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Any tech company of note understands the power of big data, but only the best are able to interpret a raw output transforming complex datasets into digestible, insightful visuals that enable better understanding and ultimately drive decision-making. 

Working directly with one of the world’s largest telecommunications brands, I’ve seen firsthand how presenting raw numerical data to the upper reaches of the company can lead to missed opportunity, miscommunication and a resistance to buy-in for sign off on major projects. Had the data been presented visually, things may have gone differently.

With those learnings in my back pocket, I’ll take a deep dive in this article into the three categories of data visualization—graphs, charts and maps—and how the right visualization tools can help to translate complex data into simple illustrations.

As we take a walk through this tutorial of data visualization types, we’ll look at their characteristics, applications, and best use cases, providing you a comprehensive understanding of different visualization techniques in the multifaceted world of data visualization (without needing to be a data scientist).

Why Is Data Visualization Important?

In the tech world, data can become very complex very quickly no matter the nature of the business, yet all rely on a data-driven strategy. From the smallest bootstrapped indie startups to giant SAAS behemoths, there is a need to understand and interpret the data the company is receiving in order to analyze performance and make decisions. 

Data visualization software can pull data into dashboards where it can be used both for regular reporting as well as presentations and in high-level meetings. Here are the primary drivers that well-presented data visualizations can unlock for your business: 

  1. Simplifying Complex Data: Large volumes of data can be overwhelming and difficult to understand when presented in raw, numerical formats. Data visualization simplifies this complexity by representing data in a visual format, making it easier to comprehend and interpret.
  2. Identifying Patterns and Trends: Visual data representations can help users quickly identify patterns, trends, and correlations that might go unnoticed in text-based data. For example, a line graph of sales over time can clearly show whether sales are increasing, decreasing, or remaining steady.
  3. Speeding Up Decision-Making: By presenting data visually, data visualization can help decision-makers understand the data faster, thus speeding up the decision-making process. Instead of sifting through spreadsheets or reports, they can glance at a visual representation to get the information they need.
  4. Enhancing Memory Retention: People generally remember visual information better than text-based information. Therefore, data visualizations can enhance memory retention and recall of important data points and trends.
  5. Facilitating Communication: Data visualizations can communicate complex data in an understandable way to various stakeholders, including those who may not be data experts. This can facilitate discussions and decision-making processes.
  6. Driving Action: By making data more understandable and accessible, data visualization can help businesses identify areas that need attention or improvement, thus driving action. For example, a heatmap showing customer activity on a website can help identify areas where users are most engaged, informing decisions about where to focus marketing efforts.
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The 3 Categories of Data Visualization

The three most common categories of data visualization are graphs, charts, and maps.

By choosing the right type of visualization for your data, you can reveal insights, tell a story, and guide decision-making. So let’s explore which visualizations are right for your data.


Graphs hold a special place in the realm of data visualization. They serve as a bridge, connecting raw data with the insights we seek.

There are several types of graphs, each with its unique strengths:

Line Graphs: Line graphs excel at showing trends over time. They allow us to track the rise and fall of data points, revealing patterns that might otherwise go unnoticed.

Bar Graphs: Bar graphs are perfect for comparing different data sets. They provide a clear, visual comparison that makes it easy to see which data set is larger, smaller, or if they're roughly equal. There are also stacked bar graphs, which extends the standard bar chart from looking at numeric values across one categorical variable to two.

Bullet Graphs: A bullet graph is a bar marked with extra encodings to show progress towards a goal or performance against a reference line. It’s seemingly inspired by traditional thermometer charts and progress bars found in dashboards.

Radial Graphs: Radial Graphs are multi-axis graphs that show a number of similar ideas in one graphical presentation. They are so called because the axes, of which there are usually five or six, radiate out from a central point.

Scatter Plots: Scatt plots are another type of graph that are ideal for showing the relationship between two variables. They help us see correlations, clusters, and outliers.

Box Plots: Boxplots display the distribution of data based on a five number summary. They can tell you about your outliers and their values, if your data is symmetrical, how tightly your data is grouped and if and how your data is skewed.

Choosing to use a graph over other forms of data visualization often comes down to the nature of your data and the story you want to tell. If you're dealing with trends over time, relationships between variables, or comparisons, a graph is often the best choice.

Graphs offer several advantages. They simplify complex data, making it easier to understand and interpret. They reveal patterns, trends, and correlations that might be hard to spot in raw data. And they communicate this information in a way that's visually appealing and easy to grasp.

Graphs are a powerful (if not the most powerful) tool in the data visualization toolkit.


Charts are another fundamental tool in data visualization. They offer a unique way to represent data, making it easier to compare, categorize, and comprehend at a glance.

