Definition

Data visualisation refers to graphical representation of information and data to evaluate data and further take decisions to drive business.

Description

In the world where the demand for data is rising exponentially, data visualisation is just an art to analyse information. Companies need data at all levels as a go to guide for their marketing and financial decisions.

Using elements like graphs, maps, and charts data can be compiled to visualise data and understand trends and patterns about market and consumers. Data visualisation using graphs, colours, and charts makes decoding data easy.

Data visualisation helps in:

• Easily sharing information
• Explore more business opportunities
• Identify relationship between different patterns

With so many pluses, data visualisation can at times bring disadvantages like inaccurate information because decision making can be biased.

Example

In a report of Transparency Market Research, it was found that global sanitary napkins were valued at US$24.5 Bn in 2021. The market is expected to grow at 5% till 2031.

Sanitary napkins are disposable napkins that retain the menstrual fluids preventing leakage. With the growing awareness at the global level, females are more conscious to maintain hygiene and good health.

This calls for a huge increase in the demand for napkins. Hence the research showed the expected rise in demand for sanitary napkins.

Importance of Data Visualisation

Data Visualisation helps companies communicate information universally using visuals. The intent is to back the decision making with authentic data.

Data visualisation assists in reading consumer behaviour and stress on the points that need quick improvement.

Some factors that make data visualisation important include:

• Prevent confusion within the company at all levels. Data visualisation explains the strategy to all team members and employees at all levels. It leaves no room for confusion.
• Faster decision making with the ability to improve insights.
• Improved ability to serve the audience with deep insights into their interests.
• Reduces the need for data scientists as data is easily accessible and understandable.
• Better ability to fix shortcomings and achieve success faster.

General Types of Data Visualisation

These are the type of data visualisation:

• Infographics: A display of information that has combined effects of visuals and words to represent data. Infographics may use charts and diagrams.
• Chart: Information that is presented in graphical or tabular form with data displayed along two axes are defined as a chart.
• Table: Set of information displayed in rows and columns is a table.
• Graph: Representing information in the form of lines, segments, or curves usually along two axes.
• Geospatial: Displaying information on maps using different shapes or colours to show relationships between data and location.
• Dashboards: Showing information in one place to help analyse and present data can be done through dashboards.

Popular ways for data representation for visualisation

Some of the popular ways to visualise data are:

• Line: Lines are used to depict trends for different categories over the same period of time to compare the data.
• Bar: Bars use different heights to depict value as representation of information. Bar graphs can be horizontal, vertical, stacked, or grouped bars.
• Pie: Pie chart shows the contribution value by different categories to make up the whole.
• Pivot: Pivot tables are useful when you want to derive exact information rather than just taking a sense of information.
• Scatter: Scatter charts depict the data by colour and volume by size of the circle. They help in visualising the relationship between two variables.
• Bubble: Bubble charts similar to scatter charts represent the weight of the value by circumference of the circle. Such charts can be used when there are several elements to study.
• Treemap: Treemaps are useful for displaying hierarchy between categories and subcategories.
• Polar: Polar chart is the same as pie chart. All the sizes of the angle depict the same value. The share of each is measured by how far the value goes from the centre.

FAQs on Data Visualisation

What makes data visualisation good?

It depends on how people perceive data. For data visualisation it is important that you highlight important aspects of data after looking at important variables.

Mention key points and include clean information. Remember these points for data visualisation to look good:

• Data positioning
• Data marking
• Colour theory
• Bars representation over circle and squares
• Information with 3D charts

What is the best way to visualise data in more than 3 dimensions in a single chart?

In a three dimensional chart, information is expressed in height, width, and depth. You can make use of colour, sizes, and shapes for depicting changes through time.

How to use colour in your visualisation?

You can do this to use colour in your visualisation:

• Highlight important information.
• Keep one colour to represent continuous data.
• Avoid using weird colour palettes.
• Give accessibility to colour blind viewers.

What are some popular tools that assist in data visualisation?

These are some tools that assist in data visualisation:

• Microsoft Excel
• Microsoft Power BI
• IBM Cognos Analytics
• SAP Lumira
• SAS Visual Analytics
• Jupyter
• Google Charts
• Micro Strategy
• D3.js
• Zoho Analytics