The Ballad of My Viz Collaboration with Kevin Flerlage (according to me)

Adam Mico
12 min readApr 25, 2021

A weekly blog about the ‘data viz’-making process, #datafam / data analytics member interviews, & entertainment for introverts (consisting of a binge bite and music morsel).

The Ballad of Kevin and Adam’s Viz Collaboration

Select the image to access the interactive visualization

Do You Want to Know a Secret?

In the 2000s, I used to write music reviews and also ran a couple of music sites. One of my favorite activities outside of reviewing music was generating lists of favorites from an artist or year. Recently, I found an old list I made in 2008 of Beatles songs and album rankings.

Knowing my good friend (and really a great buddy to the entire #DataFam), Kevin Flerlage (Twitter | Site) was a huge Beatles fan, I thought I’d share and also asked what his favorite Beatles song was. He listed several, but couldn't say for certain. I wondered if my tastes changed in the many years this list was generated. At that point, a lightbulb flickered in Kevin’s head that we should do a collaboration exploring every Beatles song. This resulted in our thought bubbles colliding — this sounded like a lot of fun and we were excited to begin this exploration!

I Should Have Known Better … our data coordination

The 1st order of business was to determine what songs we would listen to and rate, how we would rate them, and how best to coordinate our resources.

Early on, we decided on using a Google Sheet for collaboration (1, your copy is below). This way we can make our updates in real-time, take notes as needed, and create new fields or columns to use in the viz.

I pulled songs from Wikipedia, cleaned them up in Excel, and back and forth — we went to add the year, albums, etc. We also decided that we only wanted to rank songs released on albums, or non-album singles while the band was releasing music as an active band between 1963–70 — this excludes unearthed songs released later on released on deluxe versions of records or compilations. We were left with 202 songs to listen to and rate.

We then determined we would make a song rating, based on our own tastes from 1–100. Quickly, I considered a rating of 50 for him and a 50 for me would be two different things (because we’re two independent thinkers… Kevin and Ken even think differently). How do we add context to that?

I’ll Follow the Z-score

The z-score compares what your rating is and what it actually means with context. I thought that would be a good idea for us because I quickly noticed Kevin was a stricter scorer. This was verified in the end also, as I would average 61.63 of 100 (higher the better) and Kevin was 50.51. A ‘57’ score from Kevin would be higher than the average for him, but lower than average for me. The z-score simply looks at the score given to an item, scans your average score, and divides by your standard deviation (we just used the standard deviation function on Google Sheets to calculate). The z-score’s result will show that his 57 was a slightly above-average song for him, but a slightly below-average song for me (and not the same score with the added layer of factoring standard deviation). It effectively makes an apples to oranges comparison, an apples to apples comparison.

Rank Naturally

Besides z-score, we wanted to determine a true rank. Since we had scored items 1–100, there are some ties due to ranking 202 songs. To have an actual ranking system, with no ties, Kevin suggested (correctly) we needed to manually break them (or instead of a 1–100 rating system, just do a 1–202 ranking system).

All You Need is Music (and Design)

For the next week or so, we continued binging Beatles songs. I listened to them in alphabetic order and Kevin primarily listened to albums. We chatted back and forth quite a bit about the songs and far exceed our goal of five songs rated per day. Not only did we love listening to the group again, but we also itched to get started on the design.

Header King

The 1st order of business was the header. Kevin proposed that we could get our faces as Beatles or something along those lines, but feared it may be a bit cheesy. He also suggested he could ‘volunteer’ his dad, Mike, to photoshop our pics with the Beatles somehow. Again, he wanted to make sure it wasn’t too silly. As a fellow cheeseball myself (and curious to see how our melons would look like a Beatle), I was all-in. We decided quickly that I was John and Kevin was Paul. We took fresh photos and shortly Papa Flerlage put together the image featured in our heading. This even made us more excited to get going on this viz — Mike’s work was brilliant! Kevin then added the pieces in PowerPoint (3) to compile a Beatle-ful top.

Being the Benefit of Texting Mr. Kite

Text can be a pain point in Tableau (4). To use any font you want you can go offline and take an image of it and add it to your Tableau viz. I looked long and hard for five minutes to discover a free for personal use font perfectly encapsulating the Beatles — Bootle Font.

Also, check out Steve Wexler’s (Twitter | Site) (5) fun use of the typeface (we stand corrected) as shared on Twitter.

(Not-So) Mean Mr. Mustard — The Color of the Viz

Kevin also suggested a bold background color for the viz. I knew that an era-specific color would be unique and may also provide a groovy slice of the 1960s. Kevin’s maize-yellow was certainly bold but when paired with the image and appropriate colorations, it could look spectacular. We also need to determine what colors represent our measures.

