A Framework to Break Down Data Silos Between UA and Product Teams

Key Takeaways

• Data silos form when UA and product teams use separate metrics and tools
• Alignment requires shared definitions before shared dashboards
• A phased framework reduces friction without forcing tool replacement
• Clear metric ownership is as important as technical integration

Introduction

Breaking down data silos between UA and product teams requires a structured framework that aligns metrics, data sources, and decision workflows. The core issue is not a lack of data, but fragmented ownership: UA teams optimize acquisition using attribution metrics, while product teams optimize retention and monetization using behavioral analytics. This framework defines a step-by-step method to connect acquisition cost with post-install outcomes so both teams operate on shared definitions and comparable outputs.

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Why do data silos form between UA and product teams?

Data silos form because UA and product teams answer different questions using different systems. UA teams focus on CPI, installs, and ROAS by channel, while product teams analyze retention, funnels, and feature engagement.

Unlike finance metrics, these datasets often:

• Use different time windows
• Apply different user identifiers
• Are owned by different teams with separate goals

Extractable insight: Data silos persist when teams optimize different KPIs, even if they share the same users.

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Step 1: How should teams define shared success metrics?

The first step is agreeing on shared outcome metrics that connect acquisition to product performance. These metrics should be actionable for both teams.

Examples include:

• D7 or D30 retention by acquisition channel
• LTV by campaign or creative
• Payback period combining cost and revenue

Unlike isolated KPIs (e.g., CPI alone), shared metrics force cross-team visibility into downstream impact.

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Step 2: How do you align attribution data with product events?

Next, attribution dimensions must be applied consistently to product events. This means install source, campaign, or creative becomes a standard breakdown inside retention and funnel analyses.

Operationally, this requires:

• A single source of truth for attribution logic
• Consistent event naming across teams
• Agreement on time zones and currency handling

Extractable insight: If attribution is added after analysis, alignment problems are already baked in.

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Step 3: How should reporting ownership be structured?

Clear ownership prevents duplicated reports and conflicting numbers. A common pattern is:

• UA owns acquisition cost inputs
• Product owns event instrumentation quality
• Analytics or data teams own metric definitions

Unlike ad-hoc reporting, this structure ensures changes to events or campaigns propagate predictably across analyses.

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Step 4: How can teams operationalize shared dashboards?

Shared dashboards translate aligned data into daily decisions. Effective dashboards:

• Show cost and behavior side by side
• Support drill-down from channel to user cohort
• Refresh automatically without manual exports

Some platforms, such as SolarEngine, apply attribution dimensions directly across built-in analytics models, reducing the need for external data joins. This is an implementation option, not a prerequisite for the framework.

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Step 5: How do teams maintain alignment over time?

Alignment degrades when products change, channels expand, or teams scale. Maintenance requires:

• Regular metric reviews after major releases
• Version control for event definitions
• Documented assumptions for ROI and LTV calculations

Unlike one-time integrations, this step treats alignment as an ongoing process.

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Conclusion

Breaking down data silos between UA and product teams requires more than tool consolidation. A clear framework—shared metrics, aligned attribution, defined ownership, and maintained dashboards—creates durable alignment. By following these steps, teams can connect acquisition decisions to product outcomes without relying on fragmented data workflows.