Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions — Smashing Magazine
16 min readData Visualization,
UX,
DesignShare on Twitter, LinkedInAbout The AuthorMeriem Benhabiles is a Designer specialising in User Experience, with over five years working in-house, agency-side, and independently. Her background in …
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Live UX Training — Smart Interface Design Patterns with Vitaly FriedmanData visualisation sits at the intersection of two disciplines that rarely talk to each other: data and design. Many dashboards are technically correct and communicatively inert. They show the right numbers but still fail to produce a decision, a direction, or a change in thinking. Meriem Benhabiles explores what changes when you bring structured UX thinking to dashboards and data presentations, from the questions you ask before opening any tool to the decisions that determine whether an insight actually lands.In organisations today, data has never been more available. Dashboards and performance decks exist for almost every function — sales, product, marketing, operations — and the tools to build them have never been more accessible. And yet, in weekly standups and quarterly reviews, the same thing happens constantly: someone shares the numbers, the room nods, and the meeting ends without a decision or clear direction.When that happens, the data usually takes the blame. The numbers weren’t granular enough, the dataset wasn’t complete, we need more information before we can act. But the data is almost never the problem. The reality is that nobody designed it to deliver insights. The chart was built from what was available, not from the question that needed answering. The audience was assumed rather than understood, and what should actually change as a result of seeing this data — that question — was never asked at all.Data visualisation and UX are solving the same underlying problem: both are trying to move the right information to the right person in a way that changes something. The vocabulary is different, but the underlying challenge is identical, and the moment you start treating them as complementary disciplines is the moment dashboards stop being a passive collection of charts and start doing something functional.For designers who work with data, analysts who present to non-technical audiences, and marketers who need their numbers to do more than sit in a slide, this read is for you.The Chart Was Never The Whole StoryIn 1973, the statistician Francis Anscombe (PDF) published a paper that made a quiet but clarifying point. He constructed four datasets that are statistically identical: same mean, same variance, same correlation coefficient, and same regression line. Run the numbers on any of them, and they are identical. Plot them, and they could not be more different.Anscombe’s lesson to statisticians was about diagnosis: visualisation reveals the operational truth that raw numbers conceal.Anscombe’s Quartet: four datasets with identical summary statistics (mean, variance, correlation coefficient) that produce four completely different scatterplots. (Large preview)But visualisation isn’t just diagnostic; it is also communicative. The form you choose is where understanding either emerges or gets lost in the noise. Does your audience walk away with numbers, or with a story they’ll quote and talk about?One of the most striking examples is Visual Capitalist’s History of Pandemics. Instead of burying the reader in a massive data table of casualty counts, it maps the death toll of major historical pandemics using a proportional bubble layout on a single timeline.Visual Capitalist’s History of Pandemics: a proportional bubble chart mapping the death toll of every major pandemic across history on a single timeline. The scale of the Black Death against everything else is readable before a single label is processed. (Large preview)Before your brain reads a single number, your visual system grasps the sheer scale of the Black Death relative to everything else on the page. The right visualisation does not just plot the data but makes the story impossible to miss.Edward Tufte codified a foundational principle for the craft with his data-ink ratio: every mark on a chart should serve the data, not decorate it. It remains a widely used framework in data visualisation, anchored in the assumption that clarity and visual hygiene are the goal.For a chart in isolation, that holds. But a chart is never read in isolation: it’s read by a person, in a specific context, under specific pressure. Strip a chart down to its cleanest form, and you might be removing the exact layer of context a decision-maker needs. Simplicity isn’t the goal in itself; appropriate complexity is.Data is a message, and the right amount of signal depends entirely on who’s receiving it.“Which leads to the core principle of data UX: roughly 80% of the work that determines whether a dashboard succeeds happens before you ever draw a chart.That dependency is the thread the rest of this piece pulls on.The 80% That Happens Before The ChartThe high-leverage 80% almost never happens on screen. It happens upstream: before a tool is opened, before a dataset is pulled, before a single design choice is made. It comes down to three questions, and once they become habitual, they change what you notice, what you ask, and what you push back on at the outset of every project.Context: What are we trying to show with this data?This is where you define what the visualisations actually need to serve before you touch any raw data. Writing down the precise operational questions, specifically enough to determine what gets pulled and what gets filtered, is what produces a dashboard that helps with decision-making.Audience: Who is this for, and how do they think?This is the empathy step. Knowing who’s in the room, wh
UX,
DesignShare on Twitter, LinkedInAbout The AuthorMeriem Benhabiles is a Designer specialising in User Experience, with over five years working in-house, agency-side, and independently. Her background in …
More about
Meriem ↬Email NewsletterWeekly tips on front-end & UX.Trusted by 182,000+ folks.
