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For a long time, I had a rule I followed without even realising it.

If I couldn't back something up with perfect numbers, I kept my mouth shut.

In leadership meetings or strategy conversations, if someone asked me directly what I thought we should do and the data wasn't clean, complete, and defensible, I stayed quiet. I told myself this was intellectually honest. That it was what separated data people from everyone else throwing half-baked opinions around the room.

As the saying goes, “In God we trust. All others must bring data.”

What it actually was, was a mistake that cost me influence I didn't even realise I was losing.

While I sat on my hands waiting for perfect numbers, other people filled the silence. They made the calls. They shaped the direction. And I, the person whose entire job was to inform those decisions, contributed precisely nothing to the most important conversations happening in my organisation, simply because I didn’t have the data.

I wasn't being rigorous at all. I was utterly invisible.

If that sounds familiar, then this one is for you.

Data professionals are trained, rightly, to distrust imprecision. Sloppy numbers get people fired. The culture of our craft rewards precision and punishes inaccuracy etc.

That discipline is absolutely valuable in technical work.

But it becomes a huge liability in leadership conversations.

Executives rarely wait for statistical significance before making decisions. They are constantly operating under uncertainty, and what they need from the data leader in the room is not always a perfect number or data set. It is a credible perspective, informed by data, experience, and judgement.

When data leaders conflate "I don't have perfect data" with "I have nothing useful to say", they’re choosing to opt out of the conversations where they should be most present.

Your business stakeholders will start to notice. They’ll stop directing the hard questions your way. They stop giving a sh*t whether or not you have a P.O.V. You get consulted after decisions are made rather than before. Your team remains a reporting function rather than becoming a strategic one.

Your silence can have dramatic consequences.

Most data leaders who default to silence think they’re doing the right thing. They are not. They are making a choice, and that choice has a cost.

1️⃣ You cede the narrative to people with less context. When data leaders stay quiet, someone else will fill the gap. That person is often less informed, but nonetheless more willing to speak with confidence. Leadership will listen to the ones who show up.

2️⃣ Your team gets positioned as a cost centre. This old chestnut. If the only value communication coming from your function is a list of tickets closed and dashboards shipped, that is exactly how you will be evaluated. Activity metrics do not make a case for strategic investment in your function.

3️⃣ You get consulted later and later. If you are known as someone who waits for perfect conditions to contribute, you stop getting invited into early conversations. By the time your data is “perfect”, the decision is already made.

4️⃣ Your influence shrinks. Nobody announces that they have stopped taking the data leader seriously. It happens slowly. Soon you realise you’re being managed around by your peers rather than listened to.

5️⃣ You reinforce the wrong perception of data leadership. Every time a data leader stays silent waiting for perfect numbers, they confirm the belief that data people are technicians, not strategic partners. The opposite of what we want and need from our roles.

The good news is that none of this is permanent. It is a communication habit, and habits can be changed.

Most data leaders need to hear this: quantified ROI is not the only legitimate form of value communication.

Value in a data context can look like:

These aren’t always neatly expressible as a single figure, but are all genuinely valuable. All of them are communicable, if you’re willing to use both tools available to you.

Those two tools are directional metrics and value narratives.

A directional metric is an honest, assumption-transparent estimate. Here’s an example: If your new intake process saved each analyst roughly two hours per week, then it’s fine to say: "We've reclaimed about 400 hours of analytical capacity this quarter. Based on fully loaded team costs, that is roughly £X redirected toward higher value work."

You clearly state the assumption(s) and show the logic. It’s doesn’t have to be perfect so long as your working assumptions are articulated.

A value narrative is a strategic opinion, offered with confidence, grounded in your experience and judgement rather than in observed data. It sounds like: "I don't have numbers to back this up yet, but my instinct is that we're underinvesting in post-purchase experience relative to acquisition. Every organisation I've worked in has made this mistake. I think it's worth deliberately testing whether fixing that moves retention before we commit more budget to the top of the funnel."

Despite the lack of data at hand, you’re still in a position to voice a valid perspective and provide sensible direction for the organisation.

And when you genuinely do not have the numbers yet? Propose the experiment to get them. Rather than apologising for the gap, recommend how you would close it.

That’s what a strategically influential data leader would do.

👉🏼 If any of this resonates with challenges you're facing in your role, I work with data leaders and their teams to help them become more effective, influential, and strategically positioned inside their organisations. Book time with me here to talk through what support would look like for you.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

Last week, I asked you: How do your rate your org's data culture maturity?Here’s how you responded: