Product Analytics: Why Your CDP Data is a Liability

Executive Summary

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A familiar problem: inconsistent data no one trusts

It's a common enough situation. The big feature has been live for a month. In the product review, everyone looks at you. You open the dashboard connected to your expensive new CDP, ready to show how the new workflow is helping things. But the data doesn't add up.

One chart shows `user_clicked_button`. Another tracks `button_click_final`. A third, built by a different engineer, is based on `ClickedButton_V2`. No one in the room, including you, can say with any confidence which one is right. The conversation shifts to anecdotes and gut feel. The dashboard is closed, and that large investment in your CDP sits quietly, a reminder of good intentions that haven't quite paid off.

This isn't just a reporting issue. It's a sign of a deeper problem with how you collect data. You haven't just bought a data platform: you've bought a very efficient way of collecting chaotic data.

The cause: treating event tracking as an afterthought

In my experience, this is a common problem for scale-ups. The company invests in the modern data stack: you have the CDP, the warehouse, the BI tool. But a critical, and admittedly unglamorous, step has been missed: creating a blueprint for your data.

You've likely moved to the cloud and hired clever engineers. But all that's happened is that the existing mess has been moved somewhere else, only faster. Automating a broken process just means you generate bad data at speed.

What's usually happening is that event tracking is treated as a low-priority engineering ticket, not as a core part of building the product. Without a shared vocabulary and a process for defining events, each developer implements tracking in their own way. The result is data that no one trusts. Your Product Analytics work struggles to get going, because the foundation isn't solid.

This isn't just untidy. It creates real problems for the business. You can't build a reliable retention model, you can't run A/B tests you can trust, and you certainly can't build any useful AI features on top of it all.

The solution: treat your tracking plan like a product

The tool is rarely the problem. The problem is that the business logic for what to track is scattered across the codebase, rather than being managed in one central place. The way to fix this is to stop treating Data Tracking as an afterthought and start treating it like a product, with its own development process.

Create a shared dictionary of events: Before any code is written, the event needs to be defined in a central, accessible place. What does `user_activated` mean? What properties must it contain? This isn't a technical job. It's a product job. This process of Metric Standardisation is the foundation of trustworthy data.

Agree on who owns what: The Product Manager owns the 'what' and the 'why'. They are responsible for defining the user behaviours that matter to the business. Engineering owns the 'how'. They are responsible for implementing the tracking code to match the agreed specification. This clear separation of roles is very helpful.

Build in some simple, automated checks: This isn't about creating bureaucracy. It's about building automated guardrails. A sensible Data Governance framework might use a tool to manage your tracking plan, or it could be as simple as a strict review process in GitHub for any new event. The goal is to make it easier to do the right thing than the wrong thing.

Handling the internal resistance

Let's be honest. Implementing this will feel slow at first. Your engineers might argue that it's 'process for the sake of process'. Your Head of Growth might be frustrated that they can't launch three new experiments tomorrow. As a product leader, this is often the point where you have to hold the line.

This is the hard, unglamorous work of building a proper foundation. You are paying down the technical debt that your organisation has been putting off for years. It takes a bit of political capital to enforce, because you are asking people to slow down. But you are doing it so the business can move faster, and with confidence, for the next three years.

The outcome: making decisions with confidence

When your tracking is set up properly, the whole dynamic of product development changes. Product reviews stop being debates about whether the data is correct and become debates about what the data implies. You can measure the impact of your work with some precision.

You stop asking your data team to 'pull the numbers' and start asking them to help interpret a reality that everyone agrees on. This helps build a culture where decisions are based on shared evidence, not just opinion. This is how you move from being a company with lots of data to one with useful insights, building a foundation for User Behaviour Analytics that can properly drive the business forward.