You have so many options to present your data. All data visuals can be either limited or enhanced by human perception. Morphing your data into visual elements is called encoding. Encoding is done through visual elements like lines, points, shapes, typography, color, texture, position, size, and direction. Choosing an optimal way of encoding your data is key to the effectiveness of your data presentation.
Laws of human perception in encoding data
The Gestalt Principles or laws of human perception state that we, as humans, tend to group similar elements and recognize patterns. The Gestalt laws or principles describe how our eyes visually perceive visual elements reducing complex clusters of individual shapes and forms into a whole, unified scene.
Similarity (or Invariance): Relationships are automatically built between similar elements within a data visualization. This can be achieved by elements or properties of elements such as shapes, colors, size, and texture.
Continuation: The human eye is keen to achieve a visual flow. Therefore, it is susceptible to follow lines, curves, paths, and associations automatically. Sankey diagrams are one of many examples for illustrating this with data.
Closure (or Reification): We have a certain level of build-in OCD, so we actually need to correct visual cues into complete shapes to make sense of the world around us. This is heavily used in data to emphasize or highlight parts of a whole without explicitly illustrating that whole, leaving it to the reader to fill in the missing information. This visual illustrates this.
Proximity (or Emergence): This is also referred to as grouping. Basically, clustering elements together to create the perception of a group, part, or category.
Figure/Ground (or Multi-stability): Our eyes tend to isolate shapes from their respective backgrounds. Highlights and discriminating a certain data element versus the background is key for user experience.
Symmetry and order: The user needs to perceive an overall picture that is distinctive, has logic and proportionality. Users should not spend the effort to perceive the logic of the overall picture.
Five easy steps for choosing the optimal encoding for your data
Step 1: Data sketching
This may sound basic, but I believe it to be a must-have. Getting playful and sketching your data presentation will make your mind explore different possibilities and, most importantly, will give you the chance to systemize and arrange your data. During this stage, you can easily identify if the encoding will serve its purpose.
Step 2: Data real estate
There is a certain limit to how much our attention span can comprehend. This makes our computer displays and PowerPoint slide packs finite. We have a limited amount of space, and we must use it wisely. Encoding data is all about the best use of the space available. Where will your data visual live? What is the vehicle you use to present your data? Some options of encoding are not suitable for all mediums. Heavy data visuals and animations may not fit well with on-the-go presentations, using a projector, or having to print your results.
Step 3: Audience of your data
Your audience has a certain level of expertise in understanding the subject matter. Going beyond that level will disengage it. It also has a certain motivation for being attentive and a limited attention span. Given these, you must best adapt your data visual to fill all the needs of your audience. Do not overcomplicate things or use unfit 3D models, unnecessary visual effects, or clutter elements.
Step 5: Tone and style of your data presentation
What is the tone of your presentation? Can you afford to be playful? Can you afford to be express a certain style? Should the presentation appeal to emotion? Is this a quarterly revenue presentation that should have a serious tone or a consumer persona research presentation that should appeal more to storytelling? Is it wise to make it look like a highly polished piece of research? I remember being asked to downgrade a complex data visualization we spent weeks working on as it looked "too hi-tech," and the client, who had a budget restriction at the time, was concerned that this presentation might come across to the board members as being too expensive.
Toolkit for encoding your data
Lines, curves & dots
Data can be encoded using lines and dots to illustrate various trends and distributions.
Shapes & forms
Using shapes and forms can give an immersive sense of scale. Use suggestive elements as a great visual aid. You may use suggestive shapes like shapes of people, trees, houses depending on the data unit you need to showcase.
Lettering & typography
Writing can be creatively used to portray quantity, quality, length, and intensity. You may use the size of the text like in a word cloud, stack your text as a quantitative measure, or use text along with formatting and position to showcase clusters.
Color & texture
Color is used for symbolic and subliminal communication and has a huge potential to facilitate understanding. Using color and texture taps in the Gestalt Principles Law of Similarity. Imagine a chessboard. You can discriminate at a glance the "data" of each player.
Volume & quantity
Size and quantity can be creatively leveraged to convey importance and impact. There are lots of brilliant data visualizations that use this technique. We are hard-wired to be One example of this is Visualizing the Scale of Plastic Bottle Waste Against Major Landmarks featured by Visual Capitalist.
Placement, position & order
Positioning or placement of elements holds key information. It may mean that something is better, worse, or important (e.g., centerpiece).
Pointers & connections
Static images, pointers, and connections are powerful choices to illustrate associations between data.
Delimiters & containers
Most of the time, packaging counts and can be used to illustrate content, distribution, relationships, or size creatively.
Mapping & location
Mapping is a popular way to encode data. We are hard-wired to use pointers on geographic maps, heatmaps, or any other kind of map.