Your product can't see itself.
Making instrumentation a planning tool, not an afterthought.
Last month, imagine your team shipped a new checkout flow they'd spent six weeks building. The metrics looked good in testing. The design felt intuitive. But within hours of release, support tickets doubled.
The problem? Users were getting stuck at payment confirmation but the error messages weren't firing. Nothing in monitoring caught it because that specific pathway wasn't instrumented. Your team was flying blind.
This scenario plays out constantly across product teams. We release features confidently, only to discover we can't actually see how they're performing in the wild.
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The backward approach we've normalized.
Most of us treat observability as a technical add-on. We debate feature specs, draw wireframes, build, then tack on some basic analytics before shipping. "We'll add better monitoring in the next sprint," we promise ourselves, knowing full well that next sprint will bring new priorities.
I've been guilty of this myself. Before we had proper observability tools, our team pushed out a feature in our customer portal designed to make it easier for users to upload documents as part of the mortgage process. But we had no idea how it was received or if there were any errors.
It wasn't until we implemented tools that provided both system analytics and session recordings that we could actually see what was happening. The visibility revealed a couple of bugs causing significant user struggles – issues we'd been completely blind to before. Without observability built in from the start, we were just guessing.
Flipping the product planning script.
What's changed my approach completely is treating observability as an input to planning rather than an output of development.
Before we even design a feature now, I ask: "What would we need to measure to validate our hypothesis?" and "What signals would tell us if this is working?" This approach creates a shared language around learning goals with engineering teams. The conversation shifts from "how long will this take?" to "how will we know if we're right?"
For a recent onboarding redesign, this meant mapping out key drop-off signals before writing a single line of code: time spent on each step, error rates on form submissions, success paths versus abandonment patterns. This clarity changed what we built. We realized we needed to capture context alongside interaction data – was this a first-time user or someone who'd previously abandoned? What marketing channel brought them?
Making it practical with Monday-morning moves.
You're thinking: "Nice idea, but we're already stretched thin."
I get it.
Here's how to start small but meaningful:
Pick your critical path: Choose one high-value user journey that's central to your product's success. For us, it was a landing page that generated leads and accounts.
Create your learning map: Schedule a 30-minute session with your tech lead and map what you need to know at each step. What indicates success? Where might users drop off? What context matters?
Define observable signals: Turn questions into specific metrics. Instead of "Do users like this form?" ask "At which form field do we see the highest abandonment rate?" or "Which error message appears most frequently?"
Set up your first dashboard: Work with your data team to create a simple dashboard focused just on this journey. Don't try to measure everything - start with 3-5 critical signals.
Start a weekly observability review: Spend 15 minutes each week with the team looking at the data. What's it telling you? What's missing? What surprises you?
When we did this with our mortgage portal, engineers stopped viewing instrumentation as overhead and started seeing it as essential. The small investment in upfront planning paid off immediately.
The compound returns of seeing clearly.
After implementing observability tools in our mortgage portal, we experienced a transformation. The right instrumentation allowed us to spot issues in real-time and make adjustments without lengthy debates. What used to take three meetings now got resolved with actual evidence.
The real payoff wasn't just faster iterations - it was institutional knowledge. We built an evolving map of how users actually interacted with our product versus our assumptions. Those document upload issues? We now have clear data on completion rates and error patterns that guide every new feature we add.
While competitors were still debating what users wanted, we were watching what they actually did.
A moment of truth.
Here's a simple test: Think about the last feature your team shipped. If it failed to meet expectations, would you be able to pinpoint precisely why?
If your answer is "not really," you've identified the gap this approach addresses. Observability-first planning doesn't just help you build better features – it helps you build better understanding.
Next week when you're sketching out requirements for that new feature, try starting with: "What do we need to learn, and how will we see it?"
Your future self, staring at dashboards trying to figure out what happened, will thank you.
Until next week,
Mike @ Product Party
Want to connect? Send me a message LinkedIn or Bluesky.
