to check for formatting issues, data mismatches, and any other discrepancies. Compare reports from your old and new systems to ensure consistency in campaign performance metrics, attribution models, and audience segmentation.
Test your migration strategy on a small subset of data first.
During testing, involve end users (your marketing team) to validate the data. If they spot missing conversions, incorrect attribution, or inconsistencies in ad spend calculations, it’s much easier to fix them before the full migration. By testing on a smaller scale, you can catch and resolve errors before rolling out the full migration, minimizing disruption to your marketing operations.
Once this test setup is complete and meets your requirements, you can use the same framework and settings to roll it out across all other markets, business units, or clients. Standardizing the process ensures consistency across teams while reducing the risk of errors, making the full migration smoother and more efficient. 5. Keep a data governance framework in place
Marketing data can quickly become messy if there’s no clear governance structure. Before you start your migration, it’s critical to define who owns what, how data should be structured, and what processes keep it clean.
A data governance framework includes:
- Defined roles and responsibilities – Who manages campaign naming conventions? Who ensures CRM data stays accurate?
- Access controls – Not everyone needs full access to all data. Setting permissions prevents accidental deletions or modifications.
- Documentation – Keep a data dictionary that outlines naming conventions, key metrics, and standard reporting formats.
Establishing these rules and responsibilities before migration ensures a smoother transition and prevents data inconsistencies from carrying over to the new system.
For more on data governance frameworks, check out our blog What is a Data Governance Framework? A Guide for Marketers.
Key scalability considerations
Migrating your marketing data isn’t just about making the switch—it’s about ensuring your new setup can scale with your business. What works today might not work a year from now when you’re running more campaigns, using new channels, or handling larger datasets.
For example, you’ll need to think about data accessibility and governance at scale. As your team grows, who needs access to what? Implementing role-based permissions and clear documentation ensures that data remains structured, usable, and secure—even as more people interact with it.
A scalable data foundation saves time, keeps reporting consistent, and makes it easy to grow without the headaches. It builds trust in your data, ensures teams can access what they need, and adapts as priorities shift. With the right setup, you can expand into new markets effortlessly, onboard new business units, and integrate new platforms without breaking your existing workflows—no matter how big you get.
Automate as much as possible
Managing and growing your data operations shouldn’t require endless manual work. The more automation you put in place now, the easier it will be to replicate, customize, and expand your data infrastructure across teams, markets, and regions.
With the right automation tools, you can:
- Clone and bulk edit data connections to onboard new business units quickly.
- Reuse naming conventions, transformations, and authorizations to maintain consistency at scale.
- Set up automated quality checks to monitor for data discrepancies before they impact reporting.
- Standardize access permissions so new users are onboarded efficiently without creating security risks.
It’s a good idea to set up automated quality checks to monitor for data discrepancies as volumes grow. The more marketing channels you integrate, the greater the risk of mismatched formats, duplicate entries, or tracking inconsistencies. Automated alerts can help you catch these issues early before they disrupt reporting.
A scalable data foundation isn’t just about growth—it’s about adaptability. By setting up your data infrastructure with future needs in mind and automating where possible, you can scale up as business priorities shift, ensuring your marketing operations remain agile, efficient, and insight-driven.
Final thoughts: building strong data foundations for long-term success
Marketing data migration doesn’t have to be a nightmare—it just requires the right approach. By planning ahead, cleaning your data before the move, testing thoroughly, and thinking about long-term scalability, you can ensure your marketing team has reliable, accurate, and accessible data to power campaigns and insights.
The key takeaway? Data migration isn’t just about moving information—it’s about future-proofing your marketing operations and building a foundation you can scale from. A well-executed migration sets you up for better reporting, smoother automation, and more intelligent decision-making down the line.
