I've been doing data analysis for many years, and I've seen too many people with good data end up creating charts that no one wants to look at. The problem isn't the data—it's the presentation.
Step 1: Determine what you want to say. This is the easiest step to skip. Many people simply select the entire Excel table, click "Insert Chart," and end up with a chart they can't even understand themselves. A chart isn't a data container; it's an argument. Each chart should answer only one question.
For example, if you have an e-commerce sales data table containing dates, product names, sales volumes, unit prices, and profits, the questions you might ask include: Which product sells best (bar chart)? How do sales change over time (line chart)? What's the relationship between profit and sales volume (scatter plot)? These three questions require three different charts—don't try to answer all questions in a single chart.
Step 2: Choose the right chart type. Many people get this wrong too. Use bar or horizontal bar charts for size comparisons; horizontal bars work better when there are more than ten items because labels won't be squeezed into vertical columns. Use line charts for trends, with time axes increasing from left to right. Use pie or donut charts for proportions, but keep data items under five or six—switch to stacked bar charts if you have more. Use scatter plots for relationships between two variables. Use histograms or box plots for distributions.
Don't use radar charts casually. I've seen people online use radar charts to compare eight parameters of two phone models, but because the parameter scales differ (price is in the thousands, weight is in the hundreds of grams), the radar chart shows no meaningful information at all.
Step 3: Fix the default styling. Default chart styles in Excel and most tools have several common problems: grid lines are too dark (use light gray or remove them entirely), legend position is wrong (default right-side placement wastes space—move it to top or bottom), too many colors (users can't distinguish more than five colors), and font size is too small (chart fonts should be at least one size larger than body text).
A simple test: Send your chart to someone unfamiliar with the data and ask what they see. If they can explain the chart's message within five seconds, your chart passes. If they need to stare at it for a while or need you to explain it, start over.
DataVizForge is designed around these three steps: first select your data source (URL or Excel file), then choose the chart type, and finally adjust style parameters. The entire process requires no coding.