8 Ways to Improve Your Data Visualizations

Conditional formatting provides quick indicators of data skewness based on predefined boundaries, such as highlighting values above 65% as proficient, between 55% and 65% as fair, and below 55% as poor, allowing rapid assessment of quota performance.

Adding trend lines helps display data trends or moving averages in charts, offering a simple yet powerful tool to determine dataset trends and define action thresholds, with various trend line types available depending on the data.

Rule‑based filtering enables quick filters on axes or data, for example limiting sales views to the last 30 or 7 days to distinguish high‑ and low‑sales days, or filtering top‑performing sales representatives to evaluate consistent performance.

Adding hierarchies lets users start with a high‑level view and drill down into details, such as showing total sales per representative and then breaking it down by product, eliminating the need for extra visualizations.

Sorting data in ascending or descending order, especially when not date‑based, intuitively reveals the story—for instance identifying '完整' as the best‑selling product and '企业' as the poorest performer.

Formatting data improves visual appeal and readability by adjusting decimal places, thousand separators, and display formats (number, currency, percentage, large numbers) for gauges and charts like bar and column graphs.

Including comparisons, such as year‑over‑year data or budget versus actual, adds insight and enriches visualizations.

Chart titles should be simple and directly related to the underlying chart, supporting the story the data tells.

Following these data visualization best practices helps present important data more effectively, leveraging the tools at hand to make informed edits and choices.