Visualizations are a great way to interpret your insights. Right visualizations deliver your insights clearly to your audience while a wrong method of visualization of your data sums all your efforts to zero that you had put up in finding relevant insights out of the data. Whether it's a Data Scientist, an Analyst, a visualizer or anyone who is involved in the visualization of the data, some points must be kept in mind and the person presenting it must avoid some silly mistakes. To make your visualizations better and to help you in avoiding some silly mistakes that are often done in visualization, I have come up here with the top 5 mistakes that beginners and experienced usually commit in Data Visualization. Go through these mistakes carefully and analyze whether you are not committing the same, if it is so, avoids them today and make your visualizations the most appealing.
Here are the 4 Mistakes that are committed in a Data Visualization:
Every data has some unique features in it that are presented at best with only some specific charts. You need to identify the best chart that presents your data well. At times it happens that a particular type of data is presented well in a particular type of chart. Some data is presented well using a pie chart, some using a bar graph. Identify your data and make the right selection in choosing your chart. A right choice of the chart interprets your data clearly which often become hard to interpret with the wrong usage of the chart.
- Excessive usage of colors
Colors are the distinguishing factor in your data visualization. Use the right colors at the right place. Avoid sparkling colors and avoid highlighted colors. At times it happens that the color involved in the data visualization hides the necessary information that you want to interpret with your data. This must not happen. Colors are only meant for making your visualization appealable. Use them in that sense only. Use sober dark and relevant colors to your visual.
Present your insights with simple bullet points in simple usage of language. Often visualizers give a detailed and lengthy explanation of the information involved in the data. This not only makes your audience boring of so much content to read but also makes your visualization dull. Avoid lengthy and complex terms that are hard to interpret by your audience. Also, make sure you put only that piece of information in your visualization which is important for your audience. Avoid ambiguity.
Often while at visualizations silly errors are committed like in using metric abbreviations. In some visualizations, I have personally found that the metric abbreviations are not set to the highest denomination, which is an error. These errors look inappropriate and are misleading in charts. Avoid the errors in your visualization by minutely looking at the data and all scales at the x-axis and y-axis. Note that the correct scaling is done with the correct usage of the abbreviations. Avoid spelling errors as well.
Remember: A majority of the time is always spent in data cleansing, data mining and in finding insights out of it. Data Visualization is the following task making your insights visual to the targeted audience. Do not leave any loopholes in it as they are like the final touch to all your hard work. Do not waste your hard work by creating visualizations which are not appealable to the users and are misleading.
Data Visualization software you must try Tableau, Sisense, Zoho Analytics, Highcharts, etc.