17  Data Visualization

Earlier, you learned that data visualization is the graphical representation of information. As a data analyst, you will want to create visualizations that make your data easy to understand and interesting to look at.

Because of the importance of data visualization, most data analytics tools (such as spreadsheets and databases) have built-in visualization components — while others (such as Tableau) specialize in visualization as their primary strength.

In this reading, you will explore the steps involved in planning a data visualization and a few of the most common tools used to create them.

Steps to Plan a Data Visualization

Let’s consider a real-world example. Imagine you’re a data analyst for a clothing distributor. The company helps small clothing stores manage their inventory, and sales are booming.

Your company is about to make a major update to its website. To guide decisions for this update, you’ve been asked to analyze data from the existing website and sales records.

Step 1: Explore the Data for Patterns

You begin by asking your manager or the data owner for access to sales records and website analytics reports.

These include details such as customer behavior on the website, visitor demographics, purchase data, and total sales.

While exploring, you notice a pattern — customers from the northeast tend to visit more often and spend more per order. This could help explain current sales trends and provide insight into how the new website might boost sales even further.

Step 2: Plan Your Visuals

Now it’s time to refine and present your analysis.
Right now, the data exists across several different tables — not ideal for stakeholders. You decide to create visuals that explain your findings clearly and quickly.

Since your audience (sales and marketing teams) is sales-oriented, your visualizations should:

  • Show sales numbers over time
  • Connect sales to location
  • Display the relationship between website visits and sales
  • Identify which customer groups fuel growth

Step 3: Create Your Visuals

With your plan in place, it’s time to create your charts and graphs.
The visualization process involves experimenting, refining, and iterating until you have visuals that tell a clear story.

A combination of visuals often works best:

  • Line charts – track sales trends over time
  • Maps – show how sales vary by location
  • Donut charts – display customer segments
  • Bar charts – compare total visitors who made a purchase

These visuals help communicate the story behind your data and make your findings more engaging for stakeholders.

Build Your Data Visualization Toolkit

There are many tools available for creating visualizations. Your choice depends on:

  • The size and complexity of your dataset
  • The methods used in your analysis (spreadsheets, SQL, or programming)
  • The type of visualization you want to produce

Let’s explore a few popular tools.

Spreadsheets (Microsoft Excel or Google Sheets)

In our example, the built-in chart options in spreadsheets make it simple to create basic visuals quickly.

Spreadsheets are great for visualizations such as:

  • Bar charts
  • Pie charts
  • Line graphs
  • Maps
  • Waterfall and funnel diagrams

They are ideal for small datasets and quick visual exploration.

But sometimes, you need a more powerful tool to handle complex visualizations — and that’s where Tableau and RStudio come in.

Visualization Software (Tableau)

Tableau is one of the most widely used tools for professional data visualization. It lets you:

  • Pull data from multiple sources
  • Turn that data into interactive dashboards
  • Follow built-in visual best practices

Tableau works well with diverse datasets and provides dashboards where users can click and explore data interactively.

You can start exploring Tableau via the Tableau Public platform — it’s free, easy to use, and full of sample datasets.

For inspiration, visit Tableau’s Viz of the Day to see creative examples like Lighthouses of Greece and Who’s Talking in Popular Films.

Programming Language (R with RStudio)

Many data analysts use R, a programming language designed for data analysis and visualization.

RStudio, an integrated development environment (IDE), provides powerful tools for building dashboards and interactive reports.

Resources to start exploring RStudio:

Key Takeaways

  • Data visualization makes data easier to understand and more engaging.
  • You can use tools like spreadsheets, Tableau, and RStudio to create visuals depending on your needs.
  • Good data visualizations combine clarity, insight, and storytelling.
  • Experiment with multiple visualization types to see which best communicates your findings.
  • Stay curious — explore, test, and improve your visualization skills continuously.

By combining analytical thinking with great visuals, you’ll transform raw data into insights that drive meaningful decisions.