Data Visualization

you might also use pie or doughnut charts to highlight important categories to direct attention or tell a narrative. For instance, highlighting that global online sales are higher than regional sales for each other country, combined.

This use of color emphasizes how online sales make up more sales than any individual store, combined. We could show the same thing in a bar chart, but it takes up more space and might be less effective.

The previous example also highlights why “data storytelling” does not easily apply to most Power BI reports. You can craft a data story with your visuals, but ultimately, the user is the storyteller.

It is easy to show a screenshot that looks nice, but it is another thing entirely for the report to remain useful after interaction. We will leave that discussion for another, future article.

Despite the bad reputation of pie charts among data or design professionals, many people often still opt for pie charts in their reports for various reasons, like the following:

Familiarity

Space

The biggest problems with pie and doughnut charts are accuracy and precision. While it is easy to quickly identify the biggest and smallest slices, it becomes more difficult with more than three categories or when slices are similar in size.

Typically, when you have more categories, you focus on the top few and group the rest into “others” in a single slice. In Power BI you can do static grouping and binning, but unfortunately, the dynamic “TopN and others” pattern is typically too complex for the average user to implement or maintain.

If you need a deep guide specifically about color in data visualization, then we recommend this article on Datawrapper by Lisa Muth (which is tool-agnostic).

If the purpose of the chart is to evaluate magnitude and compare categories, then a pie chart is not a good option. You generally opt for pie or doughnut charts and other part-of-whole visuals when your focus is on answering data questions about proportionality.

Note that if you are truly interested in the evolution of proportionality over time, you might also consider a stacked area chart or 100% stacked area chart, too (although stacked bars work with discrete data like yearly trends).

As our world becomes increasingly data-driven and technologies like AI, the Metaverse, and the decentralized web gain momentum, pushing interactive data visualization to the next level is crucial

As humans, we have always had the urge to chart the world around us. This urge has pushed us to improve the way we collect, process, and communicate information throughout history.

Interactive scatter plot example: Demo by John Tukey & Marian Fisherkeller about Prim9, a first for computer-assisted data visualization, recorded in 1973

However, most data was only locally stored and not accessible to the general public unless (occasionally) translated to existing communication channels, like newspapers and tv shows.

Software was developed with experts on specific sectors in mind, and therefore the user base was limited. By this time, the focus was on advancing technology, not thinking much of usability, and the questioning of the ethics of software was barely acknowledged by society.

The amount of digital information started growing considerably. However, even if we found ways of building and manipulating digital information, sharing it was still restricted in most parts of the physical world (we either printed it or stored it in drives that we could then carry to other computers).

A computer requires users, not watchers”: Even if the technology wasn’t there yet, we already acknowledged that sharing information (in educational, leisure, and business contexts) had a greater impact on effective interaction.”

We had growing data, improving software, and better processors, and the mouse became a standard part of a PC setup. Thanks to the many advances and improved affordability, using a home computer became popular and led to millions of new users.

Data roles are often the first subject I discuss when building data literacy

This communication becomes a survival tactic if you are one of a few data folks in an organization

Here’s an analogy that stresses differences between data roles and elucidates the broad ecosystem needs for data development:

It is impossible for one person to build a college dorm, populate it, manage it, work with the students, tell their stories, and research the impact.

In a business setting, delivering key messages and making insights clear take priority. Most often, simple and focused graphs will best accomplish this goal.

In a business setting, delivering key messages and making insights clear take priority. Most often, simple and focused graphs will best accomplish this goal