A Deeper Dive into LEGO Bricks and Data Stories

Recently, Andreas von der Heydt, Merchandising VP at Chewy, shared an image on LinkedIn that generated a lot of buzz about data storytelling. The image compared raw data to LEGO bricks and a fully assembled LEGO house to a data story.

I had seen a variation of the same image several years ago. I was able to isolate the origin of the first four steps to a visual created by Hot Butter Studio co-founders, Brandon Rossen and Karyn Lurie. Their image focused on infographics and was meant to be read as “an infographic is data sorted, arranged, and presented visually.” Von der Heydt cropped out the infographic part and then added a fifth step with a LEGO house (Creator 5198 Apple Tree House) and the caption, “Explained with a story.”

While I like the overall concept, there are a couple of minor flaws and a major omission that must be addressed to better represent the process of moving from raw data to a data story. I do believe Von der Heydt was on to something with his analogy as it clearly resonated with many people. As a LEGO fan and an advocate for data storytelling, I felt duty bound to re-examine the analogy and develop it a little more.

With my re-interpretation of the analogy, I went a slightly different direction but retained the same number of steps. Here’s a quick summary of my five steps:

The journey of going from raw data to a data story is a process. Successful data storytelling doesn’t begin at step five—it begins right at the beginning with the data you collect. Let’s start building a deeper understanding of these crucial steps by reviewing each one in more detail. 

Note: All of the visuals in this post were based on LEGO Creator Small Cottage set (271 pieces).  

Today, most organizations collect a lot of data. Similarly, over the course of multiple birthday and holiday gifts, a household can accumulate tons of LEGO pieces. Like LEGO bricks, data comes in various forms and can be used to build all kinds of things. If you leave data or LEGO bricks in their raw form, they don’t serve a purpose other than to collect dust and take up storage space. It’s only when they’re combined that they begin to transform into something meaningful or useful.

Similarly, you’ll have data from a wide variety of source systems in your department or organization. Whether the data remains siloed in these systems or aggregated in a data lake or data warehouse, your pile of data will continue to expand over time.

Rather than storing the assorted LEGO pieces in a random pile, it’s better to organize them by color, shape, size, or function. During this process, you can remove non-LEGO items or even broken LEGO pieces from the pile. Depending on what you’re attempting to build, you may need to combine LEGO pieces from more than one LEGO set.

Before you can use the data you’ve collected, it must go through a similar process of cleansing, organizing, and combining. A significant amount of time and effort can be spent on just making data usable before it can be visualized, analyzed, and turned into data stories. 

Now, at this point, you could go rummaging through these sorted piles of LEGO bricks and start to create something in an ad-hoc fashion. However, it will be time consuming to comb through the bricks even when they have been organized into piles by color or function.

In this image, I have organized the LEGO bricks in a more methodical manner by size, shape, function, and color. For example, I have put all the windows and doors at the top next to each other, and all the slanted roof pieces in descending order of size. The bricks have also been spread out so it’s easier to determine what you have to work with, and you can quickly pinpoint the bricks you need as you’re building.

Similarly, once you have clean data, raw data tables won’t be as useful as reports with data charts and graphs that provide better visual context.