“We can build that ourselves”
If you're a software company, you've heard this before.
A product manager asks for better visibility into feature usage, or a customer success lead wants to know which accounts are showing signs of churn. A developer raises an eyebrow and says, “Yeah, we can build that.”
And honestly? They probably can. Today’s engineers are smart. They’ve got SQL, they’ve got Looker, maybe Metabase, and your data’s sitting nicely in Snowflake or Postgres. Writing a query to show how many users clicked a button last week is no big deal.
But here’s the thing: product analytics isn’t just about writing a few queries. That’s the easy part.
Step 1: Get the data in (easier said than done)
Let’s say you want to track every click, view, and interaction across your app. If you’re piping data through Segment or RudderStack, you’ve got a head start. But if not? Buckle up.
You’ll need to:
Stand up an ingestion pipeline
Roll out an update to your product to point your event stream at your new ingestors
Handle high-volume event streams without dropping data
Transform that firehose into something structured and queryable
Ensure nothing breaks when the product changes (which it will)
This alone can turn into a full-on infrastructure project. And that’s before you’ve even built a single dashboard.
Step 2: Actually make it useful
So now you’ve got dashboards. Maybe some pretty charts. But… so what?
Are the metrics up to date with the latest product release? Does your sales team trust them? Can your CS team understand them? Does anyone know what that “Engagement Index v2” actually means?
Analytics that no one uses aren’t analytics. They’re screenshots for the board deck. And usually, they’re screenshots that no one feels confident enough to explain.
To make analytics stick, you need:
A shared language for metrics
Clear ownership for upkeep
Documentation people can actually follow
The ability to adapt as “what good looks like” evolves
And most importantly, you need to get the data into the tools people already use—whether that’s Jira, Zendesk, Intercom, or Slack.
Which brings us to...
Step 3: You’re building integrations now (Surprise!)
Want to send usage insights to Sales in Salesforce? Or surface feature flags in Jira for Engineering?
Congrats, you’re in the integrations business now.
You’ll need to:
Authenticate with third-party systems
Respect API rate limits
Handle retries, failures, and timeouts
Maintain security and compliance (because yes, those Salesforce tokens are sensitive)
Oh, and every integration adds new surface area for bugs, outages, and the late-night “why isn’t this working?” messages.
Step 4: Someone has to own it (forever)
This is maybe the most overlooked bit.
When a developer builds a dashboard, who owns it next quarter? Who updates the metrics when your pricing plan changes? Who makes sure the data model still matches the app? Who responds when security asks, “Hey, what’s this open port on the analytics box?”
Analytics infrastructure isn’t a weekend project. It’s a product. One that:
Needs maintenance
Needs support
Needs security reviews
Needs context, continuity, and care
Unless you're happy relying on the one engineer who built it (and hope they never take another job).
Step 5: It’s never just product data
Sooner or later, someone will ask: “Can we blend this with our billing data? What about MRR per segment? Can we see adoption versus ARR?”
Now you’re pulling from Stripe, your CRM, your marketing tools. Now you’re in the deep end.
Financial data means:
Access control
Encryption
Auditing
Business logic that’s non-obvious and often… fluid
You’re no longer building dashboards. You’re building trust. And that’s much harder to version-control.
So, can you build it?
Yes. But here’s the better question: should you?
Building analytics infrastructure means signing up for a long-term, cross-functional, always-evolving commitment. It means thinking beyond the query and owning the pipeline, the metrics, the trust, and the tools people work in.
That’s why we built Accoil.
We handle the pipelines. We manage the metrics. We make sure the right people get the right data, in the right place, at the right time.
So your teams can stop asking “Can we build it?” and start asking, “What are we going to do about what we just learned?”