Advancements in technology have made data more available and accessible than ever before. However, data is not useful in itself. We must have the ability to interact with this data and make meaning from it by identifying and understanding patterns and outliers. And then we need to be able to present this data in the best way possible.
Data visualization is the graphic representation of data, and it’s art is in understanding the best ways to reveal complex information at a glance. Data visualization is not about knowing which graph or chart is the best to use but rather understanding the layers of detail that generate insights for the viewer. Since art concerns itself on matters of beauty, and we are drawn to beauty much like moths are drawn to flame, our visualizations cannot be devoid of aesthetic considerations.
As described by Noah Ilinksky in his book, Beautiful Visualization, for a visual to qualify as beautiful, it must be aesthetically pleasing yes, but it must also be novel, informative, and efficient. Good visualization entails that data needs to convey information clearly, excite and engage observers, drive home the intended message and remain to-the-point.
Process Of Data Visualization
1. Intended Message
The first step of data visualization is not the nitty-gritty of format or structure; it is intent.
Is essential to envisage your intent and ascertain your goal. For instance, you determine that your intent is to sell your product to a new audience. Identifying a goal will then help you articulate a clear purpose of your visual. Your goal could be to devise a new strategy to gain engagement of the new audience.
2. The Data Itself
Keeping in mind the intent of your visual, the next step is to select what data is best suited to meet your goals. Not all data is relevant. Data needs to be culled to communicate a clear message. When trying to market a product to a new audience, relevant data could include demographics, what channels your target audience use, their purchase history and loyalty.
3. Graphical and Aesthetic Construction
Graphical construction includes layouts, axes, shapes, colors, lines and typography. The main purpose of the graphical elements is to lay out the information. When using graphical elements, any element that does not guide, communicate or highlight information is extraneous. It will take away, rather than add value to your representation. Your choice of graphical construction needs to be based on clarity and efficiency.
Aspects such as color, placement, size, icons, layout, and labels are all aesthetic considerations. These choices must take into account familiarity of their audience and minimize noise.
Data visuals are used for the same purposes as speech: to inform, to persuade or to entertain. Here is a quick summary of what different graphical representation tools are best suited to:
- To compare sets of values: column charts, mekko charts, bar graphs, pie charts, scatter plots, bullets and line graphs
- To show composition (parts that make up a whole): pie charts, stacked bar charts, mekkos, stacked columns, area charts and waterfall charts.
- To showcase distribution of data: scatter plots, mekkos, lines, columns and bars.
- To analyze trends: line charts, dual-axis lines, and column charts
- To establish relationships between data sets: scatter plots, bubbles, and line charts.