Marketing data integrations promise something powerful
Connect your tools.
Bring everything into one place.
See the full picture.
At first, it works.
Data flows.
Dashboards populate.
Reporting becomes easier.
But over time, something changes.
The integrations still run.
The data still updates.
It just stops meaning what you think it does.
What integrations actually do
Integrations move data from one system to another.
They don’t:
- validate it
- standardize definitions
- resolve inconsistencies
They assume:
the data being passed between systems is already correct
Where things start to break
Integrations don’t usually fail outright.
They drift.
1. Definitions don’t align
Each platform defines metrics differently.
- sessions in one tool ≠ sessions in another
- conversions are counted differently
- attribution models rarely match
When combined, these differences don’t disappear.
They compound.
The result looks unified—
but isn’t consistent.
2. Data is reshaped in transit
Integrations transform data to fit a destination.
- fields are renamed
- values are grouped
- dimensions are dropped
These changes are rarely visible.
But they affect what the data represents.
Over time, the gap between:
source data
and reported data
gets harder to trace.
3. Partial failures go unnoticed
When something breaks, it’s rarely complete.
More often:
- one field stops updating
- one source lags
- one metric becomes inconsistent
The system continues to function.
The data just becomes less reliable.
4. Dependencies increase
Every integration adds another dependency:
- APIs
- authentication
- schema compatibility
As your stack grows, so do the points of failure.
Not all at once.
Independently.
Quietly.
5. Assumptions multiply
Each integration relies on assumptions:
- fields map correctly
- definitions stay stable
- transformations remain valid
At first, they may hold.
Over time, they don’t.
The more integrations you rely on, the harder it is to know what’s still true.
Why this isn’t obvious
Because everything still looks connected.
- dashboards load
- reports send
- data updates
There’s no clear failure point.
Only a growing gap between what the data says and what’s actually happening.
The core issue
Integrations solve for connectivity.
They don’t solve for consistency.
They move data.
They don’t define it.
What reliable systems do differently
Reliable systems define:
- how data is collected
- how it’s structured
- how metrics are calculated
Integrations support the system—
they don’t replace it.
A simple way to think about it
Integrations:
move data between systems
A structured system:
ensures that data means the same thing everywhere
What to watch for
If integrations are introducing risk, you’ll start to see:
- the same metric reported differently across tools
- unexplained discrepancies
- more time spent reconciling data
- less confidence in decisions
These aren’t integration issues.
They’re system issues.
Final thought
Integrations don’t break all at once.
They become unreliable.
And without a system behind them,
they slowly turn clarity into confusion.
Doug McCaffrey
Designs and maintains analytics systems that remain reliable over time.
Explore how this connects across your data estate: