Why Does UX Matter in Dashboards?

Have you ever opened a dashboard and felt lost, unsure of where to look first? Or needed a quick piece of information, only to find it buried among too many charts and filters that did not work well together?

That is exactly what UX, or User Experience, is about. When we talk about dashboards, UX is not just about making them look more attractive. It is about efficiency, clarity, and supporting better decision-making. UX means ensuring that the people using a dashboard can find what they need in the simplest way possible1. Dashboards are not just collections of charts. They are decision-making tools, and poor user experience can lead to errors, wasted time, and even misguided decisions.

In a world where data is everywhere, having access to it is not enough. We need to turn data into clear and fast insights. That is where thoughtful design becomes essential. Using colors strategically to highlight important information, organizing data in a logical and hierarchical way, and choosing the right chart types for each kind of information are some of the practices that can significantly improve the user experience. In addition, incorporating user feedback can help refine and optimize a dashboard so it better meets their needs.

How Much Data Do We Create Every Day?

When we look at today’s landscape, it is impossible to ignore the enormous amount of data we generate every single day. This chart illustrates clearly: The exponential growth in the volume of data created globally, year after year.

If we look back at 2010, the amount of data being generated still seemed somewhat “manageable.” But notice how, starting around 2015, the curve begins to rise much more sharply.

By 2025, approximately 175 zettabytes of data had been generated, and the trend is expected to continue growing. In fact, that is more than the total amount of data generated from 2010 to 2020 combined2.

What does this mean in practice?

It means that as the volume and complexity of data increase, analyzing information stops being a natural advantage and often starts becoming an obstacle.

In other words, having all this data available is not enough if it is disorganized, confusing, or difficult to interpret. That is exactly where design, user experience, and well-built dashboards become essential.

There is also a very common belief that the more options we have, the better. After all, more choices are often associated with greater freedom, more control, and, as a result, greater satisfaction.

What does this mean in practice?

As the volume and complexity of data continue to grow, analyzing information stops being a natural advantage and often becomes an obstacle.

In other words, it is not enough to have all this data available if it is disorganized, confusing, or difficult to interpret. This is exactly where the importance of design, user experience, and well-built dashboards comes in.

There is also a very common belief that the more options we have, the better. After all, more choices seem to imply more freedom, more control, and, consequently, greater satisfaction.

The Paradox of Choice

There is a very common belief that the more options we have, the better. After all, more choices seem to mean more freedom, more control, and, therefore, greater satisfaction.

Sheena Iyengar of Columbia University and Mark Lepper of Stanford challenged that assumption in their well-known 2000 study, When Choice Is Demotivating: Can One Desire Too Much of a Good Thing? 3

Through a series of experiments conducted across three studies, both in laboratory settings and in real-world environments, they reached an important conclusion: having more options can lead to less action, less satisfaction, and even greater frustration.

To better understand whether more options help or hinder decision-making, the researchers designed a very practical experiment in a supermarket.

They set up a gourmet jam tasting booth that operated for two Saturdays, five hours each day.

In total, approximately 754 shoppers were observed. However, the analysis focused on the people who stopped at the booth to try the jam.

So, how was the experiment conducted?

There were two different scenarios, alternating throughout the day:

• Scenario 1 – Limited choice: shoppers were presented with only 6 jam flavors to sample.
• Scenario 2 – Extensive choice: in this case, the booth displayed a much wider variety, offering 24 jam flavors.

The goal was to observe how people behaved in these two different contexts: Would more options generate more interest? More purchases? Or simply more confusion?

Some might say the results were counterintuitive.

• When 24 jam flavors were available, 60% of shoppers were drawn to the booth. They stopped, looked, and sampled the products.
• When only 6 flavors were available, the level of attraction was lower: 40% of shoppers stopped.

In other words, more options really do attract more attention. They seem more interesting and more inviting.

But when we look at what truly matters action, decision-making, and purchase behavior the results change completely:

• Only 3% of the people who encountered the 24-flavor display bought a jar of jam.
• On the other hand, when there were only 6 options, the purchase rate rose to 30%.

So, people who were given fewer options were far more likely to make a decision and complete a purchase.

The conclusion of this study is clear: having many options may seem more appealing at first, but it can become demotivating when it is time to decide.

Yes! This is the paradox of choice.

That is why we need to reinforce the good old idea that less is more, and that good design is not just about aesthetics, it is about strategy.

Some Data on Dashboard Abandonment

If up to this point, we have been discussing how an excess of information and options can create paralysis, these numbers show the real impact of that problem in the world of dashboards.

The first figure comes from the Luzmo BI Adoption Report, published in March 2025. It reveals an alarming reality: only 20% of dashboards are used frequently, whether daily or weekly. This means that around 80% are abandoned or used very rarely4.

What does this tell us? It shows that there are a huge number of dashboards that simply do not create value, do not support the people who are supposed to use them, and end up being forgotten.

The second figure comes from a study by Yellowfin in partnership with Dresner Advisory, which found that 41.7% of users spend less than one hour per week looking at dashboards5.

