Intro to Designing with Data

Data Visualization Guidelines

Learning to See Through Data

Fundamentally, a datum is one dimensional and bound by its form — a word, image, genome, spreadsheet cell or note of music. Broadly speaking, in this form, it is meaningless.

So how can a datum be “dimensionalised” to create meaning? How does it become relevant, actionable and monetized; especially in a world where much of it is accessible, free and growing at an incalculable pace?

The unearthing of relevance and dimensionality comes when data assimilates with other data. It begins to form recognizable patterns and messages — no different than musical notes on a score that guide a symphony. Data visualization is the transformational engine that provides the transliteration between knowing and understanding: knowing being based on facts and understanding based on confidence and certitude.

Data visualization acts as the interface between two information systems: the human mind and the computer. Both systems transform data into wisdom (knowing into understanding) through visual cues, interactivity and the real-time utilization of information systems. Data visualization enables the human mind to efficiently interact with large volumes of data — discerning hidden characteristics, patterns, anomalies and trends within dynamically changing information spaces (real and virtual).

Regardless of its form, data visualization fundamentally sets out to help users validate the expected and discover the unexpected. It is often the connective tissue between an original event and subsequent activities known as a “chain of causation.” In practice, data visualization can influence our conscious and subconscious without much intervention of language, transmitting massive amounts of structured and unstructured information across geographies and cultures.
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Data visualization objectives

Our objective is to realize the hidden value that resides in vast pools of data (essentially accelerating meaning). This is not about dumbing down information. We are interested in discovering ways to make data actionable for our business and the businesses of the clients we serve. The process of understanding or revealing the new and undiscovered data requires us to “see deeper,” beyond more than a simple glance at an image or chart. It requires in-depth interrogation and context that will come as we employ new and innovative data visualization and interactive techniques.
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The problem with canned graphics

Commonly used tools and platforms offer an abundance of charts and graphs that can be applied quickly and efficiently. These methods, while convenient, are little more than signposts. The canned graphics to which we have grown accustomed often do not afford the user the ability to frame a problem, challenge an assumption, customize or refine.

If data is meant to accelerate meaning, personalized exploration of that data is an unyielding requirement. Canned graphics will lead to inadequate and often erroneous results. Data visualization is meant to open eyes, not reduce exploration. We have the ability to use data in a way that will reflect our world and help us understand it more succinctly. Data empowers the narrative, and the narrative (storytelling) is the mechanism that has emboldened humans for centuries.
 
This chapter in the Digital Design System introduces standards and best practices for the creation, consumption and distribution of data at Aon. You’ll find information on how to select the right chart type for your data, detailed visual style guidelines and best practices.
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Consider the following questions as you create a data visualization:

What are we revealing, or story are we telling, through a data visualization?

Data visualization cannot simply be a display. It has to solve a problem or expose an opportunity.

Does your proposed data visualization motivate action?

‍Data visualization is more than displaying charts and graphs, it’s about making data publishable, consumable and actionable. Consider a data visualization a call to action.

For whom are we visualizing data?

‍It is essential to understand for whom a data visualization is being generated and the way they prefer to consume that data.

Data-Ink Ratio

The data-ink ratio is a concept introduced by Edward Tufte, the expert whose work has contributed significantly to designing effective data presentations. In his 1983 book, “The Visual Display of Quantitative Data,” he stated the goal:

‍Above all else show the data.
‍Tufte, 1983

 A large share of ink on a graphic should present data-information, the ink changing as the data change. Data-ink is the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented.
‍Tufte, 1983

Tufte refers to data-ink as the non-erasable ink used for the presentation of data. If data-ink would be removed from the image, the graphic would lose the content. Non-data-ink is accordingly the ink that does not transport the information but it is used for scales, labels and edges. Non-data-ink refers to all visual elements in charts and graphs that are not necessary to comprehend the information represented on the graph, or that distract the viewer from this information.

The data-ink ratio is the proportion of Ink that is used to present actual data compared to the total amount of ink (or pixels) used in the entire display. (Ratio of data-ink to non-data-ink).

Good graphics should include only data-ink. Non-data-ink — the visual elements in data visualization that are not necessary or distract the user — is to be deleted everywhere where possible. The reason for this is to avoid drawing the attention of viewers of the data presentation to irrelevant elements. The goal is to design a display with the highest possible data-ink ratio (that is, as close to the total of 1.0), without eliminating something that is necessary for effective communication.