If you’re at all familiar with data analysis, then you’ve surely encountered data visualisation. It’s a key part of data analysis. In today’s post, we’ll explain and dive deep into the different visualisation categories and the types of visualisation charts, but first, let’s make sure we all understand what it is and why it’s important for businesses and working professionals.
Data visualisation is the representation of data or information in a graph, chart, or other visual formats. By using visual elements like charts, graphs, and maps, data visualisation tools provide an accessible way to see and understand trends, outliers, and patterns in data.
With the rise of big data, we need to be able to interpret increasingly larger batches of data. Machine learning makes it easier to conduct analyses like predictive analysis, which can then serve as helpful visualisations to present. But data visualisation is not only important for data scientists and data analysts, it’s necessary to understand data visualisation in any career. Whether you work in finance, marketing, technology, design, or anything else, you have data and you need to visualise data.
The Importance of Data Visualisation
We need data visualisation because a visual summary of information makes it easier to identify patterns and trends than looking through thousands of rows on a spreadsheet. It’s the way the human brain works. Since the purpose of data analysis is to gain insights, data is much more valuable when it is visualised. Even if you’re able to pull insights directly from the data without any visualisation, it would be difficult to present or communicate this to your audience. For example, rattling off numbers to your boss won’t tell them why they should care about the data, but showing them a graph of how much money the insights could save/make them is sure to get their attention.
Here are some benefits of successful data visualisations:
- Faster decision-making: By viewing and manipulating large data sets in visual formats, you can understand the story your data tells you at a quick glance, rather than poring over piles of numbers and tables for hours or weeks.
- More data exploration: Modern visual analytics technologies allow users to interact with data in context to discover hidden patterns, see data relationships and uncover actionable insights.
- Better track business initiatives: Dashboards help people easily track the performance of business initiatives by allowing them to quickly see how everyday operations affect KPIs
- Extend your analytics investment: Because visuals make it easier to understand data, everyone in an organisation including business users can explore data and find insights that improve company growth and effectiveness.
Why Data Visualisation is Important For Your Career
It’s hard to think of a professional industry that doesn’t benefit from making data more understandable. Every sector benefits from understanding data including manufacturing, media, trade, healthcare, education, finance, retail, etc.
Data visualisation skills are also a highly coveted skillset among recruiters and businesses in today’s data-driven world. It’s increasingly valuable for professionals to be able to use data to make decisions and use visuals to tell stories of when data informs the who, what, when, where, and how. While traditional education typically draws a distinct line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two: data visualisation sits right in the middle of analysis and visual storytelling.
The Uses of Data Visualisation
Data visualisation has many uses and each type of data visualisation can be used in different ways. We’ll get into the different types of data visualisations and visualisation charts in a moment, but for now, here are some of the most common ways data visualisation is used.
Monitor changes over time
This is perhaps the most basic and common use of data visualisation, but that doesn’t mean it’s not valuable. The reason it is the most common is that most data has an element of time involved. Therefore, the first step in a lot of data analyses is to see how the data trends over time.
Frequency is also a fairly basic use of data visualisation because it also applies to data that involves time. If time is involved, it is logical that you should determine how often the relevant events happen over time.
Identifying correlations is an extremely valuable use of data visualisation. It is extremely difficult to determine the relationship between two variables without a visualisation, yet it is important to be aware of relationships in data.
Examining a network
An example of examining a network with data visualisation can be seen in market research. Marketing professionals need to know which audiences to target with their message, so they analyse the entire market to identify audience clusters, bridges between the clusters, influencers within clusters, and outliers.
When planning out a schedule or timeline for a complex project, things can get confusing. A Gantt chart solves that issue by clearly illustrating each task within the project and how long it will take to complete.
Analysing value and risk
Determining complex metrics such as value and risk requires many different variables to be factored in, making it almost impossible to see accurately with a plain spreadsheet. Data visualisation can be as simple as colour-coding a formula to show which opportunities are valuable and which are risky.
