Hello there data enthusiast. Today I'm back with Mistakes beginners make when creating dashboards using Power BI. I hope you stick till the end because I won’t waste your time. With that said, let's get fraudy, shall we:
When creating dashboards, it is very important that you do not mix up yourself by coloring too much and forgetting the main goal of BI tools which is to show trends, patterns, and insights in a way that is easily understandable. Today I want you to learn from the mistakes I made when creating a real estate dashboard so that you won’t have to make the same mistakes again. For that reason, I would like you to pause for a minute and look at the following visual and spot out its mistakes before you dive all in.
Mistakes Beginners Make When Creating Dashboards Using Power BI
1: Overuse of Colors Without Hierarchy
Power BI makes it easy to go wild with colors. But in the dashboard shown above, we see bold red, multiple greens, and highlighted blocks all competing for attention. Color should guide the eye, not confuse it. Use color sparingly—assign it to draw attention to critical insights only, not every card. Remember the goal is to turn raw data into meaningful charts and graphs that give actionable insights and not to showcase your graphic design abilities.
2: Overcrowding and Visual Clutter
The dashboard tries to cram a lot of information into a single screen. There are many visuals, and some of them feel squeezed. This makes it hard for the user to focus on the most important insights. By wanting to show everything you end up having cluttered, overwhelming dashboards. A key principle of dashboard design is less is more. For example, the tile (was intended to be a slicer) labeled “Locations” displays six regions in huge blocks, but adds no visual value. This can be replaced with a complete unfiltered slicer or drop-down filter to allow interaction without crowding the canvas. The number of cards, along with the two bar charts and scatter plot, completely confuse the audience. It's not immediately clear what the most important information is. The dashboard lacks a strong visual hierarchy to guide the user's eye.
3: Inconsistent Number Formatting
Check the value cards:
- “Max Price: $350M” vs. “Min Price: $6.5M” — USD.
- “Total Revenue: Ksh9.3bn” and “Average Revenue: Ksh65.30M” — KES.
Mixing currencies without proper labels or context confuses your audience. Imagine presenting such an inconsistent dashboard report to your stakeholder (let's not even go there). Always maintain a consistent currency or provide clear legends and conversion rates.
4: Inconsistent and Inappropriate Visual Choices
Some of the visual choices don't seem optimal. Don't just pick visuals because they look fancy, but because they effectively communicate the data. For example a line chart is not the best visual for representing the price by bedrooms. Line charts are used to show trends over time. In this case the best fit would have been a bar chart. Also the bar charts, while not terrible, might not be the most effective way to compare price by location. The location should be used as with a slicer.
Again, the “Average Price” visual using a gauge chart— is commonly discouraged unless you're showing progress toward a goal. In this context, the average value is better suited for a card or bar comparison.
6: Overloading Summary Cards and Scatter Plots
Six KPI cards are presented upfront—Max Price, Min Price, Total Houses, Revenue, Average Revenue, and Missing Values. It’s a cognitive overload. Group related metrics together and only highlight the 3–4 most actionable ones.
Again, the “Price Bedroom Scatter” chart lacks axis titles and has dense plotting. While scatter plots can reveal patterns, tooltips and filters are essential for large datasets. Consider applying zoom or grouping logic.
How it should be
Thank you for sticking around to the end. Now that we have highlighted the mistakes, let's have a look at the corrected version of the dashboard report.
Wrap-up
Dashboards aren’t just about showing data—they’re about communicating insight.
As you build your BI skills, remember: clarity, consistency, and usability > decoration.
What did I miss? Feel free to drop a comment (Critics are highly appreciated by the way).
Until next time, stay data-driven and don’t get fraudy! (just kidding)
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