Data visualisation is about converting complex data sets into simple visuals, helping viewers interpret the results. The primary objective of any data is to make informed decisions regarding any operation or take corrective actions. In-depth analyses give rise to plenty of data sets, making it harder to comprehend all of them. Summarising data in the form of visual analogies makes it easier for the brain to process the information quickly.
Converting complex data into a simple narrative is also known as data storytelling. This strategy helps laymen understand complex analysis, leading to better decision-making. Such techniques are widely used in commercial settings to plan future operations based on data analysis.
How can I use visuals like graphs, charts, and infographics to gain actionable insights? In this article, get insight into all the factors behind successful data storytelling and visualisation. We will share practical tips to better understand the correlation between data sets and their trends.
Data Story-Telling and Its Components
Data storytelling is a perfect way to address both technical and non-technical audiences and provide valuable insights based on any sort of data. Before planning various elements of this representation, learn about its components:
Data
This is the primary requirement of any presentation. You either gather first-hand data with surveys, interviews, or trend analysis, which is called primary data. Or use already available data from other research work called secondary data.
Narrative
This means using words to describe data sets easily and clearly. Narratives help us understand the essence of any analysis and its results. You can use technical or non-technical terms based on the target audience's expertise level.
Visualisations
Data visualisation involves using diagrams, graphs, charts, and infographics to represent data. This graphical representation makes the data storyline easier to interpret and provides actionable insights.
The Role of AI
Understanding all these aspects can be difficult for non-technical people. If you are also one of them, no worries. You can still take advantage of data visualisation with the help of artificial intelligence.
Tools like Twistly make your work trouble-free as it automatically generates charts and tables based on the provided prompts. You can present complex data sets in a clear and effective manner to your target audiences. What are ideal colours, contrast, alignment or choice of suitable data visuals? This AI tool decides everything, which requires you to provide detailed prompts to get a personalized solution. So, try to optimally utilise data storytelling or visualisation with this tool.
Factors Behind Successful Data Storytelling
For successful data story-telling that assists towards informed decisions with lower cognitive load, you need to consider the following elements:
Saturation and Data-ink Ratio
For uncluttered results in data visualisation, a balance between saturation and data ink is necessary. Experts strive for a high data-ink ratio where more ink is used to showcase data points rather than other elements,
Chart creators try to minimize non-data-ink that does not result in any meaningful insights. Moreover, using enough white spaces along with data ink prevents saturation and doesn't cause visual clutter.
Alignment and Visual Hierarchy
Another factor behind successful data storytelling is the arrangement of data points in a certain alignment to create a visual hierarchy. Two famous approaches, the Z and F patterns, are widely used by data specialists to organise and prioritise data.
How do viewers scan charts or graphs, and what regions get their attention first? Chart creators keep this in mind when presenting different data points. This makes it easier to grab the attention of viewers towards important stats.
Contrast and Pre-attentive Attributes
The use of different colours, positions, groupings, sizes, and text thicknesses helps create the desired contrast to highlight important information points. These techniques are also called pre-attentive attributes, which grab the attention instantly without any conscious effort. Experts highlight critical points by using darker shades, unique elements, positions, etc. They highlight the results without requiring you to go into details.
These are all the best approaches for using data visualisation successfully, irrespective of the nature of your content or context. Applying them to visual analogies like charts, graphs, or infographics enhances their impact.
Tips to Implement Visuals in Data-Heavy Presentations Effectively
With or without technical expertise, anyone can use data storytelling by using suitable visuals for data sets. To make this approach effective, the following are helpful strategies:
Based on the data and its categories, always make use of the right chart type for presentation,
Don't add unnecessary colours or misleading visuals in the chart for higher aesthetics.
Make use of graphs only when you have larger data sets to interpret in a presentation. Otherwise, text or showing stats is enough for message delivery.
Add summary points for each graphical representation and compute average or aggregate statistics for actionable insights.
Prefer line charts when you are representing time series or any continuous data.
To have a highly visual presentation, make use of pictographs.
Prefer a histogram when you want to express numeric data sets belonging to multiple categories.
To represent categorical data with long labels, make use of horizontal bars. Mentioning labels on the left side assists in scanning from left to right.
Prefer a scatter plot when you want to highlight the correlation between different variables.
Use darker hues or shades with plain or light-coloured backgrounds. This will allow you to highlight critical points that your audience will notice first.
If your target audience has less expertise, try using non-technical terms. If you use tech jargon, add its definition below.
Bottom Line
Data visualisation is an effective method of interpreting complex data to produce actionable insights without any confusion. Whether used for educational learning, commercial objectives, or any other purpose, it simplifies any data-heavy presentation. Contrasts, data-ink ratio, visual hierarchy, and other aspects are essential to consider when creating data storytelling that is free from cognitive load.
