The Data Visualization Design Process: A Step-By-Step Guide

Ai nuclear energy background, future innovation of disruptive technology Free Photo

When it comes to planning a data analytics project, we frequently find ourselves wondering where to start. There is a lot that needs to be done in order to draw an insight that is useful and valuable for the organization, from data gathering, cleaning, exploration, analysis, and visualization. 

One of the most effective ways for bringing data out of spreadsheets and into real-world interactions is to visualize it in charts, graphs, dashboards, and infographics. However, getting started with data visualization can be daunting. Do you ever have the impression that the statistics are about to fall over on you when you're looking at data science jobs? If that's the case, this step-by-step tutorial on data visualization is for you!

We've put together a 5-step strategy for anyone who wishes to use tables, graphs, charts, and diagrams to clearly communicate an observation or explain an analysis, keeping in mind that good visualization is a process that requires iteration.

A 5 Step Guide to Data Visualization Design Process

Step 1: Analyze Your Audience

Wait! Don't get your computer out and start drawing graphs! To begin, we must plan ahead. In the long run, a little amount of planning ahead of time will save you a lot of time, sweat, and tears.

You must first assess your target audience. Who will make data-driven decisions?

The least linear of all the thought processes in the design process is analyzing your audience. Rather than depending on computer software or your programming talents, this step enlists the help of the most precious computer of all: your mind.

Step 2: Choose the Right Chart

Understanding all of the different chart kinds and selecting the right one for your desired takeaway message takes some time. There are a plethora of fantastic graphs to select from!

If you're not sure which chart to use, classics like the bar chart for comparing categories and the line chart for visualizing changes over time are good choices. Most of the time, these charts will be "correct," thus they're a safe bet.

Pie charts, contrary to popular perception, are not bad and do not have to be avoided entirely. When it comes to pie charts and donuts, I have seven rules. I teach you how to turn a 3D pie chart with way too many slices into a storytelling bar chart with icons in this pie chart makeover.

You can experiment with less-familiar chart kinds including bubble charts, bullet charts, dot plots, heat maps, scatter plots, slope graphs, social network maps, tree maps, waterfall charts, and more once you've mastered the traditional chart types.

Step 3: Select a Software Program

Sit down and create a rough draught of your visualization on the computer once you've got a basic notion of what you want it to look like.

For creating data visualizations, there are a plethora of software applications accessible. Some of them are completely free. Others are inexpensive. Others, at least for smaller businesses, are fairly costly.

Step 4: Declutter

It's time to tweak your data visualization and make your message stand out after you've completed the first draught on the computer. There is no such thing as a perfect computer program. Regardless of which software tool you use, you'll have to roll up your sleeves and make deliberate modifications. The first change I make is to clean up my visualization. There are much too many borders, lines, and superfluous ink in software systems. Look at every single speck of ink on the chart. Is there a specific reason behind it? If you can't explain why you need it, you don't need it.

Step 5: Clarify Your Message with Color

Colors are one of the most important aspects of a chart, so choose them carefully. There are a few procedures involved in selecting colors. To begin, pick a colour palette that complements your client's own style. Second, utilise action colour to draw the reader's attention and eyeballs in the right direction.

Although on-screen reading is becoming more popular, your visualization is still likely to be printed. Because color printing is costly, your visualization will most likely be reproduced in grayscale rather than full color. I like to test my manuscripts ahead of time to ensure that they will still be readable when printed in grayscale. You may put your draughts to the test in a few different ways. You may start by printing one draught in full color and the other in grayscale, then comparing the two. Alternatively, you may simply preview your image file in grayscale to avoid having to print anything. To choose an image file in Microsoft PowerPoint, for example, click on it to select it, then go to the Picture Tools: Format tab at the top of the screen. Then, go to the Color icon and choose Grayscale Recolor for your image file.


There are numerous ways to represent data science and machine learning: new tools and charts kinds appear on a regular basis, and each aims to produce more appealing and informative charts than the previous one. Instead of confusing and overloading the reader with unnecessary information, we recommend focusing on the notion that visualization should clarify and summarise the important message.

Previous Post Next Post