Essence and principles of Data Visualization in Storytelling Journey..
By: Kamal Mishra
Tags: Exploratory Data Analysis, Artificial Intelligence, Data Science, Data Visualization

Principles are key to create better impact using Visualization in Data and AI

With a plethora of data getting into the mainstream of business and everything we do, “analysis of data” and “analysis of relevant data” is getting critical for success. Data Visualization and Exploratory Data Analysis (EDA) are extremely vital in the end to end journey of Data Science journey. It helps from a storytelling perspective to convey the outcomes/results in a simple to understand manner.

The AI for consumers is different — this could be photo analysis, smart speakers, user-related behavior analysis, etc.

The AI for business is different — how do we automate, optimize, have better predictions, and so on.

Writer Jon Westenberg has stated that “Storytelling is the greatest technology that human ever created”. When we relate our stories leveraging visualizations, it’s powerful and easier for our brain to capture, interpret and understand.

Photo by Kalea Jerielle on Unsplash

Data Visualization: what is it?

General understanding amongst business users/analysts about “Visualization” is regarding the graphical presentation of information, intending to provide the viewer with a qualitative understanding of the data. The graphical representation may entail manipulation of graphical entities (points, lines, shapes, images, text, etc.) and attributes (color, size, position, shape, etc.)

Disclaimer: image representations are illustrative only & may not correlate

Data Visualization: why is it important?

It’s all about vision and the way we look at data.

“Computers are good with data. Humans are good with pictures.” Trends, patterns, exceptions are all easier to spot if we look at pictures rather than tables of data. Here is a classic example around this theme below. Anscombe’s quartet explains that.

Anscombe’s quartet represented for understanding of the context here

What is it used for business context?

There are two specific dimensions — Visual analysis and visual communication.

The visual analysis uses our innate visual advantage for processing information. It spots a pattern in a large amount of data, sees things that are not obvious otherwise. It interacts with data and determines actionable insights — What happens if we make adjustments to that area? and so on.

Visual communication is about the presentation and communication of data in an intuitive manner. It simply conveys information, so we all see and interpret the same things. It guides the end-user to focus on the important aspects of data. This is something that effectively represents large amounts of data in a relatively small area.

Any Data Visualization needs to start with the audience in mind. The “What would you like to know?” needs to be followed up with “What would you do if you knew this information?”

Rules of Visual Perception:

Visual Perception follows certain rules:

– Our brain does not process all the light that enters the eye

– Our eyes are drawn to a familiar pattern. We “see” what we expect to see

– We see things about it’s surroundings

If we can utilize the rules of visual perception, we can be more effective in:

– Analyzing data to find something

– Putting the “story” across to the consumer of data

Three primary sets of rules:

a) Edward Tufte’s rules

b) Gestalt Principle of Perception

c) Grice’s Maxim of Communication

If we disobey the rules, our data will be misleading or incomprehensible.

We see what we expect to see:

Our Perception:

Memory plays a role in our cognition:

Tufte’s Principles:

Edward Tufte invented the concept of Chart Junk. His most famous work “The Visual Display of Quantitative Information” was published in 1983. It is probably the most important book ever written on Data Visualization.

Per him, five key principles of Data Visualization are as follows:

a) Graphical Integrity: Visual representations of data must tell the truth.

b) Data-Ink Ratio: Data Ink is the ink on a graph that represents data. Good graphical representations maximize data-ink and erase as much non-data-ink as possible.

c) Avoid Chart Junk: Chart Junk is the excessive and unnecessary use of graphical effects in graphs.

d) Data Density: Proportion of total size of the graph that is dedicated to displaying data. Maximize data density and data matrix within reason.

e) Small Multiples: Series of the same small graph repeated in one visual. Small multiples are a great tool to visualize large quantities of data and with a high number of dimensions.

Graphical Integrity:

Data-Ink Ratio:

Chart Junk:

Gestalt Principle of perception:

A group of researchers in the early 20th century, known as Gestalt psychologists formulated the regularities according to which the perceptual input is organized into unitary forms.

Grice’s maxims of polite conversation:

Paul Grice, a 20th-century philosopher, is well known for his conversational maxims, which attempt to describe characteristics of polite conversation.

These maxims of conversation apply equally well to Visual Communication of quantitative information in the workplace.

To sum it up, presenting data is critical and hence some of the best practices and following standard rules help effectively drive solutions. This creates a better impact on our storytelling journey. We use this significantly while solving problems in Data Science and AI. Thanks for reading!! All the best on the Data Visualization journey.