It made the most sense to draw inspiration from the garb our Beatle likenesses were draped in, so originally it was blue for Kevin and green for me. Although this worked conceptually, the green didn’t pop as much as I would have liked off the background, and knew the reddish pink would look magnificent against the maize.

For those hex code scoring at home, my color is hex #e1544d (faux-Google Chrome red), Kevin’s is #3d9ac4 (or faux-prilly blue), and the background is #f5c841 (faux-Crayola maze).

Chart Together

Kevin proposed we use a connected dot plot (AKA dumbbell or AKA barbell chart) to compare rankings (1–202) and he used parameter sorting to make it much more fun to explore when people want the chart sorted by different options (6,7).

We knew since we wanted to show all songs at once on the viz, the dashboard would be super long. The challenge was how are we going to fill the rest of the space well. Initially, we decided on the columns for the viz. Column A would be my side, Column B would be the connected dot plot. Column C would be Kevin. After a false start with three columns, we basically decided simultaneously that two columns (in a vs. viz) would work so much displaying the data.

As shared earlier, Kevin was building a variety of different charts (during a chart storm or a chart brainstorm) for the other portion of the viz. He had so many and my favorite was his vs. singer analysis scatterplot (see sample below), but nothing else worked as well from my perspective as a box and whisker plot when comparing a lot of records with but also easily visualizing their mean and quartiles (with the ability to dig deeper by hovering over each dot). Kevin felt that the box and whisker charts were the best route to navigate the 2nd column with and also was perfect for all the ‘vs.’ analysis using the z-score described earlier.

Kevin’s cool vs. scatterplot that didn’t make the viz.

I shared my version of the box and whisker and he tinkered with it a little (I prefer a slightly lighter box, but the version that made it on the viz looks great due to its contrast) and we decided to use that charting for the entire 2nd column of the viz.

At the time, we really only had singers, albums, and years. I didn’t like using ‘year’ because it doesn’t say a lot when considering the band’s history, so I did a little investigating and grouped year by eras I made up based on their stage as a band which also folded nicely into two-year chunks. I simply added them as separate sets with a calculation, but it’s just as easy to do in a single calculation.

Dashboard Performance Efficiency Tip: Our viz isn’t too calculation-heavy as most of the calculations were done on the data source. Over the years, especially when dealing with filter-heavy workbooks utilizing large data sources (millions of records), I found it best to handle as many ‘tried and true’ static calculations on the back end to reduce the stress on your dashboard — it will work more predictably and efficiently. If you have items that may change/want more open to change or if you can’t make the changes to the source itself, then the calculations should be made in Tableau. Besides, if your data source isn’t in the millions and/or you don’t have a lot of sheets applying many filters in your dashboard, the difference isn’t noticeable.

All We’ve Got to Do (is finish this dashboard)

The final pieces were cleaning up text, tooltips, putting Easter eggs, and coming up with a few more bits.

Kevin thought of the awesome post-its that made it fun. Although not timely to the Beatles era (there weren’t ‘Post-its’ until after the Beatles broke up (AKA when Romy invented them and Michelle made them yellow)), but I let that go because they were ultra-fun and hysterical. Kevin, which he probably regrets now, developed the “Who got it right?” vs. form to help determine which ratings best align with our #DataFam.

“Who got it right?” votes as of 10 AM CST on 4/25 (45 votes)

I added a Top-20 playlist for each of our ratings (see in music morsel) and auto YouTube search links when clicking a song on the connected dot plot.

There are other Easter eggs not mentioned, but I don’t want to spoil the party (yes, yet another Beatles song reference).

Collaboration … Here Comes the Fun

When collaborating and you desire to create the best viz possible, it’s essential to leave egos at the door. Both Kevin and I had a lot of ideas with basically a 50/50 portion being implemented in the final product (with much more on the cutting room floor). Our focus was on the outcome and what we intended to accomplish. This paved the way for us to be open-minded and quickly pivot when we knew an idea is better than our own. When doing so and celebrating what each brings, the development is fluid and doesn’t feel like work — this combined to make the process addicting and exhilarating.

Besides that, you can’t help but learn, form greater bonds with friends (or make new ones), and be proud of the collective effort put in.

Every successful collaboration I’ve been part of has not only made the visualization better but also helps develop a new respect and appreciation for the partner(s) involved.

As for Kevin, my one-word summation of the experience with him in this project was ‘effortless’. In many ways, we were one mind that ping-ponged good ideas into better ones without any conscious effort.

