Excel Bar Charts: Make Them Look Great

Key insights

• Summary: I summarize a YouTube tutorial showing how to make Excel charts look modern and readable; this is a neutral recap and not the original author’s work.
  
• Key features: Excel 2025 now simplifies creating bar charts, offers clearer formatting, and updates charts automatically when source data changes for smoother reporting.
  
• Quick steps: select your data, use the Insert tab to pick a style (clustered, stacked, horizontal), and use Switch Row/Column if orientation looks wrong.
  
• Design tips: sort bars to show rank, remove clutter by hiding unnecessary gridlines and legends, and add data labels on bars for quick value reading.
  
• Formatting tools: use the Chart Design and Format contextual tabs or right-click menus to change colors, thickness, and label placement for clearer visuals.
  
• Advanced handling: format multiple data series as clustered bars, use PivotTable integration for large datasets, and apply practical fixes from the video like thicker bars and “don’t resize with cells” for stable layout.
  

Video overview: what David Benaim demonstrates

In a recent YouTube video, David Benaim walks viewers through practical steps to make Microsoft Excel bar charts look more modern and readable. He argues that Excel's default chart styling is dated and then shows specific changes that transform basic charts into clearer visuals. The video follows a short, focused sequence of edits that include sorting data, removing clutter, adding data labels, thickening bars, and highlighting a single bar.

Along the way, Benaim includes short timestamps so viewers can jump to each step, which makes the tutorial easy to follow. He also points out options such as switching between horizontal and vertical bars and adjusting whether a chart resizes with worksheet cells. Overall, the video targets practical improvements rather than advanced features, making it suitable for users who want fast gains in chart quality.

Step-by-step improvements shown

First, Benaim shows how to create a basic bar or column chart from selected data and then suggests sorting the values so the chart reads more logically. He demonstrates the quick wins of removing non-essential elements such as gridlines and redundant axis ticks, which immediately reduces visual noise. Then he adds data labels directly on the bars to communicate numbers clearly without forcing the viewer to read the axis.

Next, the video covers aesthetic tweaks like making the bars thicker and choosing a single bar to highlight by changing its color or opacity. Benaim also explains how to stop charts from resizing with worksheet cells, which preserves layout when users insert rows or adjust column widths. These steps are practical and low-risk, letting users upgrade a chart’s appearance in a few clicks without rebuilding the underlying data.

Finally, he contrasts horizontal bar charts with vertical column charts, noting when each orientation works best for readability and label placement. Throughout, he uses clear examples so viewers can replicate each change on their own workbooks. The result is a set of repeatable patterns that suit reports and slide decks alike.

Design choices and readability tradeoffs

Benaim emphasizes simplicity, but these changes introduce tradeoffs that users should consider. For example, removing axes can make charts cleaner, yet doing so relies on data labels to convey exact values; this works well for small datasets but can crowd the view when labels overlap. Therefore, users must balance visual cleanliness with the need to display precise numbers, especially for dense charts.

Similarly, sorting values improves trend recognition, yet it may hide the original categorical order that carries meaning for some audiences. On the other hand, thicker bars improve legibility and emphasize differences, while very thick bars can reduce white space and hide fine-grained comparisons. Consequently, designers must judge whether the improved visual weight helps or harms interpretation in each case.

Challenges with multiple series and dynamic data

When charts include several data series, clutter quickly becomes a bigger issue than it is for a single-series chart, as Benaim's simple edits do not fully address overlap or legend complexity. In those situations, choosing clustered versus stacked layouts, adjusting series gap width, or using small multiples are stronger options but they require more design decisions. Moreover, adding many data labels can create label collisions that reduce clarity rather than improve it.

Another challenge arises when data updates dynamically, for example via PivotTables or linked ranges. Some formatting choices, like highlighting a specific bar by color, can break when the highlighted item moves. Likewise, preventing charts from resizing with cells improves layout stability but can cause misalignment when the worksheet structure changes. Therefore, users need to test formatting choices with representative datasets and refresh cycles.

Finally, there is a time-versus-consistency tradeoff: manual tweaks give precise control but take effort to apply across many charts, while themes and templates offer consistency but may not match every chart’s needs. Balancing manual edits and reusable styles will determine how maintainable a charting approach becomes over time.

Practical takeaway for Excel users

Benaim’s video offers practical, low-barrier changes that improve most basic charts: sort the data, remove unnecessary axes and gridlines, add clear data labels, thicken bars, and highlight key elements. These edits improve immediate readability and presentation value without demanding advanced Excel skills. For many users, adopting these patterns will make routine reports and slides more effective.

However, the video also underscores the need for judgement when facing complex or dynamic datasets. Users should test edits on live data and consider templates or conditional formatting for scalable workflows. In short, Benaim delivers a concise, actionable guide that both beginners and experienced users can adapt according to their reporting needs.

Keywords

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