I have seen various versions of this image used as memes in my favorite subreddits, r/dataisbeautiful. The image originates from the first four steps to a visual created by Hot Butter Studio by co-founders Brandon Rossen and Karyn Lurie. I love this image for its great visualization of the importance of showing more than just visualizations. 

At the top of the image, there is a pile of mixed LEGO bricks, and it is titled “Data.” As you move down, the bricks are increasingly built to represent how data is sorted, arranged, and presented visually. All while the bricks are looking more and more organized. The final panel is an entirely constructed LEGO house labeled ‘Explained with a story.’

Successful data storytelling does not end once you have collected or reviewed the data. That is just the beginning. Today, most organizations collect many data. Like LEGO bricks, data comes in various forms and can be used to build all kinds of things. Leaving data or LEGO bricks in their raw form does not serve a purpose other than collecting dust and taking up space in the garage. Combined, they begin to transform into something meaningful or valuable. 

“A single lego brick has almost unlimited potential. As each additional brick is combined with the first brick, its complexity increases, but so does its value.”

– Matthew Roche

You can remove non-LEGO items or broken LEGO pieces from the pile during the data preparation or cleaning process. Before you can use the data you have collected, it must go through a similar process of cleansing, organizing, and combining. A significant amount of time and effort can be spent just making data usable before it can be visualized, analyzed, and turned into data stories. Depending on what you are attempting to build, you may need to combine LEGO pieces from more than one LEGO set (Dykes, 2021).

Your data will only continue to grow.

You must know what you are building when you build a LEGO creation. In the analysis process, when you identify a crucial observation or insight, it is similar to these subassemblies or subcomponents that form a part of your desired LEGO structure. Most of the time, with LEGOs, you are building something for yourself. However, there are situations when you might construct something for someone else, such as a sibling, parent, or friend. In contrast, when we perform data analysis, we most often perform it to benefit others—our manager, team, department, company, etc. The more you know about your key stakeholders’ interests or needs, the more targeted your analysis can be, and the more valuable your insights will be. Understanding the business goals or problems can guide your analysis and prioritize which insights you focus on—and, ultimately, what goes into your data story. You can build anything with LEGOs, but having a target audience in mind will focus your efforts (Dykes, 2021).

Going from raw data to a data store is a journey!

Rather than storing the assorted LEGO pieces in a random pile, it is better to organize them by color, shape, size, or function. Organizing the LEGO bricks methodically by size, shape, function, and color makes it easier to determine what you have to work with, and you can quickly pinpoint the bricks you need as you build. Similarly, raw data tables will not be as valuable as reports with data charts and graphs that provide better visual context once you have clean data. Data visualization can help you see the data more clearly and explore the information to find potential insights. 

Data, I Hardley Knew Ya

Observations and insights will be incomplete if they do not have an overarching narrative that binds them together. When you add relevant context and meaning to the numbers, your audience will be engaged and enlightened by your insights (Dykes, 2021). As you explain your insights with data stories, you better prepare the audience to make informed decisions and take action. Data storytelling is the final step at the end of a multi-step process. The quality of your data stories will depend on what happens at each preceding step. 

Disclaimer: The ideas and opinions expressed in this blog post are solely my own and are not representative of any current or previous employer. Any similarity to any actual company, organization, or individual is purely coincidental. This blog post is written to provide general information and does not intend to violate any non-disclosure agreement or confidentiality agreement.

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References:

Dykes, B. (2021, September 9). A deeper dive into Lego bricks and Data Stories. RSS. Retrieved September 21, 2022, from https://www.effectivedatastorytelling.com/post/a-deeper-dive-into-lego-bricks-and-data-stories

R/dataisbeautiful – [OC] the Lego data story, adapted from original image by Monica Rosales Ascenio. Reddit. (n.d.). Retrieved September 21, 2022, from https://www.reddit.com/r/dataisbeautiful/comments/pexts2/oc_the_lego_data_story_adapted_from_original/

Roche, M. (2018, December 8). Lego bricks and the spectrum of data enrichment and reuse. BI Polar. Retrieved September 21, 2022, from https://ssbipolar.com/2018/12/05/a-spectrum-of-data-enrichment-and-reuse/

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