First of all, what is Data Visualization?

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According to Wikipedia, Data Visualization is defined as the graphical representation of information and data. It’s intended to communicate information effectively using visual elements, statistical charts, plots, and other tools. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in the data.

To put it another way, it's a way of representing data through visual elements that allow audiences to see essential data points without being overwhelmed. And this is a much-needed skill in this day and age.

Whether it's checking your phone, browsing the internet, or using your GPS, the technology that we use daily provides a wealth of data at our fingertips. But the raw data is too much for the average individual to handle.

We don’t understand what Latitude and Longitude data means by itself

That's why being able to filter and transform that data into a visualization is quickly becomes an essential skill for understanding. Very few people can make sense of Latitude and Longitude data moving in real-time, but most people can understand what a GPS shows, a visualization of what roads to take to get to the store.

Source: Photo by Brecht Denil on Unsplash

Likewise, it can be hard to track purchase data across multiple sites. But a credit card statement that visualizes how much you spent on different categories can help you understand if you’re over budget or not.

Data Visualization allows us to filter a massive amount of information into an easy-to-read format that can allow your audience to understand the key insights that you want them to gather from the data. It does this by leveraging different systems, such as vision, perception, and cognition, to present data in a way that allows your audience to turn the information that they see into knowledge.

But to understand how you can learn how to do this, let’s first talk about how people acquire knowledge.

The DIKW Model

Before you become too entranced with gorgeous gadgets and mesmerizing video displays, let me remind you that information is not knowledge, knowledge is not Wisdom, and Wisdom is not foresight. Each grows out of the other, and we need them all.

– ATTRIBUTED TO ARTHUR C. CLARKE (LATHROP, 2004)

There is something called the Data, Information, Knowledge, Wisdom (DIKW) model, which is a model of how people make sense of information.

Source: By Longlivetheux - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=37705247

It is a crucial concept that helps define the role of data and information and knowledge management. And the way that it works is by the creation of a data pyramid.

But to illustrate it more tangibly, let's use the analogy of wine.

Data:

The base of the pyramid is data in its rawest form. This data can be symbols, numbers, or other data that hasn’t gone through any refinement. It can take the form of what we perceive from our five senses or data readings from different technology.

To follow our example, these would be grapes of varying types, quality, size, and shape: nothing has been done to them yet, and they can exist in many different forms.

Information:

After that comes information. Information is data that has been structured, organized, or otherwise processed to have a specific relevance or context. This is often a necessary step to make data useful. For example, suppose you’re concerned with understanding how we compare with our competitors. In that case, we might choose several specific metrics from our company reports and compare them to another data source that shows how companies rank in our industry.

This process might be similar to sorting and processing particular grapes to turn them into either red or white wine.

Knowledge:

What happens after that is turning that Information into Knowledge. This step is a complicated and elusive process, as knowledge is often difficult to define. Knowledge is sometimes described as the synthesis of multiple sources of information, context, and personal experiences to provide a framework for incorporating new experiences and information. It is also sometimes understood as procedural information, as in the steps you might take to answer a question or perform a skill.

In other words, it's about how you as an individual can synthesize or organize information into a framework that allows you to make sense of information.

To clarify this, let's look at our example. Knowledge, in this case, can be understood as the experiences surrounding a particular wine. For example, what type of wine pairs well with certain foods, your limit for wine consumption, or the appropriate steps to taste test wine. It can also be tied to specific experiences: for example, you like this wine brand because it reminds you of your last trip to Italy.

Wisdom:

The last step is going from Knowledge to Wisdom. I won't spend much time on this because a lot of this gets into recent academic research.

Still, a lot of Wisdom involves understanding knowledge to the point where you can appreciate the ethical actions to take or how to apply them to a new context.

For example, you may pick out a specific wine for a friend you’ve never heard of or tasted before, but because it has similar traits to other wines that your friend likes. Or, you may know that you should only have this amount of plum wine, something you’ve never tried before, because of the limits you have with red wine.

This model provides a lot of understanding of the process that occurs to turn raw data into knowledge or even Wisdom, which aims to create data visualizations. Climbing the pyramid can also provide many insights at each step.

But this model also shows places where we can run into problems if we fail to take each step correctly. There could be undetected errors in the Data or misinformation as you try to structure Information. Knowledge could be based on hearsay, or you might have unfounded beliefs based on Wisdom.

As we climb the data pyramid, properly addressing each step will allow us to create compelling visualizations that can provide great insights.

So this model will serve as the structure of the book.

Structure of the book

In the first section, I'll review my time working with Data Science. I'll talk about the importance of structuring data to turn it into Information by creating hypotheses, the importance of metrics for data analysis, how data quality can be affected by little choices we make on the design side, and techniques for improving productivity.

I’ll then talk about learning through Journalism and Psychology. I’ll talk about the idea of telling stories, how visualizations can be planned and predicted through visualization wheels and questions, and the process that journalists go through to create stories that try to bridge the Information and Knowledge gap.

Then, I'll wrap up by heading back to UX. I'll apply my UX background to the lessons I've learned to try and gain to try and improve upon the process and make acquiring Knowledge (and perhaps even Wisdom) easier for your audience.

I'll talk about using UX techniques and approaches to make sure that the story you tell targets your audience and uses their existing mental models to get them to know or do something. In this way, we can approach Knowledge and Wisdom.

By doing this, I hope to illustrate a way to think about data in your daily life and provide lessons in turning that data into a compelling and effective visualization.