When stakeholders use data... to justify past decisions

Last month in our training,

A data analyst told a story.

It made half the room nod...

and the other half wince.

“The stakeholder said: 'Don’t worry about the accuracy. I just need the numbers to back me up in the meeting."'

The room laughed.

The kind of laugh you let out when something hits too close to home.

This wasn’t the first time I heard it.

And it probably won’t be the last.

It reminded me of this Dilbert comic:

The Hidden Cost of the “Placebo Effect”

This happens more than we like to admit.

We think we’re being consulted to inform decisions.

But often, we’re pulled in after the decision is already made.

We become decorators of the truth.

Tasked with creating beautiful dashboards to support pre-made conclusions.

Or worse: to give the illusion of analytical rigor.

Let’s call it what it is: performative data work.

• The chart that justifies the plan.

• The analysis no one reads.

• The slide that’s used for “optics,” not insight.

The data becomes a placebo—something to make stakeholders feel better, not decide better.

Why People Don't Listen

Before you throw your laptop across the room, here’s the harsh truth:

It’s not always their fault.

Stakeholders often don’t understand what’s possible with data.

But more importantly, we data professionals don’t always communicate in a way that makes our work undeniably useful.

Let’s be honest:

Sometimes...

• We talk like analysts. Not advisors.

• We prioritize detail. Not direction.

• We’re accurate. But not always impactful.

  
  

So when we show up late to the conversation, with a complex dashboard and 17 bullet points of nuance, it’s no wonder they go with their gut instead.

The Influence Upgrade: 3 Shifts

Here’s how to shift from data monkey to trusted business partner.

1. From “Execution” to “Equality”

You know the vibe.

They come in with a request.

You ask a few smart questions.

But they still treat you like a report generator.

Here’s the shift:

Build trust by challenging them—without being a jerk.

“Why do you need this?"

"What decision are you trying to make?”

You’re not being difficult.

You’re showing you care about the why, not just the what.

That’s when they stop seeing you as a tool—and start seeing you as a teammate.

2. From “Explaining” to “Framing”

Stakeholders don’t want a lecture.

They want clarity. Direction.

But many data professionals default to this:

“Channel A had 12% growth. Channel B had 9%. But Channel B has lower acquisition costs, and…”

It’s accurate. But it puts the burden on them to figure out what it means.

Try this instead:

“If we want quick growth, Channel A is better. If we care about cost efficiency, Channel B wins. Since we’re under budget pressure, I’d go with B.”

You’re not hiding the nuance.

You’re guiding the decision.

That’s framing.

3. From “Accuracy” to “Actionability”

Before – Technically solid, but not actionable:

“Conversion rate dropped from 4.1% to 3.3% post-release (–0.8pp, ~30% relative drop, p < 0.01).”

Accurate. Statistically sound.

But… now what?

After – Clear, relevant, and still grounded in data:

“Sign-ups dropped 30% after the redesign. Fewer users are reaching the pricing section, especially on mobile. I recommend checking if the new button placement is causing the drop.”

You’re still analytical.

But now you’re helping the team move forward, not just stare at a number.

That’s actionability.

The Bottom Line

If you feel like your work is just being used to decorate decisions, you’re not alone.

But here’s the good news:

You can change how people see your role.

You can shift from data monkey to trusted business partner.

And it starts with communication, not code.

So ask yourself this:

Are you influencing decisions—or just validating them?

Because the latter might feel comfortable…

But the former is where real impact lives.