Part Three: Using embedded web objects and viz extensions
This is the final installment of a three-part series that walks you through my recently built dashboard. Title of the dashboard is called: Juan Gabriel-Emotion, Sound, and Legacy. This visualization takes a deep tour in the career of Mexican idol Juan Gabriel analyzing his career and song portfolio. Throughout this project, I not only learned about the genius of ‘JuanGa’; I was able to peel the layers of the surface to fully understand how his music resonates with listeners for more than five decades.
Below is the breakdown of the three-part series:
- Dashboard design:
- Visualizing your template and crafting your story
- Scatterplots:
- Focusing on song mood and assessing song quality
- Viz Extensions & Embedded Web Objects:
- Linking vizzes together to build an overall picture
If you’re new to Juan Gabriel, you’re about to discover an artist whose music may quickly earn a place in your favorites.
Born Alberto Aguilera Valadez in Michoacán, Mexico, Juan Gabriel overcame early hardship after relocating to Ciudad Juárez, where his creativity, resilience, and hope for a future took shape. From a young age, he was writing songs, later becoming known for his rare ability to blend vulnerability and emotion into unforgettable performances.
As a Mexican-American with family roots in Juárez, this project is deeply personal. Juan Gabriel’s music was a constant in my home growing up, and the Netflix documentary Juan Gabriel: I Must, I Can, I Will further inspired me to explore his genius through data.
What truly set Juan Gabriel apart was his extraordinary ability to convey deep emotion through music that was often upbeat and high-energy. While high-tempo songs are typically associated with celebration, he infused them with vulnerability, longing, and emotional weight. This contrast—joy in rhythm, pain in expression—became his signature and allowed his music to resonate across generations. More than five decades later, those songs still connect because they reflect emotions that are timeless and deeply human.
Tableau Viz Extensions
Viz Extensions are Tableau add-ons to your development tool belt. These are custom chart types that are built by web developers that allow for visualization builders to create complex visuals within the Tableau application. Tableau has an extensions API that allows developers to use web technologies like HTML, CSS, Javascript and through this connection developers can leverage these extensions to behave natively in the application.
There are some different and/or complex chart types that can be built with some popular hacks, some complex data science functions and a ton of calculations. But sometimes this portfolio of work comes with some limitations. With visualization extensions, some very bright developers have built these packages to do the heavy lifting for you.
One more time for the folks in the back: These great developers have built these tools to do the heavy lifting for you! Immediately breaking the ceiling for what you can do in the application.
As a developer why does this matter to you?
It allows you to have more options than the native chart options that Tableau has in its versions. Think of it like working construction with a hammer, some screwdrivers and a hand saw. These extensions give you a power drill, nail gun and an electric circular saw table. It makes your framing more precise and allows you to finish the job quicker allowing for assessment of quality. When working on a Tableau visualization the introduction to these viz extensions allows you to limit the amount of ‘tech debt’ you create through your viz development. Including these in your repertoire allows you to tell your story in different ways and makes your work memorable. For today’s chat, I will be highlighting my use of the Voronoi tree map via the viz extension created by LaDataViz.
LaDataViz is one of the top creators in the Tableau viz extension space. They bridge the gap between what data tells you and the story you tell via web based charts.
Please visit them here.
Using Voronoi Tree Maps
In my latest Tableau visualization, I wanted to get into the insights of just how great Juan Gabriel was as a performer, song writer, and singer. His portfolio is unique where he has a songwriting touch on over 94% of all his studio albums. An amazing distinction in a time where most famous singers rely on a steady group of songwriters to compose their hits. In the visual below I use Voronoi tree maps to show the distribution of Spotify song play for each of Juan’s albums.
In the above photo, circled in purple is the Voronoi tree map that I was able to leverage via LaDataViz. This type of visual allows me to show the distribution of Spotify plays from each of Juan’s studio albums. This is a pleasant difference compared to a regular box/square tree map.
Lets take a closer look the chart below:
The image to the right visualizes the 1983 studio album, Todo by Juan Gabriel. This chart is intriguing to me because its different than a box tree-map. I love the unique shapes derived from this visual. Along with some created highlight dashboard actions you can hover each part in the Voronoi and see the same song in the adjacent visuals around it.
Embedded Web Objects
I would not do a Juan Gabriel dashboard justice if I could not display the genius of his work via Spotify music. Leveraging embedded web objects I am able give front-end users that gift of Juan Gabriels music. In the above dashboard page screenshot you can see the embedded spotify preview widget. This allows my dashboard to communicate with Spotify and display an audio preview widget.
Below is a view of the Spotify widget in this blog:
The above widget gives you an immediate look into the top tracks of Juan Gabriel. The widget in the dashboard is a direct link to the respective album selected, so you are able to preview every song in the album for a snippet of the song.
Using the dataset I created, I was able to collect all respective Spotify embed URL codes for each of his studio albums. With the collaboration of a parameter action, dashboard filter action and a web url action; you are able to interact with the Juan Gabriel studio timeline I created and dynamically change to show a respective widget for each studio album.
I love music! I mean I really loooove music. Learning this capability with Tableau Public has allowed me explore different things that incorporates music.
Click here to read the original post by Shreya that walked me through this capability.
Closing Thoughts
Part three highlights how Viz Extensions and embedded web objects can expand Tableau’s capabilities without adding unnecessary complexity. Instead of relying on heavy calculation workarounds, these tools keep dashboards cleaner, more flexible, and focused on insight.
The Voronoi tree map from LaDataViz introduced a more expressive way to show distribution while reducing technical debt. It allowed the visual to support both clarity and emotion—something that’s difficult to achieve with standard chart types alone.
Embedded web objects added an immersive layer by linking analysis directly to the music. By allowing users to listen while exploring the data, the dashboard became more interactive and human.
Together, these tools demonstrate that extending Tableau thoughtfully isn’t about adding flash—it’s about telling better, more memorable stories with data.
His music didn’t just connect emotionally—it became part of life’s soundtrack for millions, accompanying weddings, family gatherings, and personal milestones across decades. The emotional authenticity in his songs, whether joyful or heartbreaking, helped break cultural norms around male vulnerability in music and gave voice to feelings many listeners had never seen expressed so openly.