The Zuma Roundtable Summary

Recently Zuma's data leadership roundtable reconvened. Again we brought together hands-on data leaders to talk about their most pressing challenges, learnings, and, recommendations for life on Berlin's frontline of data in business-marketing.

?? A huge thanks to those contributors: Ashish Thakur (Kayzen), Marc Roulet (sevdesk), Natalia Wierzbowska (Hotjar | by Contentsquare), and Oisin McKnight ( Kittl )

The ZUMAs: Matt Brady , Joe Vaughan

?? And now for the highlights and recommendations ....

We covered:

Marketing Attribution, GA4, GTM

Best practices for event tracking

Leadership buy-in

Collaboration with product and other analysts

For Data Leaders and ICs operating in Marketing

1. Handling Large-Scale Data. Companies deal with huge amounts of data daily, so having a solid data management plan is a must. Without clear processes, it’s tough to predict what marketing strategies will work best. Data leaders should establish clear governance and automation strategies to manage data efficiently.

2. Working Around Google Analytics 4 (GA4) Limits. GA4 only lets you export a million events per day, which can mess with your marketing insights. Important data like first visits and session starts often get left out. To maintain accurate insights, consider upgrading to Google Analytics 360 or supplementing GA4 with additional attribution models, such as multi-click or data science-driven approaches.

3. Assigning Tag Management Ownership. Someone needs to own Google Tag Manager (GTM) to avoid messy tracking setups. The right team depends on company structure, but in the meantime, bringing in a freelancer could be a quick fix. Ensuring a dedicated owner or team for tag management will reduce inconsistencies and improve data reliability.

4. Solving Attribution Challenges. Google and Meta each have their own way of tracking conversions, often leading to mismatched numbers. Building an in-house multi-touch attribution model or leveraging a third-party tool can provide more accurate cross-platform insights and reduce reporting discrepancies.

5. Keeping Event Tracking Organized. Without proper governance, tools like Mixpanel and Segment can lead to messy data. Standardizing event names, keeping good documentation, and assigning clear responsibilities will help make data more useful. A well-maintained event-tracking framework prevents data silos and ensures consistency across teams.

6. Prioritizing Documentation and Standardization. A lack of documentation makes tracking confusing and inefficient. Setting up standardized event tracking across platforms is time-consuming but worth it in the long run. Data leaders should champion documentation initiatives and make it a core part of the data strategy.

7. Improving Data Accuracy with Server-Side Tracking. Ad blockers and browser restrictions can lead to lost data, which hurts marketing efforts. Moving critical tracking to the server side can help keep data accurate. Investing in server-side tracking can enhance data reliability and minimize disruptions caused by browser limitations.

8. Getting Leadership Buy-in for Big Projects. Convincing leadership to invest in data-heavy projects isn’t always easy. Aligning these initiatives with business goals and networking internally can help secure the resources needed. Framing data projects in terms of ROI and business impact will make them more appealing to decision-makers.

9. Making Product Analysts More Strategic. Product analysts shouldn’t just act as service providers—they should be seen as strategic contributors. This ensures that marketing insights are actually used to guide business decisions. Encouraging analysts to proactively provide recommendations based on data trends will improve business outcomes.

10. Balancing Analyst Specialisation and Flexibility. Specialised analysts bring deep expertise, but keeping some generalists on board helps connect insights across different business areas. Avoiding early specialisation keeps teams more adaptable. Data leaders should strike a balance between specialisation and cross-functional exposure to maintain agility in analytics teams.

11. Building a Strong Web Analytics Team. As data teams grow, communication and effective stakeholder support get harder. Encouraging analysts to develop business knowledge along with technical skills helps them provide more valuable insights. Promoting a mix of technical and business acumen among analysts will increase their impact on marketing strategies.

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What’s the biggest challenge in marketing analytics for your team today, and how should data leaders tackle it?

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