Here's how you can steer clear of common mistakes in Data Visualization projects.

Keep your visualizations clean and simple. That means no clutter; everything should justify its existence in that space. The clearer the visuals that are presented, the better they are understood.

Clarity of presenting data is very important to get the right insights. Clarity can be given by: - Choosing the right Chart - Proper Visual Formatting i.e., colors, font & its size - Proper Data Labels - Avoiding excess data in the chart

Choosing the correct chart type is critical in data visualization. Using the wrong chart can lead to misinterpretation and confusion, potentially misleading your audience. For instance, using a pie chart to show changes over time can be confusing, as pie charts are better suited for displaying parts of a whole. Conversely, a line chart is ideal for illustrating trends over time, providing a clear view of data progression. Bar charts are excellent for comparing quantities across categories, while scatter plots are perfect for highlighting correlations between variables. Selecting the right chart type ensures your data is accurately and effectively communicated.

Poor color choices in data visualization can obscure your message and mislead your audience. Overly bright or clashing colors can make charts hard to read, while too many colors can overwhelm viewers and dilute the focus. A limited color palette ensures clarity and consistency, helping the audience quickly grasp the information. Consistency in color use across charts maintains a professional look and aids in comparison. Additionally, ensure sufficient contrast to highlight key data points and make them stand out. This is crucial for accessibility, as a significant portion of the population is colorblind. Thoughtful color use enhances comprehension and impact.

No of Colors in a visual should be very minimal & appealing to gather insights. Common Mistakes in choosing color are: - Too many colors in a visual - Misleading colors (Red - Good & Green - Bad) - Different colors for gradually increasing data - Colors should not be to contrast - Highlighting colors for irrelevant data

Data Integrity should be maintained which sets the Data Visualization in a right way. - Interpretation of the data is very important to make a proper data visualization. - Accuracy of the data should be maintained by proper data validations & choosing the right type of data. - Checking the formulas for DAX & Calculated Columns to make the calculations are right.

Focus on the data visualizations can be improved by - Mapping the rough format for visuals - Arranging the charts in sequential flow - Avoiding too much data/charts

I've made mistakes in visualization projects and learned from both my own experiences and others, some important once are : Choosing he Right Chart: Instead of adding different charts to make your visuals look better, choose charts based on what you want to convey. Opt for the simplest charts whenever possible. Avoid Information Overload: Don't add too much information at once. Break it down based on hierarchy or other factors to make it more manageable. Maintain Structural Flow: Don't just add charts to accommodate all the data. Ensure there’s a structural flow that helps users grasp the information easily and make proper decisions.