Data Visualization Best Practices

As a Business Intelligence analyst, creating visualizations is crucial for gaining insights into our data. Data visualization is the way we communicate numerical or countable information, using charts, graphs, maps, and sometimes icons. However, it’s essential to adhere to the principle: the goal of visualization is the best insight into data, not just creating pretty pictures. This means that data transformations we make should not be just for display on the chart but to create something clear and understandable for our audience. Here are some good practices to consider.

Know Your Audience

Understanding your audience is the first thing you should do. Who are your recipients, and what questions do they need answers to? Is it the company’s management, operations manager, or perhaps the client? Let’s assume your audience is the management. They most likely want to get acquainted with the data quickly and efficiently. This means they will look at your report very briefly, and data interpretation should be self-evident. Therefore, choose your main presentation points carefully. Knowing the questions your audience expects answers to makes it much easier to adjust the appearance of your report. To facilitate this task, you can ask an external person (e.g., a team member) for a quick interpretation of the report you prepared.

Know Your Data

Knowing the data you present is crucial. You should be familiar with the data source and be aware of any processing that has been done. Of course, the best way to do this is to do it yourself. However, when you don’t have this opportunity, it’s worth getting acquainted with the data model. This will also help you understand what visualizations to use. Here are some types of relationships that we can present in specific visualizations:

  • Fragment of the whole – tree map
  • Comparison – column chart
  • Structure – stacked column chart
  • Correlation – scatter plot
  • Distribution – histograms or box plots
  • Geographical data – maps
  • Ranking – bar chart
  • Trend – line or stacked chart

Highlight Important Points to the Audience

Always think about how to present data so that the audience can understand it as quickly as possible. Using so-called pre-attentive attributes can help. Pre-attentive attributes involve using color, size, shape, and placement of our key data in a way that groups or distinguishes elements. A good example is the use of colors. They can help indicate data that stands out from the rest. You can use contrasting colors, for example, marking outstanding points with color, while the rest remains gray. In Power BI, you can do this using functional colors (more on this soon) or conditional formatting. By using these techniques, you can make it easier for recipients to understand the data you present.

Less is More

Try to choose the right number of visualizations. Too many different visualizations on one card will generate noise and a feeling of chaos. A good way is also to remove titles and axis descriptions where possible. If you can’t remove them, try to make them light and transparent. Also, try not to use too many colors. Don’t worry if there’s a bit of free space on your card, and don’t use borders. Try not to use 3D effects either (unless you’re creating a three-dimensional chart). Three-dimensional charts often give a distorted perspective.

Be Precise and Neutral in Presenting Your Data

Approach data presentation carefully because the most common problem is misinterpretation. Visualizations should clearly and accurately indicate patterns and trends. Our beliefs or prejudices about the presented topic can distort users’ interpretation, and we must avoid them at all costs! To do this, you can use three things:

  • Language – make sure your recipients use the terminology you use when creating visualizations, such as the names of presented KPIs.
  • Labels – make sure you present data in a clear way and don’t generate too much noise.
  • Scaling – significantly affects your audience’s perception. Make sure numerical axes start from zero. Also, pay attention to the scale in the case of percentage changes.


Using the right visualizations allows your audience to understand data more easily and find trends. Data visualization is a crucial skill that involves efficient communication through data, making it easy to receive and interpret. I hope the above tips will improve the quality of your reports.

Bonus: Under this link, you will find misleading charts. If you want to know what NOT to do, take a look.

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