5 Types of Data Visualisation Categories
Data visualisations belong in the temporal category if they satisfy two conditions: that they are linear, and that they are one-dimensional. Temporal visualisations normally feature lines that either stand alone or overlap with each other, with a start and finish time.
Examples of temporal data visualisation include:
- Scatter plots
- Polar area diagrams
- Time series sequences
- Line graphs
Data visualisations that belong in the hierarchical category are those that order groups within larger groups. Hierarchical visualisations are best suited if you’re looking to display clusters of information, especially if they flow from a single origin point. The downside to these graphs is that they tend to be more complex and difficult to read, which is why the tree diagram is used most often. It is the simplest to follow due to its linear path.
Examples of hierarchical data visualisations include:
- Tree diagrams
- Ring charts
- Sunburst diagrams
Network data visualisations show how they relate to one another within a network. In other words, demonstrating relationships between datasets without wordy explanations.
Examples of network data visualisations include:
- Matrix charts
- Node-link diagrams
- Word clouds
- Alluvial diagrams
Just like the name, multidimensional data visualisations have multiple dimensions. This means that there are always 2 or more variables in the mix to create a 3D data visualisation. Because of the many concurrent layers and datasets, these types of visualisations tend to be the most vibrant or eye-catching visuals. Another plus? These visuals can concise a ton of data down to key takeaways.
Examples of multidimensional data visualisations include:
- Scatter plots
- Pie charts
- Venn diagrams
- Stacked bar graphs
Geospatial or spatial data visualisations relate to real-life physical locations, overlaying familiar maps with different data points. These types of data visualisations are commonly used to display sales or acquisitions over time and can be most recognisable for their use in political campaigns or to display market penetration in multinational corporations.
Examples of geospatial data visualisations include:
- Flow map
- Density map
- Heat map
6 Types of Common Data Visualisation Charts
When you think of data visualisation, your first thought probably immediately goes to simple bar graphs or pie charts. While these may be an integral part of visualising data and a common baseline for many data graphics, the right visualisation must be paired with the right set of information. Simple graphs are only the tip of the iceberg. There’s a whole selection of visualisation methods to present data in effective and interesting ways.
1) Bar Chart
At some point or another, you’ve either seen, interacted with, or built a bar chart before. Bar charts are such a popular graph visualisation because of how easy you can scan them for quick information.
A Bar chart illustrates changes over time. But if there is more than one variable, a bar chart can make it easier to compare the data for each variable at each moment in time. For example, a bar chart could compare the company’s sales from this year to last year.
When to use a bar chart visualisation?
Use a bar chart for the following reasons:
- You want to compare two or more values in the same category
- You want to compare parts of a whole
- You don’t have too many groups (less than 10 works best)
- You want to understand how multiple similar data sets relate to each other
Best practices for a bar chart visualisation
- Use consistent colours and labelling throughout so that you can identify relationships more easily
- Simplify the length of the y-axis labels and don’t forget to start from 0 so you can keep your data in order
2) Line Chart
Like bar charts, line charts help to visualise data in a compact and precise format which makes it easy to rapidly scan information in order to understand trends. Line charts are used to show resulting data relative to a continuous variable – most commonly time or money. The proper use of colour in this visualisation is necessary because different coloured lines can make it even easier for users to analyse information.
The x-axis is usually a period of time, while the y-axis is quantity. So, this could illustrate a company’s sales for the year broken down by month or how many units a factory produced each day for the past week.
When to use a line chart visualisation?
Use a line chart for the following reasons:
- You want to understand trends, patterns, and fluctuations in your data
- You want to compare different yet related data sets with multiple series
- You want to make projections beyond your data
Best practices for a line chart visualisation
- Along with using a different colour for each category, you’re comparing, make sure you also use solid lines to keep the line chart clear and concise
- To avoid confusion, try not to compare more than four categories in one line chart
3) Scatter Plot
Scatterplots are the right data visualisations to use when there are many different data points, and you want to highlight similarities in the data set.
