A Career Lesson: Sometimes Instructions Come with Subtext

Early in my data career, there was one phrase I heard repeatedly: “Make it look good.” (and many other variations of the same thing).

At the time, I thought I understood exactly what it meant - deliver clean analysis, make sure charts were readable, provide clear explanations, and include enough supporting detail in the appendix so anyone reviewing the work could follow the numbers and understand the logic behind them. In other words, I interpreted “make it look good” as a request for clarity, precision, and completeness. And that’s exactly what I focused on.

For every piece of work I delivered, I made sure the analysis was solid, visuals were clean, and the explanations were structured clearly enough that there was little room for confusion. From both a technical and communication standpoint, I knew my work was strong.

Yet something always lingered in the back of my mind. Despite the effort I was putting in, why did my manager and leadership usually respond so neutrally? The work would get used, but there was rarely any recognition or excitement around it. At some point, I made peace with that and assumed it was simply how things worked in analytics. If the work was accurate and reliable, it just quietly moved forward without much fanfare.

As time went on and I became more comfortable in my role, my relationships with managers also evolved. And as conversations became more open, I magically began to see the notorious phrase “make it look good” from a very different angle. Turns out, it didn’t always mean what I initially thought it meant.

More often than not, there was a layer of subtext behind it. What they sometimes meant was closer to something like this:

• "Present the findings in a way that supports the direction the team is trying to move in."

• "Frame the insights so they don’t trigger unnecessary pushback."

• "And make sure the message lands well with everyone in the room."

In other words, the instruction was not just about technical clarity; it was also about positioning, framing, and influence. And if I’m being honest, the deeper subtext was often something like: “just make sure we don’t look bad in the meeting” (corporate politics - a different story altogether!)

Once that realization sank in, I started approaching data communication very differently. For years, I had been focusing on visible parts of the work: the numbers, the visuals, and the explanations. But a significant portion of the real impact was happening between the lines, where persuasion, narrative, and decision-making were quietly shaping how the work was received.

The interesting part is, I had to figure most of it out all on my own. No one ever explained this directly.

That’s another reality people rarely talk about - managers almost never communicate expectations in perfectly explicit terms. They often assume you’ll pick up the context behind their requests automatically. It’s not intentional; it’s just how professional communication tends to work in many organizations. A lot of the meaning sits in tone, context, and subtle phrasing rather than direct instructions.

This is where I learned one of the most valuable lessons of my career: When instructions are vague, the best response isn’t guessing. It’s conversation.

But that conversation doesn’t need to be confrontational or uncomfortable. You can be simple and thoughtful. Instead of taking a phrase like “make it look good” at face value, you can ask questions that help you understand the broader context. For example:

• What decision is this analysis meant to support?

• Who is the main audience for this presentation?

• What concerns might they raise when they see these numbers?

• Is there a particular message we want the audience to take away?

You might worry that asking too many questions might come across as pushback, but questions like these don’t challenge authority. In fact, they show that you’re thinking beyond simply completing the task and are trying to understand the strategic intent behind the work. And that understanding can completely change how you present your analysis.

Final Words….

It took me quite a few years and working closely with management to realize that success in a data role isn’t measured only by whether the numbers are correct. It’s also measured by whether the message lands, whether stakeholders understand it, and whether it moves decisions forward (ideally in the right direction).

Accuracy is obviously essential, but communication is what ultimately determines whether that accuracy has any real influence. So, if you are early in your analytics career, let me give you a few tips to spare you from making the mistakes that I did:

👉Don’t just focus on what you’re being asked to do. Try to understand why it’s being asked in the first place.

👉Sometimes a simple (and passive) instruction like “make it look good” carries far more meaning than it appears on the surface.

👉And the only way to uncover that meaning is by having better conversations with the people you work with.