Stop apologizing for the data

How analysts get trapped in the cycle of telling stakeholders what they want to hear

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Read time: 2 minutes

As a data analyst, your job is to uncover the truth.

But sometimes, that truth makes people uncomfortable.

You run an analysis, and the numbers don’t look great. Maybe a strategy isn’t working, a new initiative isn’t delivering results or the insights are not yielding any trends.. You present the data, and instead of acceptance, you get resistance.

Can you check again?
Are you sure this is right?
Let’s cut the data another way?

At first, these seem like reasonable requests. Of course, you want to be thorough. But after a while, you realize what’s actually happening:

They don’t like the answer, so they keep pushing until they get a different one.

And if you are not careful, you start feeling responsible for their disappointment.

You apologize when the data doesn’t tell the story they want to hear.

You scramble to find a better-looking number.

You start questioning your own work, not because it’s wrong, but because the reaction makes you feel like it is.

That’s how analysts fall into the trap of shaping data to make people happy.

Why this is a problem

1. It’s a waste of time. If the financials don’t support the strategy, slicing the data a hundred different ways won’t change that.

2. It creates a culture of bad decisions. If leadership only hears what they want to hear, they won’t make the right calls.

3. It kills morale. Analysts become frustrated when their work isn’t taken at face value. They stop feeling like problem-solvers and start feeling like spin doctors.

How to handle it instead

🚫 Giving in to pressure

takeholder: "Can you tweak the numbers so it looks more in line with expectations?"
Analyst (You): “The data is what it is. If we’re not seeing the expected results, we should focus on understanding why.”

✔ Why this works: You reinforce that the data is factual, not something to be adjusted based on preference.

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🚫 Endless rework with no new insight

Stakeholder: “We need to keep slicing the data. There must be something we’re missing.”
Analyst(You): “Okay, I’ll keep looking.”

❌ What’s wrong? You’re wasting time on analysis that won’t change the reality of the situation.

✅ Pushing for action instead of more analysis

Stakeholder: “We need to keep slicing the data. There must be something we’re missing.”
Analyst(You): “We’ve analyzed multiple angles, and the trend is clear. Instead of more slicing, let’s discuss what actions we can take based on this.”

✔ Why this works: You set a boundary and redirect the conversation toward decision-making.

What to do instead

Stand by your analysis. If you’ve done your due diligence, trust your work.

Be clear and direct. Instead of saying, “Unfortunately, the data doesn’t show X,” say, “The data shows Y, which suggests we need to rethink X.”

Shift the conversation. Instead of chasing numbers that don’t exist, ask: “What action can we take given this reality?”

The goal of data isn’t to make people feel good. It’s to make people see clearly.

You don’t need to apologize for the truth. You just need to present it.

Have you ever been pressured to “find a better number”? comment below —I would love to hear your experience.

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