Each point on a scatter plot means “when x = this, then y equals this.” That way, if the points trend a certain way (upward to the left, downward to the right, etc.) there is a relationship between them. If the plot is truly scattered with no trend at all, then the variables do not affect each other at all.
When to use a scatter plot visualisation?
Use a scatter plot for the following reasons:
- You want to show the relationship between two variables
- You want a compact data visualisation
Best practices for a scatter plot visualisation
- Although trend lines are a great way to analyse the data on a scatter plot, ensure you stick to 1 or 2 trend lines to avoid confusion
- Don’t forget to start at 0 for the y-axis
If you’re someone who wants a little bit of everything in front of you in order to make thorough decisions, then tables are the visualisation to go with. Tables are great because you can display both data points and graphics, such as bullet charts, icons, and sparklines. This visualisation type also organises your data into columns and rows, which is great for reporting.
When to use a table visualisation?
Use a table for the following reasons:
- You want to display two-dimensional data sets that can be organised categorically
- You can drill-down to break up large data sets with a natural drill-down path
Best practices for a table visualisation
- Be mindful of the order of the data. Make sure that labels, categories and numbers come first then move on to the graphics
- Try not to have more than 10 different rows in your table to avoid clutter
5) Pie Chart
Pie charts are an interesting graph visualisation. At a high-level, they’re easy to read and understand because the parts-of-a-whole relationship is made very obvious. But top data visual experts agree that one of their disadvantages is that the percentage of each section isn’t obvious without adding numerical values to each slice of the pie.
When to use a pie chart visualisation?
Use a pie chart for the following reasons:
- You want to compare relative values
- You want to compare parts of a whole
- You want to rapidly scan metrics
Best practices for a pie chart visualisation
- Make sure that the pie slices add up to 100%. To make this easier, add the numerical values and percentages to your pie chart
- Order the pieces of your pie according to size
- Use a pie chart if you have only up to 5 categories to compare. If you have too many categories, you won’t be able to differentiate between the slices
A histogram is a data visualisation that shows the distribution of data over a continuous interval or a certain time period. It’s basically a combination of a vertical bar chart and a line chart. The continuous variable shown on the X-axis is broken into discrete intervals and the number of data you have in that discrete interval determines the height of the bar.
Histograms give an estimate as to where values are concentrated, what the extremes are and whether there are any gaps or unusual values throughout your data set.
When to use a histogram visualisation?
Use a histogram for the following reasons:
- To make comparisons in data sets over an interval or time
- To show a distribution of data
Best practices for a histogram visualisation
- Avoid bars that are too wide that can hide important details or too narrow that can cause a lot of noise
- Use equal round numbers to create bar sizes
- Use consistent colours and labelling throughout so that you can identify relationships more easily
There are countless data visualisations out there and they all tell different yet impactful data stories. In other words, your data isn’t rendered visually useless just because it doesn’t work in one particular category or type of data visualisation. You just need to help your data find its visual match. Once you’ve got that covered, you can start pinpointing key insights and trends.
When data visualisations are put together on a dashboard with a data visualisation tool, these visualisations become magic in helping people understand what is going on in their role/business that is impacting them.
It’s also important to mention that data visualisations are not limited to certain colours, icons, and overall design. You’re the artist here; your visual preferences can make a difference when telling your story.
Learn More About Data Visualisations & How To Get Started on Creating Your Own
Effective data visualisation is a crucial step in data analysis. Without it, important insights and messages can be lost. At Aventis, we understand the importance of data visualisation, which is why we’ve kick-started our Data Analytics Value Chain Series with these three foundational courses for beginners.
Interested in reading more about data visualisation? Check out this comprehensive article – Numbers Shouldn’t Lie – An Overview of Common Data Visualization Mistakes.