In times driven by data and analytics, especially when it comes to major decision-making, the correct interpretation of the data is important, which can be greatly influenced by how we present it. The best tool, no matter how good it is, is irrelevant if it cannot be used. Similarly, a dataset, no matter how good or detailed it is, loses its value if it cannot be understood by the people who need to use it. Therefore solutions need to be tailored to the dataset in question, as well as to the audience or users who will make use of the solution in their day to day work. 

The right visualisation can reveal trends, habits, relationships and outliers which would otherwise remain hidden in data which is not visualised in the right manner. The wrong choice of visualisation can confuse, mislead, misdirect or alienate the very people that need to understand the data. So, think about it. Not only does the wrong choice of visualisation make your dataset incomprehensible but it can actually allow for misinterpretation, something that you would not want!

Let's talk about the audience for a moment because the audience in visualisation matters a lot. Datasets normally have multiple stories to tell, so the role of visualisation is to make those stories as clear as possible to the intended audience, which could range from analytics experts, who might prefer complex plots such as box plots, heatmaps or network graphs to capture complex patterns and nuances, to non-technical stakeholders such as managers, policymakers and consumers who might benefit from simple, fast-to-read, more intuitive visuals such as bar charts or line graphs. Inclusivity is very important because a visualisation tool should not assume that every user has the same level of statistical or technical literacy. For instance, a red and blue heatmap is great for an analytical expert unless he/she is colourblind, in which case the heatmap could be done in greyscale (black & white) so that it can be easily read by people who are colourblind. Clear labelling, accessible colour schemes and interactive features can ensure that people from different backgrounds are able to draw meaning from the same data. 

The dataset type most often will dictate the most suitable visualisation approach. For example, for categorical data such as product types, demographics and survey response the best visualisations are bar charts and column charts because these highlight proportions and comparison between discrete categories. On the other hand, for time series data such as sales figures over months, stock prices and sensor readings the best visualisations are line charts and area charts as these show trends, patterns and seasonality over time. For geospatial data such as customer locations, climate zones and logistic routes, the best visualisations are maps, choropleth maps and bubble maps as these add a spatial dimension making it easy to spot regional variations or clusters. For hierarchical maps, such as company structure and product families the best visualisations are treemaps and sunburst charts as these capture relationships and proportions with layers. For relational data such as social networks, process connections and supply chain the best visualisation are normally network graphs and sankey diagrams as they show interactions, dependencies and flows. Distributions such as customer ages or processing times are best visualised through histograms, box plots and violin plots to show variability, central tendencies and outliers. And for multivariate data such as for instance when comparing product performance across multiple metrics, this is best visualised through scatter plots, bubble charts and parallel coordinate plots since they allow the users to explore relationships between multiple variables at once. 

Accessibility should not be an afterthought. If your data tool is going to be accessed by stakeholders of different technical and/or analytical abilities then it is important that this is kept in mind at all stages when designing the tool. The tool should have clarity and jargon is to be avoided where possible. When colour palettes are involved ensure colour accessibility. Interactivity in the design such as the ability to zoom in or to filter as well as highlighting what matters as well as consistency throughout the various layers/stages of the tool are also important. 

So, data visualisation is not about placing the numbers neatly and pretty on a graph as a way to decorate a powerpoint presentation during a meeting nor to impress senior management with data overload. It does not work that way. Visualisation is an important choice to make as the plot can make a difference between insight and misunderstanding. By matching the visualisation to your dataset and audience as well as by designing with inclusivity in mind, we create tools that empower people and businesses to make better decisions.