SmashingConf Freiburg 2026
Celebrating 10 million developers
How To Measure UX and Design Impact, 8h video + UX training
Advertise on Smashing Magazine
Live UX Training — Smart Interface Design Patterns with Vitaly FriedmanData visualisation sits at the intersection of two disciplines that rarely talk to each other: data and design. Many dashboards are technically correct and communicatively inert. They show the right numbers but still fail to produce a decision, a direction, or a change in thinking. Meriem Benhabiles explores what changes when you bring structured UX thinking to dashboards and data presentations, from the questions you ask before opening any tool to the decisions that determine whether an insight actually lands.In organisations today, data has never been more available. Dashboards and performance decks exist for almost every function — sales, product, marketing, operations — and the tools to build them have never been more accessible. And yet, in weekly standups and quarterly reviews, the same thing happens constantly: someone shares the numbers, the room nods, and the meeting ends without a decision or clear direction.When that happens, the data usually takes the blame. The numbers weren’t granular enough, the dataset wasn’t complete, we need more information before we can act. But the data is almost never the problem. The reality is that nobody designed it to deliver insights. The chart was built from what was available, not from the question that needed answering. The audience was assumed rather than understood, and what should actually change as a result of seeing this data — that question — was never asked at all.Data visualisation and UX are solving the same underlying problem: both are trying to move the right information to the right person in a way that changes something. The vocabulary is different, but the underlying challenge is identical, and the moment you start treating them as complementary disciplines is the moment dashboards stop being a passive collection of charts and start doing something functional.For designers who work with data, analysts who present to non-technical audiences, and marketers who need their numbers to do more than sit in a slide, this read is for you.The Chart Was Never The Whole StoryIn 1973, the statistician Francis Anscombe (PDF) published a paper that made a quiet but clarifying point. He constructed four datasets that are statistically identical: same mean, same variance, same correlation coefficient, and same regression line. Run the numbers on any of them, and they are identical. Plot them, and they could not be more different.Anscombe’s lesson to statisticians was about diagnosis: visualisation reveals the operational truth that raw numbers conceal.Anscombe’s Quartet: four datasets with identical summary statistics (mean, variance, correlation coefficient) that produce four completely different scatterplots. (Large preview)But visualisation isn’t just diagnostic; it is also communicative. The form you choose is where understanding either emerges or gets lost in the noise. Does your audience walk away with numbers, or with a story they’ll quote and talk about?One of the most striking examples is Visual Capitalist’s History of Pandemics. Instead of burying the reader in a massive data table of casualty counts, it maps the death toll of major historical pandemics using a proportional bubble layout on a single timeline.Visual Capitalist’s History of Pandemics: a proportional bubble chart mapping the death toll of every major pandemic across history on a single timeline. The scale of the Black Death against everything else is readable before a single label is processed. (Large preview)Before your brain reads a single number, your visual system grasps the sheer scale of the Black Death relative to everything else on the page. The right visualisation does not just plot the data but makes the story impossible to miss.Edward Tufte codified a foundational principle for the craft with his data-ink ratio: every mark on a chart should serve the data, not decorate it. It remains a widely used framework in data visualisation, anchored in the assumption that clarity and visual hygiene are the goal.For a chart in isolation, that holds. But a chart is never read in isolation: it’s read by a person, in a specific context, under specific pressure. Strip a chart down to its cleanest form, and you might be removing the exact layer of context a decision-maker needs. Simplicity isn’t the goal in itself; appropriate complexity is.Data is a message, and the right amount of signal depends entirely on who’s receiving it.“Which leads to the core principle of data UX: roughly 80% of the work that determines whether a dashboard succeeds happens before you ever draw a chart.That dependency is the thread the rest of this piece pulls on.The 80% That Happens Before The ChartThe high-leverage 80% almost never happens on screen. It happens upstream: before a tool is opened, before a dataset is pulled, before a single design choice is made. It comes down to three questions, and once they become habitual, they change what you notice, what you ask, and what you push back on at the outset of every project.Context: What are we trying to show with this data?This is where you define what the visualisations actually need to serve before you touch any raw data. Writing down the precise operational questions, specifically enough to determine what gets pulled and what gets filtered, is what produces a dashboard that helps with decision-making.Audience: Who is this for, and how do they think?This is the empathy step. Knowing who’s in the room, wh
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