Data visualisation techniques and how they support data insights

In an era of big data, businesses are inundated with vast amounts of information. Collecting and analysing this data is undeniably important, but the challenge then becomes how to share any findings and to effectively communicate the insights gleaned from the data.

This is where data visualisation becomes essential. Data visualisation techniques take raw data outputs and complex information and distil them into visual tools that anyone can understand.

The importance of effective communication when sharing data insights

Effective communication is inextricably linked to conveying data insights, because no matter how robust an analysis, if the findings are not communicated in a clear and comprehensible manner, the impact will almost always be diminished.

This is because stakeholders need to understand the key takeaways from any data analysis in order to make informed, data-driven decisions, so it is the responsibility of data storytelling and visualisation to transform complex data sets into insights that are easily digestible.

“Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations,” explains Harvard Business School Online’s Business Insights blog. “It can be used to put data insights into context for and inspire action from your audience.”

The blog goes on to point out that without effective communication, insights can go “unnoticed or unremembered by your audience.”

How data visualisation helps people understand insights gathered from data

Data visualisation serves as a bridge between raw data and actionable insights. Instead of drowning in Excel spreadsheets and tables filled with numbers, people can grasp the essence of the information through visual elements such as graphs, charts, and dashboards.

The human brain is inherently wired to process visual information more efficiently than raw text or numbers, making data visualisation a powerful tool for conveying complex findings.

In order to be most effective, though, it’s important to remember one of the golden rules of data visualisation: keep it simple.

“Simplicity is essential in data visualization because it allows the viewer to focus on the most important information without being overwhelmed by unnecessary details,” explains the UX Collective. “Clear charts work better because they are easy to read and interpret. When a chart is cluttered with too many colors, shapes, and labels, it becomes difficult to focus on the most critical information. The human eye is naturally drawn to contrast, so when there is too much going on in a chart, it becomes harder to identify the key points.”

How to use data visualisation techniques to support more effective communication

Data visualisation goes beyond simply representing data points on a graph. It begins with a thoughtful data science process that selects the most appropriate method to convey specific types of information. For example:

• Bar charts are excellent for comparing different categories.
• Line graphs are effective in showing trends over time. 
• Pie charts are useful for illustrating parts of a whole.

In its article Tips for creating effective, engaging data visualisations, Tableau – a popular data visualisation company – suggests starting with a few key questions.

• Who is my audience?
• What questions do they have?
• What answers am I finding for them?
• What am I trying to say?
• What other questions will my visualisation inspire, or what conversations may result?

How are data insights shared?

Data insights can be shared through a variety of channels, with the right choice depending on the audience and the context.

• Typically in business settings, reports and presentations are common mediums for sharing insights.
• Data scientists often use platforms like Tableau or various Python data visualisation libraries to create interactive dashboards. The interactivity of these tools enables stakeholders to explore the data on their own while building up business intelligence.
• Social media platforms like LinkedIn are also increasingly being used to share key findings with broader audiences. 

What are common data visualisation techniques?

Bar charts

Suitable for comparing different data categories, bar charts or graphs provide a clear visual representation of the relative sizes of different data points.

Line graphs

Ideal for showcasing trends over time, line graphs (also known as line charts) help visualise the progression – or regression – of data points.

Pie charts

Useful for illustrating parts of a whole, pie charts provide a quick overview of how individual components contribute to the overall data set.

Scatter plots

Effective in displaying the relationship between two variables, scatter plots help identify correlations and outliers within data sets.

Histograms

Perfect for understanding the distribution of data, histograms showcase the frequency and pattern of data points within specific ranges.

Heat maps

Particularly valuable for large data sets, heat maps use colours to represent values, making it easy to identify patterns and anomalies.

Infographics

Combining visual elements with concise text, infographics offer a comprehensive yet digestible overview of key insights.

Other benefits of using data visualisation

Apart from aiding communication, data visualisation offers a number of other benefits. These include:

• Enhanced decision-making. By creating visual representations that simplify complex data, data insights can be better understood and, more importantly, used for informed decision-making.
• Increased accessibility. Visualisations make data accessible to people with varying levels of expertise, from data scientists to decision-makers with limited statistical knowledge.
• Easier identification of trends and patterns. Visualisation techniques can help uncover previously unseen trends, patterns, and correlations within data.
• Informed resource allocation. Project managers often use data visualisations for resource planning and allocation, using visual representations for key programme metrics, such as time or people allocated to a project. In doing so, organisations can allocate resources more effectively, enhancing both performance and productivity.
• Improved data quality. Visualisation tools often highlight inconsistencies and errors in data, prompting data analysts to address and rectify issues, and ultimately leading to improved data quality.

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