In other words, a large share of users interacts with dashboards only superficially. This may point to two problems: either the dashboards are not relevant, or they are so difficult to understand that users prefer not to engage with them.

These numbers make one thing very clear: creating dashboards is not just about putting data on the screen. It is about designing an experience that truly helps people navigate information, interpret data easily, and make fast, confident decisions.

UX and Design Best Practices for Dashboards

That is why UX for Analytics is essential to creating value. Without good user experience, data does not turn into decisions. And that is exactly the problem we will continue exploring from this point forward.

Here, I have gathered some best practices for creating dashboards with the user experience in mind. These include staying focused on the objective, ensuring a clear information hierarchy, using colors correctly, minimizing visual noise, paying attention to alignment and spacing, choosing the right chart type, and making sure the dashboard delivers actionable insights.

It may seem obvious, but it is very common to see dashboards turn into large walls of charts without a defined purpose. And that creates more confusion than clarity.

Focus On The Objective

When we talk about focusing on the objective, we mean answering questions such as: Who is going to use this dashboard? What decision do they need to make? What is the main question that needs to be answered quickly?

For example, a dashboard for the finance team needs to answer different questions than a dashboard for the sales team. It does not make sense to mix everything in the same place.

A good exercise is this: if you had to explain the purpose of your dashboard in one sentence, what would it be? If you cannot answer that clearly, it is a sign that the objective is still not well defined.

Having that focus from the beginning guides every other design decision: what should go at the top, what should be treated as detail, and what can be left out.

Without a clear objective, there is no hierarchy, there is no simplicity, and the user experience will be confusing.

Information Hierarchy

With a clear and well-defined objective, it becomes much easier to organize what matters most.

Think of it as a pyramid: the top represents the most important information, such as KPIs, key metrics, and critical statuses. These are the elements that should appear at the top of the dashboard or receive the greatest visual emphasis.

In the middle, you place the supporting data, the information that helps explain the context behind the main indicator.

At the base, you include complementary details or more granular data for those who want to explore further.

Without hierarchy, everything appears to carry the same weight. And when everything is important, nothing is important. The result is that the user gets lost and cannot tell what should be prioritized.

That is why you should always ask: What does the user need to see first? What is essential for decision-making? What can remain in the background?

Hierarchy is not only about position on the screen. Color, font size, icons, and spacing also help guide the eye toward what matters most.

Correct Use of Colors

It is important to understand that color is information, not decoration7.

You should work with two distinct palettes. One should contain the client’s base brand colors, and the other should be an alert palette ranging from green to red.

🟢 Green: signals something positive, normal, or within expectations, usually meaning OK.

🟡 Yellow: indicates attention, something being monitored, or an early warning level.

🟠 Orange: points to higher risk, an ongoing issue, or a situation where immediate action may be needed. 

🔴Red: represents a critical alert, failure, or a situation that is out of control and requires action.

Another important tip is to maintain consistency. If you use red to indicate a problem, do not use red somewhere else to represent something positive. This kind of consistency helps users quickly interpret what they are seeing.

It is also essential to consider contrast. Light text on a light background becomes difficult to read, just as charts with very similar shades create confusion instead of clarity.

Minimize Visual Noise

Visual noise happens when there are too many elements on the screen that do not help users understand the information, such as heavy gridlines, unnecessary borders, colored backgrounds, shadows, or decorative icons with no real function.

These details compete with the actual data. In the end, the user spends more time trying to figure out what is relevant and what is not.

Every element on the screen should have a clear purpose: guiding the eye, highlighting information, or structuring the reading flow8.

Let’s look at this example here 6: On the left side, each block seems to have the same level of importance, and there is no clear hierarchy. There are also several decorative or repeated elements that do not add value. This creates confusion and makes the user spend more time trying to understand what really matters.

Similar metrics appear in different areas without a clear need. At the bottom, we have a “Revenue and Orders over Time” chart, and right above it, other isolated charts showing the same segmented data, but without a clear connection. This duplicates information without adding analytical value. Customer Satisfaction is placed in the middle of the KPIs, with no clear connection to the performance metrics it influences, such as revenue by category or repeat purchases.

Notice that pieces of information that are part of the same story, such as revenue trend, revenue target, and customer satisfaction, are spread across distant sections. This forces the user to jump from one side to the other, creating unnecessary cognitive effort.

Everything is also crowded together, with little visual breathing room, which makes it harder to distinguish each section clearly.

Now let’s look at the panel on the right, labeled “Better.” This is where we can clearly see how applying UX best practices makes a real difference.

At first glance, we can already notice a clear hierarchy. The most important information is highlighted at the top, such as the main revenue KPI. Supporting metrics appear just below it, organized within the proper context.

The design is cleaner, with fewer unnecessary borders and gridlines, and it uses white space to separate sections. This creates visual breathing room and helps the user understand where each group of information begins and ends.

In addition, related data is grouped logically. For example, revenue trend and revenue target appear side by side, making comparison much easier. The filter panel is also well positioned, allowing users to adjust the analysis without creating confusion.

Colors are used consistently, highlighting only what is relevant and avoiding visual clutter. The layout is simple, but highly functional, because it provides context and guides the user’s attention to what matters most.

