Picture a spreadsheet. Hundreds of rows. Emissions data, supplier records, compliance deadlines. A sustainability analyst opens it every morning and has to find something specific — a missing disclosure, a number that doesn’t add up, a supplier who hasn’t responded in six weeks.
Now picture someone redesigning that spreadsheet into a “modern dashboard.” Cards instead of rows. Summary tiles at the top. Progressive disclosure so you don’t see the detail until you click for it. Cleaner. More approachable. Slower to actually use.
This is the readability trap — and it’s one of the most common ways enterprise software quietly fails the people who depend on it most.
The UX industry has a well-earned obsession with reducing cognitive load. The research is real: unnecessary complexity tires users out, increases errors, drives people away. George Miller’s foundational 1956 paper established that working memory can hold only around seven items at once — a finding that has shaped how designers think about information density for decades. The problem is the word unnecessary.
In a consumer app, most complexity is unnecessary. Users are doing one thing, occasionally, with low stakes attached to mistakes. Simplify aggressively and things get measurably better.
In professional analytical tools, complexity is often load-bearing. It’s there because the domain is complex, the decisions are consequential, and the users are experts who need the data — not a curated summary of it. When you remove that complexity in the name of good UX, you don’t reduce their cognitive load. You relocate it. Now they have to click through three screens to reconstruct the picture that one well-designed table would have shown them at a glance.
This is what researchers call the “information scent” problem: when the cues that help users navigate toward the data they need are hidden behind progressive disclosure and summary cards, expert users spend more time hunting and less time working. The interface that looks simpler is actually doing more cognitive work to the person using it.
When I was working on Scaler — an ESG analytics platform where users spend the majority of their time in dense, multi-dimensional data views — I ran evaluation sessions comparing different density configurations on realistic datasets. The kind of data users actually work with, not the sanitized version.
This finding is consistent with research on expert performance in data-intensive domains. Reducing visible data in the name of simplicity increased task completion time and error rates for expert users, even when novice users benefited from the same change. Experts and novices are not the same user. Designing for one often actively harms the other.
The instinct to simplify is not wrong. It just needs a harder question attached to it: is the complexity I’m removing noise, or signal?
Alberto Cairo frames this well in The Truthful Art(2016): the goal of a well-designed data display is not to simplify reality but to make complexity navigable. There is a difference between visual noise — decoration, redundancy, unnecessary variation — and substantive complexity that reflects the actual structure of the domain. The designer’s job is to know which is which, and to be honest about the difference when stakeholders push for a cleaner screen.
The difference between simple and legible is the whole game. Simple means fewer elements. Legible means the right elements, arranged so the user can find what they need, understand what it means, and act. For expert analytical work, those two things point in opposite directions.
Design for the task, not the screenshot.
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