Which Visual Story Do You Want to Tell?

Visualization has existed for thousands of years, but how and when we use data to tell our visual story is still in its infancy. While researching about data types and what they mean, I stumbled upon a Harvard Business Review article. In Visualizations That Really Work, author, Scott Berinato provides a reminder before he begins “Know what message you’re trying to communicate before you get down in the weeds.” (Berinato, 2016)

Okay, let’s say you follow Berinato’s advice and you know who your audience is. You think, “Great! I can jump right in and create a visual that will really show the audience how those data points all relate. It’ll be easy.” Well, not so fast. Although the data visualization field is relatively new, there are some basic parameters to creating meaningful data visuals. It begins with a couple of simple questions:

1. Is the information conceptual or data-driven? – Daniel Mullins in The 4 Types of Visualisation and the Role They Play in Market Research states in simpler terms “are you looking at qualitative or quantitative information? Ideas or statistics?” (Mullins, 2017).
2. Are you declaring something or exploring something?

The two questions above form a simple 2×2 grid which shows your data visualization can fall into one of four quadrants; conceptual-declarative, conceptual-exploratory, data-driven-declarative, or data-driven-exploratory (see image below). With this defined, let’s look a little closer at each of the quadrants to understand them better.

Image Credit: Tony DeRose

First up is conceptual-declarative or idea illustration. This type of visualization is used to convey complex processes into a more simpler concept so the audience can comprehend the relationships within the concept.

In the example below Nir Eyal’s Hooked Model conveys the idea of how good companies ultimately create a never-ending loop to keep you coming back for more of their products, services, etc. Triggers are given (either internally or externally) which cause the consumer to take action. This action has an award associated with it which then gets the consumer to invest and want more or to keep returning. While the words above describe what the Hooked Model is, simply looking at the visual below tells the whole story more quickly than words can. Idea illustration per IBM’s What is Data Visualization goes on to say “It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence…” (IBM, 2021).

Image Credit: Tony DeRose

Next up is conceptual-exploratory or idea generation. This is a “think tank” type visualization. This could start out with a simple question someone is trying to answer or something that involves an entire team. The idea on the back of a napkin, collaborative whiteboard iterations by a team are examples of the type of idea generation. The goal is to solve a problem, answer a question, come up with a new innovation. One of the most common of these illustrations is the mind map. Below in my mind map, I show how humans and AI together can be used. On the left side I show the current and future state of AI advancement. On the right side, I show where AI can be applied thus benefiting more efficient use of time and resources.

Image Credit: Tony DeRose

Third is data-driven-declarative or everyday dataviz. This type of visualization is more of your day-to-day charts and graphs. Think sales performance data or customer retention quota data. These types of visualization can typically be found in reports presented to upper management where time is of the essence. Therefore, the data storyteller has the job of providing context and affirming the data in an easy to digest chart or graph. The Coronavirus pandemic is an excellent example of data-driven-declarative visualization. The world was in crisis and experts needed to assure and affirm what was happening with regard to number of cases, mortality rate, affected regions, etc. To this day Our World in Data maintains an ongoing slue of data about COVID-19. Below is an example of a visualization for case fatality rate between September 2020 and June 2024 among the United States, France, Canada, Germany, India, and the United Kingdom.

Image Credit: Our World Data website accessed 21-Jul-2024

The last type is data-driven-exploratory or visual discovery. This is probably the hardest quadrant due to the complexity. This is where the data is big data and can even be dynamic. If you’re answer to the two original questions lead to this quadrant, then you are doing more data analysis, spotting trends. You’re probably in business intelligence or a data scientist. Here you can either use your visuals to confirm or explore a theory. Using the Our World in Data site again, I chose to look at the monthly CO₂ emissions commercial airliners emit. This large dataset would look dull and not convey well in table format (1st image) nor what you be able to see the trend that month over month Oceania countries emit the most CO₂ (2nd image).

Image Credit: Our World Data website accessed 21-Jul-2024

Berinato, S. (2020, September 14). Visualizations that really work. Harvard Business Review. https://hbr.org/2016/06/visualizations-that-really-work

Eyal, N. (2023, November 2). The hooked model: How to manufacture desire in 4 steps. Nir and Far. https://www.nirandfar.com/how-to-manufacture-desire/

Mathieu, E., Ritchie, H., Rodés-Guirao, L., Appel, C., Giattino, C., Hasell, J., Macdonald, B., Dattani, S., Beltekian, D., Ortiz-Ospina, E., & Roser, M. (2020, March 5). Coronavirus (COVID-19) cases. Our World in Data. https://ourworldindata.org/covid-cases

Mullins, D. (2020, February 5). The 4 types of visualisation and the role they play in market research. B2B International. https://www.b2binternational.com/2017/08/11/4-types-visualisations-role-play-market-research-process/

Ritchie, H., Rosado, P., & Roser, M. (2023, December 28). CO₂ and greenhouse gas emissions. Our World in Data. https://ourworldindata.org/co2-and-greenhouse-gas-emissions

What is data visualization?. IBM. (2021, September 28). https://www.ibm.com/topics/data-visualization