Data visualisation is a critical component of data analysis. While numbers and statistics are essential, the power of a visual graph or chart lies in its ability to communicate complex information quickly and efficiently. DataHorse, as a data analytics tool, incorporates several core principles of data visualisation, helping users make the most of their data visualisations. In this blog, we'll explore the core principles of data visualisation and how they enhance data interpretation using DataHorse.
1. Show the Data Clearly
The primary purpose of any data visualisation is to present the data in a way that is easy to understand. Users looking at a graph, report, or article rely on the visualisation to tell the underlying story without needing to read large chunks of data.
When we say "show the data," we don’t mean displaying every piece of information possible; rather, it's about being selective and intentional with the data you present. Visuals should focus on the most important parts of the data while simplifying the rest.
In DataHorse, this principle is followed by allowing users to focus on critical metrics by emphasising the right variables and aggregating or hiding less relevant data points. For example, when showing trends over time, DataHorse allows users to highlight key trends while avoiding unnecessary details like redundant labels or insignificant fluctuations.
2. Reduce the Clutter
One of the most common mistakes in data visualisation is clutter—unnecessary visual elements that distract from the message. This includes:
- Gridlines: While helpful, gridlines that are too bold or numerous can dominate the chart.
- Tick marks and labels: Too many ticks or overly detailed axis labels can overwhelm viewers.
- Gradients and textures: Overly stylized charts with decorative elements can often obscure the data they are meant to show.
In DataHorse, visualisations emphasise clean and minimalist design. Instead of using flashy decorations, the focus is placed on conveying data in a straightforward manner. Features such as automatic adjustments to gridlines, appropriate axis labels, and sleek colour palettes ensure that the data remains at the forefront. Users can also interact with settings to hide or show elements as needed, thus further reducing visual noise.
3. Integrate the Text and the Graph
One of the challenges often faced in data presentations is the disconnect between text and visuals. In some presentations, the text and chart are not integrated well, requiring viewers to jump back and forth to make connections.
To solve this, a better model is one where visualisations complement the text by integrating critical information directly into the graph. This includes:
- Legends that explain colours, lines, or bars, placed near the visual rather than far away.
- Titles that succinctly explain the visualisation’s purpose.
- Annotations or small pieces of descriptive text placed directly on the graph.
DataHorse allows users to enhance visualisations with annotations, labels, and dynamic legends. If users are creating a scatter plot to show correlation, they can add descriptive annotations right on the plot, ensuring that viewers don’t need to look elsewhere for explanations. Additionally, well-placed legends and smart labelling techniques are available by default, preventing confusion.
4. Use Preattentive Processing
Preattentive processing refers to the brain's ability to rapidly detect differences in basic visual properties, such as shape, size, colour, and position. Before we consciously process what a visualisation represents, our brain automatically recognizes differences in these attributes. Effective visualisations leverage this by making important information stand out through simple design choices.
Some common preattentive features include:
- Shape and size: Larger or uniquely shaped elements will immediately grab attention.
- Colour: Bright or contrasting colours can quickly direct the viewer's eye to significant data points.
- Position: Data points that are outliers in position (e.g., far from other points) naturally draw attention.
In DataHorse, users can customise colours, shapes, and sizes to highlight key insights. For instance, when showing comparative sales data, users can automatically highlight top-performing categories in brighter colours, while underperforming categories might be shown in muted shades. Similarly, outliers in scatter plots can be marked with larger points or contrasting colours to attract immediate attention.
5. Choose the Right Chart Type
Another fundamental principle of data visualisation is selecting the appropriate chart type. Not all data fits into a bar chart or line graph. Some data is better suited for scatter plots, heatmaps, or pie charts, depending on the relationships being displayed.
In the image above, several types of statistical significance tests are demonstrated, each corresponding to different data types and research needs. For example:
- A Fisher's Exact Test visual is best for comparing two categories on two categorical outcomes.
- A Regression Analysis is optimal for visualising continuous variables, like drug dose and tumor size.
With DataHorse, choosing the right chart type is simple. DataHorse automatically suggests the most appropriate chart based on the dataset you’re analysing. This feature helps users avoid misrepresentation of data by recommending the chart that best suits the data structure and analysis objective.
6. Maintain Proportionality and Accuracy
A key component of any visualisation is to ensure that data is accurately represented. Misleading charts—whether through exaggerated scales, distorted proportions, or incomplete axes—can skew interpretations.
DataHorse handles this by offering auto-scaling features that maintain the proper proportions. For instance, bar lengths, axis ranges, and grid spacing are adjusted to ensure an accurate depiction of data. Whether it’s a pie chart or scatter plot, DataHorse ensures that all elements are proportional and that users can trust the visual output.
Why Visualization Matters for Data Analysis
Visualisations are not merely illustrations but are essential to understanding and decision-making. When done well, they can:
- Simplify complex datasets into digestible insights.
- Highlight trends and outliers that might not be apparent from raw data.
- Engage viewers by making data more interactive and less intimidating.
By integrating these core principles, DataHorse offers not just a tool for analysis but a platform that guides users in creating effective, professional, and insightful visualisations. Whether you're looking to present findings to a board of directors, publish research results, or simply better understand your data, mastering these visualisation principles can make all the difference.
In conclusion, DataHorse empowers users by not only providing insights but also by ensuring that the way those insights are presented is optimised for clarity, accuracy, and impact. With built-in features that follow the core principles of data visualisation, users can trust that their data stories will be understood clearly and effectively.