Data Visualisation: Why Does it Matter?

What is data visualisation?

Firstly, what exactly is data visualisation? It can be defined as the methods used to represent data as visual objects contained in graphics. These visual infographics, including charts, graphs and maps helps identify key trends, patterns and relationships in data.

Why does it matter?

Knowing how to effectively turn data into raw insights is an essential skill to have within this fast-paced ‘data- heavy’ world:

  1. Data visualisation gives answers faster

Graphs visually illustrate the relationships in data through the patterns in their shapes. It is evident how much faster it takes to recognise trends in visual graphs rather than scanning rows of numbers in a table of data. This helps to identify key trends in data instantly, therefore improving the overall productivity and efficiency of your workflow.

  1. Helps communicate messages clearly

Data can often be too complicated to be described adequately in text format. Key information and patterns can also be lost this way. Creating infographics through charts and graphs is essential to help produce reports and presentations and help back-up key points in meetings. It is also considerably a more visually appealing way to view data. Therefore, acquiring the necessary skills to visualise data is essential to communicate effectively with internal stakeholders and external audiences within your organisation.

  1. Helps reveal unnoticed information

Charts and graphs are essential to not only help convey information, but also establishes strong comparisons to be made between sets of data. Bar charts are particularly good for comparisons, whereas scatter plot charts help identify relationships and distributions in data sets.

What are the different types of data?

  1. Categorical

Qualitative or categorical data exemplifies types of data which are divided into groups. Examples of these variables include age group, sex, race or educational level.

  1. Numeric

Numerical data is associated with data that is measurable. It can be classified into two groups, including discrete data, which represents items that can be counted, and continuous data, which identifies as measurements. Examples of numeric data include a person’s height, weight or blood pressure.

  1. Time-based

Time-based data or a ‘time-series’ is a sequence of data points taken continuously, at equally spaced points in time. This type of data set is used to measure change and predict how something may change in the future. Examples of time-based data can include performance monitoring, network data or trades in a market.

Want to find out more about data visualisation?

Attend one of our ‘Effective Data Visualisation’ courses, to listen to content expert, Andy Pemberton. Learn the key principles of data visualisation and know how to turn raw data into infographics, presentation slides, dashboards or reports.

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