There are several types of charts, each with its unique strengths and use cases:

  • Pie Charts: These are excellent for showing proportions or percentages. They provide a clear, visual representation of how each part contributes to the whole. One of the most popular and simple chart types to understand.
  • Donut Charts: Donut charts are essentially the same as Pie Charts but with the area of the centre cut out. It’s one of the simplest representations of data and is a circular statistical graphic which is divided into slices to illustrate numerical proportions or percentages.
  • Bar Charts: Ideal for comparing different data sets or categories. They offer a visual comparison, making it easy to see which category is larger, smaller, or if they're roughly equal. There’s also such thing as a stacked bar, which shows the composition and comparison of a few variables, either relative or absolute, over time.
  • Column Charts: Are only different from bar charts in their orientation. A bar chart is oriented horizontally, whereas a column chart is oriented vertically. While they are similar, they can’t always be used interchangeably because of the difference in their orientation.
  • Histograms: These are useful for showing the distribution of data. They help identify patterns, such as whether the data is normally distributed or skewed.
  • Area Charts: These are particularly useful when you want to visualize volume or quantity over time. They can also be used to show the cumulative value of multiple data series, making them ideal for understanding the proportion of individual components to the whole, or for comparing multiple related data series.
  • Bubble Charts: Bubble charts are excellent for visualizing data that has three dimensions of variables. Each bubble represents a data point, with the position of the bubble reflecting two data values (usually on the x and y axes), and the size of the bubble reflecting the third value. This makes bubble charts useful for comparing and visualizing complex relationships between data sets.
  • Line Charts: Line charts are ideal for showing trends over time. They connect data points with a line, making it easy to see whether numerical values are increasing, decreasing, or staying the same over time. This makes line charts a great choice for tracking changes in data over intervals like months, quarters, or years.
  • Funnel Charts: Funnel charts may be of particular relevance to CFOs and CROs, as it’s used to represent stages in a sales process and show the amount of potential revenue for each stage.
  • Waterfall Charts: A waterfall chart helps in understanding the cumulative effect of sequentially introduced positive or negative values. It can help to understand how a starting value becomes a final value over a period of time through a series of intermediate additions and subtractions.
  • Flow charts: A flowchart is a type of diagram that represents a workflow or process. Typically, it shows the steps as boxes of various kinds, and their order by connecting them with arrows
  • Radar Charts: Radar charts (also known as spider charts, polar charts, web charts, or star plots) are a way to visualize multivariate data in a two-dimensional diagram.
  • Gantt Chart: A gantt chart is another version of a bar chart that illustrates a project schedule. This type of chart is frequently used by project managers and the like.

The decision to use a chart over other forms of data visualization often depends on the nature of your data and the story you want to tell. If you're dealing with proportions, comparisons between categories, or distributions, a chart is often the best choice.

It's important to note that while all graphs are charts, not all charts are graphs. Graphs are a subset of charts that represent data points connected by lines in a two-dimensional space. Charts, on the other hand, is a broader category that includes graphs but also other types of data representations like pie charts and histograms. So even though the two terms, charts and graphs are often used interchangeably, there are some subtle differences and, therefore, they are categorized as such.


Maps are the geographical heroes of data visualization. They transform spatial data into visual stories, revealing patterns and distributions that might otherwise remain hidden.

There are several types of maps, each with its unique strengths:

  • Choropleth Maps: These are excellent for showing data that's divided into geographical regions, such as countries, states, or counties. They use different colors or patterns to represent data, making it easy to see variations across the regions.
  • Heat Maps: Ideal for showing density or intensity in a geographical area. They use color gradients to represent data, with different colors or shades indicating different levels of intensity.
  • Dot Distribution Maps: These are useful for showing the location and distribution of individual events or features. Each dot represents a data point, making it easy to see where events or features are concentrated.
  • Treemaps: Treemaps are particularly effective when you need to display hierarchical data across two dimensions. They allow for the visualization of large amounts of nested data, making them ideal for showing proportions among variables or categories within a dataset. 

The decision to use a map over other forms of data visualization often depends on the nature of your data. When you have geographical location data, mapping is the obvious choice. Though remember that mapping also expands to spatial patterns too and this is an area that can really unlock insight into different categories by providing a way of looking at data in a way that it hasn’t before. 

To expand on that point, mapping data offers several key advantages:

  • They provide a geographical context, making it easier to understand and interpret spatial data.
  • They reveal patterns and distributions related to location, which can be hard to spot in raw data.
  • They communicate this information in a visually appealing and easily digestible manner.

There is very much a time and a place for creating data visualizations in this way but when done correctly they can inform all manner of insights.

Using Data to Make Decisions

Having journeyed through the depths of data visualization, we've explored the three main categories: graphs, charts, and maps. And while there are many more types of data visualization you can utilize, these three categories are the most commonly used as ways of interpreting data analysis across any business.

Graphs, with their ability to show trends and relationships, are perfect for tracking changes over time or comparing different variables. Charts, on the other hand, excel at comparing data points, showing proportions, and categorizing data. And maps, the geographical heroes, are invaluable for revealing patterns and distributions related to location.

Don’t forget, there are a plethora of data visualization tools on the market to help pull all of the key metrics into one place through the use of APIs, direct connections and manual uploads. Making visualizations interactive is also something many of the tools contain ‘off the shelf’.

Choosing the right tool to display your best data is a crucial step in the data visualization process. It's about understanding your data, the story you want to tell, and the best way to communicate that story.

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Matt Slater
By Matt Slater

Matt Slater is the SEO Director of Black and White Zebra, a digital publishing and tech company. Raised in the United Kingdom, Matt now calls Toronto, Canada home. With over 10 years experience working with startups, emerging technology and the online media industry, his main focus has been driving organic growth and engaging new users in the most meaningful ways possible.

Understanding the big picture and the 'why' behind decision making has been his cornerstone when it comes to creating content that matches user intent.