Need to quickly build a report or dashboard? With modern data visualization tools like Microsoft Power BI, that’s no longer a challenge these days. In fact, with AI, you can generate a first draft in no time. But that’s exactly where the challenge lies. Creating a report is one thing. A report that’s actually used is quite another.

How do you ensure that your report meets the user’s information needs? That insights are immediately clear? That someone can understand what they’re looking at and what action is needed without any explanation?

When developing reports, the same questions keep coming up:

• Does this match what the user wants to know?
• Is the right information in the right place?
• Does visualization help you make decisions faster?

And finally, the most important question: what really makes a report good?

The answer rarely lies in the tool or technology, but in the choices you make. By putting yourself in the user’s shoes and focusing on what is and isn’t relevant, you make a difference.

In this blog post, we’ll walk you through 10 practical principles to help you transform a “pretty” dashboard into an effective one. That way, your next report won’t just be viewed—it’ll be used for its intended purpose: making better decisions.

We'll start with a sample sales report as a starting point.

1. Design within the boundaries of the screen

Too many visualizations on a single page force you to stretch out the report. As a result, the user has to scroll and loses track of the big picture. And that’s exactly what you want to avoid—a good report should be understandable at a glance.

The solution lies in focus and structure. Whenever possible, incorporate visualizations and spread your report across multiple pages. Start with a clear overview page highlighting the key insights, and then move on to more detailed pages.

This way, you keep the report organized and guide the user directly to the right information.

2. Provide context for your data

In the initial report, we see an example of a so-called gauge visualization (the gauge at the top right of the initial report) showing revenue of 8 billion. It is unclear what this figure represents. Does it refer to a figure for the past year, the current year, the current month, or something else entirely? More importantly, what is that figure being compared to—for example, the same period last year or the target for the current year, quarter, etc.? This can be clarified by specifying in the report title that it refers to sales for Q1 of 2026 or in the caption of the visualization.

The amount shown in the card visualization is the total for that period. Below that, in a slightly smaller font, we also see the Q1 target along with a percentage change. That information now has context.  

3. Replace details with high-level information

We see a lot of visualizations and (numerical) details in the initial report. Starting with figures expressed down to the euro cent. One might wonder whether information at that level of detail adds value for the public. The same question applies to the placement of labels on the axes of visualizations. Furthermore, we see tables containing complete lists of countries and products that make a table unnecessarily wide and long. The solution: round numbers to the nearest thousands, millions, or even billions. You can omit the titles on the axes and include them in the title of the visualization. This creates more space for the graphical aspect of the visualization. You can also limit yourself to a top N of countries to reduce the list. Do you still need a lot of detail? Then place it within the hierarchy of a matrix visualization. This creates clarity and insight by reducing the number of characters used (letters and numbers). 

4. Choose the right visualization option

The range of visualizations is growing by the day. And there are already plenty of them. But what do they actually add? While they may provide the solution for highly specialized issues, for most business reports, even just a third of the standard Power BI visualizations will suffice. Column charts, line charts, scatter plots, cards, and other green-colored visualizations are more than sufficient to meet the need in most cases.  

3D visualizations and those where the length of an angle must be “estimated”—such as globe maps, pie charts, donut charts, and gauges (red-colored visualizations)—are best avoided. This is because the human eye is well-suited to perceiving two-dimensional objects: length and width. Depth is estimated, and that is exactly what you want to avoid. In the case of a pie chart, donut chart, or gauge visualization, the user must estimate the proportion of the whole, whereas in a column chart, that proportion can be read more easily. Another reason not to use these visualizations is that they take up a relatively large amount of space on the canvas. They can easily be replaced by visualizations such as column charts, bar charts, or cards. See the example provided in Principle 2. 

5. Always start the Y-axis at 0

When a line graph has a narrow range, people tend to zoom in to make the line pattern more visible. This is done by setting the Y-axis to start at the lowest value in the dataset. This often gives a misleading view of the graph. At that moment, the human eye is focused on the line pattern, so the range of the Y-axis is not perceived, which can lead to incorrect conclusions. First: you may perceive something as a peak, but in reality it is no peak at all or a much smaller one because the graph shows a zoomed-in version of reality. Second: if the axis starts at the lowest value (5 million in our example), sales appear low because they fluctuate around the visual 0-value.  

Starting from 0 allows us to see the actual pattern, but because the range is so narrow, it isn't clearly visible. Is that a problem? No, because if the range is that narrow, you can use the line chart's zoom slider option.  

This way, you can always zoom in on both the x-axis and the y-axis. The downside is that there is an additional visual element—a slider—that draws attention. Carefully consider whether it is really necessary to use this option. Another solution is to place the same visualizations one above the other, with the first starting at 0 and the other starting at the lowest value. 

6. Place each visualization in the correct spot on the canvas

When we look at a screen, we process visual elements in the same way we read a book. In Western writing systems (among others), people read and write from left to right, moving to the next line, and so on. Subconsciously, you process visualizations in the same way, moving from the top left in a Z-pattern to the bottom right. For this reason, this approach is referred to as the Z-order in data visualization. In the solution, we have four visualizations. Their importance starts at the top left; that is, we automatically read the most important information from the top left, starting with the top 10 per country. We then continue reading in the Z-order from more to less important information, or from less to more detail. For this reason, the section containing the slicers and the logo is placed on the right side of the screen (less important). 

7. Limit the number of visualizations per page

Logically, you should use as few visualizations per screen as possible. But how many is too many? You don’t need to put all the information on a single screen. Try not to place more than four visualizations on a single screen (and keep the Z-order in mind). Are there more? Then place the most important ones on the first report page, followed by the second, and so on. Report pages can, of course, also be organized by theme. 

8. Limit the number of pages per report

If a single report contains many pages, there’s a good chance it will run more slowly. Have you perhaps included multiple topics (or target audiences) in a single Power BI report? In that case, it’s better to split the report into multiple Power BI reports. This reduces the size of the datasets, improving the report’s performance. 

9. Use a soft color palette and color references, and use them sparingly

The use of bright and excessive colors can cause the brain to become overstimulated, which can cloud the perception of information. It is best to use a single color palette for the entire organization. This color palette should also be soft and pleasant to the eye. You may also want to consider a color palette designed for people with color blindness. After all, 8% of men and 0.4% of women inherit red-green color blindness.  

Another aspect to keep in mind is color consistency. In other words, if you use blue to represent sales in one visualization, use the same color for sales in other visualizations as well. The human eye automatically makes that connection, and if you apply this consistently, the eye doesn’t have to look far, which creates a sense of calm and clarity. Another aspect is overuse. Consider the example from the opening report; the background is blue. Why? That’s the organization’s color, but it’s not a reason to plot this information (color is also information). It adds no value. Best practice is to use white as the background. Sometimes black is chosen. But in any case, be consistent in this. 

10. Make sure the design is appealing

Last but certainly not least. Our initial report certainly does not address this. An appealing design is partly the result of the proper application of all the topics described earlier. This makes it clearer what information is being displayed, gives the design a more uncluttered look, and makes it easier to find the information.  

By putting these 10 principles into practice, I arrive at the following sales report, which features significant visual improvements.