UppedGame
We design and maintain analytics systems that remain reliable over time.
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The Default Pattern
Most teams start here:
- open Looker Studio (or another BI tool)
- connect a data source
- start building charts
They try to “figure things out” as they go.
It feels productive.
It isn’t.
What Dashboards Actually Do
Dashboards don’t define anything.
They:
- display
- aggregate
- filter
They answer:
“How should this be shown?”
Not:
“What does this mean?”
Why Starting Here Creates Problems
When dashboards come first:
- logic gets embedded in charts
- definitions vary by report
- metrics are rebuilt repeatedly
- inconsistencies multiply
Now:
the same question produces different answers depending on where you look
The Hidden Cost
It works—at first.
But over time:
- updates become risky
- changes break other reports
- trust erodes
- teams rely on explanation instead of confidence
The dashboard becomes:
a place where logic lives—but isn’t controlled
Where Logic Should Live Instead
Before dashboards, you need:
1. Defined Events
What actions are captured—and how consistently
2. Structured Data
How those events are organized and modeled
3. Clear Definitions
What metrics actually mean—and how they’re calculated
4. Centralized Logic
Where transformations are applied—and reused
Only then do dashboards have something reliable to display.
What Happens When You Reverse the Order
When structure comes first:
- dashboards become simpler
- metrics align across reports
- logic is reusable
- changes are controlled
Now dashboards answer:
“How do we present what we already trust?”
The Role of the Warehouse
With tools like BigQuery:
- raw data is stored
- transformations can be applied centrally
- definitions can be reused
This is where:
logic becomes durable
Not in dashboards.
Why This Matters More in GA4
With Google Analytics 4:
- data is event-based
- structure is flexible
- definitions are not fixed
If you build dashboards first:
you’re defining your system implicitly—over and over again
The Shift That Matters
From:
- “Let’s build a dashboard”
- “Let’s visualize this data”
To:
- “What does this metric mean?”
- “Where is this defined?”
- “Is this consistent everywhere?”
What Good Looks Like
Dashboards that:
- require minimal explanation
- reflect consistent definitions
- align across teams
- are easy to update
Because:
the thinking already happened upstream
Final Thought
Most teams believe:
dashboards create clarity
In reality:
dashboards expose whether clarity already exists.
If This Feels Familiar
If your dashboards:
- don’t match
- require constant explanation
- break when updated
The issue isn’t your visualization tool.
It’s that:
you started at the end of the system.
Doug McCaffrey
Designs and maintains analytics systems that remain reliable over time.
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