We finished much earlier than anticipated and after a couple of quick back and forth regarding wording, sat back and scheduled our publication.

Side prop: Cheers to Jeffrey Shaffer (Twitter | Site) who privately pointed out a typo to us on the bottom of the viz.

To me (and would believe this holds for Kevin), we created something that we really liked ourselves and proud to share with others. We appreciate the great attention it received and love from the community (some even mentioned they didn’t care for their music but dug the viz), but that’s simply a wonderful cherry on top of our long and winding road to answer a simple question… “What’s your favorite Beatles song?”.

You Can’t Do That… The Outtakes

  • If you couldn’t tell, each header was inspired by a Beatles song. 😁
  • Albums were not graded by the album experience, but just on the whole of individual song scores. Listening to an entire album as one entity can elevate songs based on the themes, production, and overall arrangement. For example, “Magical Mystery Tour OST” is my highest ranked album based on its rating of the five original (non-George Martin penned) compositions. My favorite full album experience is “Revolver”, though. Kevin really appreciated how “Abbey Road” flowed seamlessly from one song to the next (me too). I just know that The Beatles (White Album) would be a middling album for me these days and no longer in the discussion as a personal favorite. If condensed to one album, it could be a 100/100 experience, but there are superfluous experiments with poor payoffs which hurt the experience (we’re looking at you “Revolution 9”).
  • Kevin liked Ringo more than George. That got some reaction from the community. Ringo wrote only two of the songs he sang on (“Octopus's Garden” and “Don’t Pass Me By”) and was known for just getting the scraps from Lennon-McCartney compositions that they had no interest in singing on or was well within his limited range. But I can understand how George’s Indian music experimentation was nice on its own but felt like an odd bedfellow with the rest of the Beatles' oeuvre (and Ringo was the most likable Beatle personality).
  • Back to my 2008 ratings… my tastes have clearly changed in the ~13 years since I posted that list. The older I get, the more I appreciate the Beatles’ transition era. It was a period they were cohesive, explored more adult concepts, and had quality control with their experiments. With that said, some of their best work occurred after the transition era, but it was less consistent and the splintering as a unit was evident (i.e. musically the music felt more and more like four separate journeys rather than one journey).

In The End, There’s Further Exploration

  1. Here is the Google Sheet you can use to score Beatles songs and/or make your own inspired Beatles viz.


  • Make a copy of the file by selecting, File >> Make A Copy (must have a Google account).
  • In the 2nd column, select YouTube Search links to find the song you want to review.
  • The 3rd column is the only column that needs to be completed. Use that one to rate each song from 1 (worst) to 100 (best).

Notes: All other items are automated using formulas so you can see your ranking (non-unique), mean score, standard deviation, and z-scores will all be calculated for you. Each song includes the year, lead singer(s), and the album it was originally released.

2. We created the z-score on a Google Sheet before adding it to Tableau. Andy Kriebel (Twitter | Site) explains how to calculate the z-score within Tableau via a YouTube tutorial.

3. Ken Flerlage (Twitter | Blog) provides a detailed look at using PowerPoint in Tableau back in 2018.

4. Text in Tableau can be a bit frustrating as only a few are both web and Tableau safe. Here is another great post by Ken Flerlage (Twitter | Blog) that helps demystify font usage in Tableau.

5. Make sure to check out/pre-order Steve’s new book, “The Big Picture”. It’s a book intended to assist business professionals to gain efficient insight from data visualizations.

6. A sweet blog tutorial on using parameters to sort is from Jay Farias (Twitter) for Evolytics. Note: This is the sorting mechanism used for all charts.

7. Here is a fantastic starter guide to creating the connected dot plot in Tableau that comes from Ryan Sleeper’s (Twitter | Site) on this blog. Kevin’s twist was to size the connecting line thicker, which looks cool when animating and helps our dots pop more.

Binge Bite

“The Staircase” is a transfixing docuseries covering the trial of novelist, Michael Peterson following the suspicious death of his wife, Kathleen. Although this approaches it from the defendant’s angle as he, his family, and his counsel were the primary focal points of the series, there are plenty of insights that allow you to see it from a different angle too (notably, Michael Peterson’s own behavior).

Music Morsel

This is easy. It’s my Top-20 Beatles songs released as an active band and linked on the viz.

Just because I’m feeling charitable, I’ll share Kevin’s too…



Adam Mico

Data Visualization and Enablement Leader | Data Leadership Collaborative Advisory Board Member | Tableau Visionary + Ambassador | Views are my own