Trust your product analytics data again
If you don't trust the data in Mixpanel, Amplitude, or Pendo, the tool isn't the problem. The implementation is. Here's what breaks, and how to fix it.
Don't trust the data your product analytics tool shows you? You're probably blaming the tool.
Mixpanel, Pendo, Amplitude, GA. Doesn't matter which one.
The go-to conclusion is "let's migrate to another one." That fixes nothing.
You'll migrate, and you'll run into the same problems you had before.
Because the tool isn't the problem. The implementation is.
Without a clean implementation, you'll never trust what it shows you. No matter the tool.
You'll use it halfway. You'll cross-check every number against another source. You'll lose trust along the way.
And before anything else, that costs you time and efficiency.
The errors that break trust in your data
The same errors show up again and again, across different implementations, in different companies.
Duplicate events. The same event fired twice (a tag pasted twice, back-end and front-end both sending the same thing without knowing it). It inflates any metric you look at.
Inconsistent taxonomy. An event is named differently on iOS than on Android, or differently in the name than in the documentation. Nobody knows which one is correct.
Broken identity. Multiple IDs for the same user, with no merge process between the anonymous and identified profile. The same user shows up as two different people.
Timestamps and time zones wrong. Events appear on the wrong day or hour. It breaks any cohort or funnel analysis with a time window.
Dev, QA, and production mixed together. Test data contaminating real reports. Nobody filters it out, nobody knows what's noise and what's real.
Properties with the wrong type. A number arriving as a string, a date arriving as free text. It breaks filters, breaks segments, breaks reports.
Lost UTMs, broken attribution. You can't tell where a user came from, so you can't tell which campaign is working and which isn't.
Lots of clicks, few events that matter. Hundreds of interaction events with no business meaning, and none that tell you whether the user reached a real moment of value.
With an implementation like that, you can't trust the data.
So the tool doesn't work for you.
Why this happens (the real pattern)
Bad technical implementation almost always comes from bad event planning, and from the engineering team not being involved in the project from the start.
Implementing tracking isn't simple. You need to define:
What each event means.
When it fires.
How it's sent: from the back end or the front end.
What properties it carries.
And this multiplies because it's almost never a single team implementing it. It's several squads (back end, web, iOS, Android) that all need to stay aligned and standardized with each other.
When that coordination doesn't get real time invested in it, you "save" time today and pay it back with interest later: rework, delays, and the business team losing trust in the numbers.
This pattern repeats in different variants:
The rushed migration. A team moves all its tracking from one tool to another under deadline pressure. Nobody stops to think through which business questions need answers. The result, months later: thousands of events, most with no real use, and the business team not trusting any report.
Switching tools as the fix. A team with 6, 7, 8 data tools already in its stack decides the problem is which CDP or which analytics platform they're using, and evaluates migrating. The planning of what each team actually needs to see (and why) never happened, in any of the tools.
Infrastructure scaling with nobody watching. User and event volume grows month over month, nobody adjusts the implementation accordingly, and delivery or capture failures stay invisible until someone from outside checks the logs.
The fix: technical audit + tracking plan + reimplementation
Whether you're reimplementing your current tool or rolling out a new one, there are no shortcuts. You need to cover these 6 points.
1. Plan the events and properties you'll track, with a standardized taxonomy.
2. Identify the source of each event: back end, front end, or both.
3. Define your user_id and identity management. When you identify the user, and how you merge the anonymous profile with the identified one. This is KEY.
4. Align the whole plan with the engineering team. Don't just send them a spreadsheet. Every definition needs to be reviewed together, in detail.
5. Implement and test in QA.
6. Only move to production once QA testing checks out.
The tracking plan isn't the first step. It's the output of having defined the first three points properly.
Skip that order, and you'll end up reimplementing the same mess, twice.
If you don't fix it, you'll want to switch tools. And it'll happen again.
It's the pattern we see the most: teams migrate from Mixpanel to Amplitude, or from Amplitude to something else, hoping the switch alone solves the problem.
But if the new implementation gets built with the same rush and without the same groundwork, you're back in the same place in six months. Just with a different logo on top of the dashboard.
Centralized isn't the same as trustworthy.
The tool won't save you from bad planning. And good planning works with any tool.
Is this happening to you right now? Let's talk.
