Header Logo
Diagnostic Newsletter Blog Contact
Workshops
Design Principles for Analytics
Log In
← Back to all posts

Data-to-Decision Leakage

Apr 14, 2026
Connect

Most data teams track what they produce.

Number of dashboards.
Number of reports.
Number of requests delivered.

It looks like performance.

It’s not.

I think most organizations have no idea how much value they lose between data and decisions.

Not because of tools.
Not because of skills.

Because of what I’d call data-to-decision leakage.

 

Data is collected.
Dashboards are created
Used once
Understood (sometimes)
Actionable (never)

Somewhere along the way, value disappears.

Silently.

A dashboard is opened.
No decision is made.

A report is delivered.
No action follows.

A request is completed.
The business exports to Excel anyway.

But none of this is measured.

Instead of asking: "How many dashboards did we deliver?"

Ask something much simpler.

Was a decision actually made in the last 3 months because of this dashboard?

If the answer is no, you should be worried.

Because it means:

  • the dashboard is not tied to a decision
  • or the decision happens somewhere else
  • or no decision is needed at all

In all three cases,

The value is not where you think it is.

I believe this is where most data teams get stuck.

They improve outputs. But outputs are not the goal.

What to do instead

Start simple.

For each dashboard, ask:

  • What decision is this supposed to support?
  • Has a decision been made recently because of it?
  • What changed as a result?

Even rough answers are enough.

Because right now, most companies have a leakage problem.

Data goes in. Dashboards come out.

Decisions don’t.

And the more you produce, the more you lose.

If you want to go further, the next step is obvious:

Map where value breaks in your current setup.
Not in theory. In reality.

That’s usually where things get uncomfortable.

And that’s exactly the work I’m currently doing with a small group of companies across Europe and the US.

I’ll be opening this up more broadly in the next newsletter, with a dedicated diagnostic offering.

🇫🇷 En français :

RĂ©servez votre appel 

In English :

 Book a call with me here

Have a great week!

Aurélien

Responses

Join the conversation
t("newsletters.loading")
Loading...
Every analytics product should pass these three tests
*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1"> During a recent workshop, several participants admitted they didn't know how to judge whether an analytics product was "good". Then I wrote three letters on the whiteboard: Useful. Usable. Used. Fr...
Introducing the Analytics Design Office
*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1"> I'm excited to introduce a new concept I've wanted to build for a long time. The subscription model has transformed industries like design. I believe it's time to bring the same approach to analyt...
Why analytics workshops fail before they begin + Webinaire in September
*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1"> One pattern keeps appearing, regardless of the company. A few months ago, I observed exactly the same situation in two completely different organizations. The first one was a large retail company. ...

The Analytics Operating Review

What you’ll get every Tuesday A series of sharp visuals that decode common mistakes in analytics. Fast to read. Easy to apply. Hard to forget.
Footer Logo
Privacy Policy Terms and Conditions
© 2026 Dataviz Clarity