Remember: the cleaner the design, the faster the user can understand the data.

Alignment and Spacing

A dashboard with misaligned charts, inconsistent block sizes, or uneven spacing between elements creates a sense of disorder, even when the data itself is correct.

When elements are properly aligned, such as columns, charts, and KPIs, the eye moves through the content naturally and without effort. The brain can understand the logical structure without having to figure out where each section begins or ends.

In addition, since we read from left to right and from top to bottom, it is important to maintain that same logic in the dashboard layout as well.

A good practical tip is to use grids and guides to keep blocks aligned, and to apply consistent margins and padding. Small details like these communicate care and professionalism.

Choosing the Right Chart Type

Each chart type serves a specific purpose. It highlights a particular relationship within the data and helps users understand the main message with minimal effort.

The figure below works as a practical visual guide to help choose the most appropriate chart type based on the objective of the analysis.

In the left column, we see different key ideas that may guide the way we analyze data. For example: Do you want to show a single number? Compare values across groups? Present proportional composition? Visualize changes over time? Or understand the relationship between metrics?

Each of these questions points to a set of recommended chart types. For example, if the goal is to show changes over time, line charts or area charts are usually the best options. To compare values across categories, bar charts or column charts tend to work better. And to show the composition of a whole, pie charts, donut charts, or treemaps can make sense, as long as they are used correctly.

The most important thing to understand is that a chart is not just decoration. It is a bridge between the data and the insight we want to extract. A poorly chosen chart can confuse the user or even distort the interpretation of the data.

That is why this kind of matrix is so helpful. It helps prevent classic mistakes, such as using a pie chart to compare very different values or a line chart to display data with no time progression.

Use it as a checklist: What question am I trying to answer? From there, choose the chart type that makes the most sense for telling that story as clearly as possible.

So now we arrive at the final item in our list of best practices.

Actionable Insight

Think about how a GPS or Waze works: you need to know where you are right now, which is your current result, represented by Outcome Metrics. You also need to understand what is influencing your route, such as traffic, roadwork, or road conditions, which are your Driver Metrics. Most importantly, you need practical guidance on how to adjust your path, which is where Actionable Metrics come in.

Without that, the map is just a drawing. But when these three layers work together, the dashboard stops being a panel of static data and becomes a navigation system for decision-making.

The Outcome Metric shows the starting point and the destination. It represents the view of the final result.

The Driver Metrics are the factors you monitor to understand why you are ahead or behind, just as a GPS shows traffic, roadblocks, or alternative routes.

And the Actionable Metrics are like alerts and voice commands: “Turn left now,” “Take a faster route.” They tell you what to do in practice in order to get where you want to go.

Turning dashboards into data navigation systems is one of the solutions that could help prevent the dashboard abandonment problem mentioned earlier in this article.

Conclusion

A dashboard is much more than a collection of charts placed on a screen. It is a tool designed to help people make sense of information, identify what matters most, and take action with confidence.

In a world where the volume of data continues to grow at an overwhelming pace, the challenge is no longer just accessing information. The real challenge is presenting it in a way that is clear, relevant, and easy to use. Too much data, too many options, and poor organization can create confusion instead of clarity. And when that happens, even the most technically well-built dashboard can fail to deliver value.

That is why UX and design are not secondary concerns in analytics. They are essential. When we apply good practices such as focusing on the objective, creating a clear information hierarchy, using color intentionally, reducing visual noise, aligning elements properly, and choosing the right chart types, we make dashboards more than visually appealing. We make them useful.

A well-designed dashboard helps people understand faster, decide better, and act sooner. And that is ultimately the goal of analytics: not simply to display data, but to turn data into insight, and insight into action.

References:

1. How to Design Better Dashboards [Video]. YouTube.
   https://www.youtube.com/watch?v=s-Il4S0OvzQ
2. Statista. Global data creation growth statistics.
   https://www.statista.com/?srsltid=AfmBOopieefYKjcXZiGL93eDj-b8cYuLMs3Q0zCwj80-m1w11zd50hos
3. Iyengar, S. S., & Lepper, M. R. (2000). When Choice Is Demotivating: Can One Desire Too Much of a Good Thing?
4. Luzmo. Is Data Visualization Dead?
   https://www.luzmo.com/blog/is-data-visualization-dead
5. Yellowfin BI. Need More Than Dashboards to Run a Data-Driven Business.
   https://www.yellowfinbi.com/blog/need-more-than-dashboards-to-run-data-driven-business#:~:text=in%20...
6. Metabase. Top 5 Dashboard Fails.
   https://www.metabase.com/blog/top-5-dashboard-fails
7. Pedrosa, T., & Toutain, L. [PDF document].
   https://cinform-anteriores.ufba.br/vi_anais/docs/TaisPedrosaLidiaToutain.pdf
8. Lucas. Menos é Mais: Pare de “Suar” seu Dashboard no Power BI. Medium, February 4, 2026.
9. Interaction Design Foundation. What Is Visual Hierarchy?
   https://ixdf.org/literature/topics/visual-hierarchy