A follow up to The Women of #IronViz

It’s now 5 days removed from the Tableau Conference (#data17) and the topic of women in data visualization and the particularly pointed topic of women competing in Tableau’s #IronViz competition is still fresh on everyone’s mind.

First – I think it’s important to recognize how awesome the community reception of this topic has been.  Putting together a visualization that highlights a certain subsection of our community is not without risk.  While going through the build process, I wanted to keep the visualization in the vein of highlighting the contributions of women in the community.  It wasn’t meant to be selective or exclusive, instead, a visual display of something I was interested in understanding more about.  Despite being 5 days removed from the conference, the conversations I’ve been involved in (and observed from a distance) have all remained inclusive and positive.  I’ve seen plenty of people searching for understanding and hunting for more data points.  I’ve also seen a lot of collaboration around solutions and collecting the data we all seek.  What I’m thankful that I have not witnessed is blame or avoidance.  In my mind this speaks volumes to the brilliant and refined members of our community and their general openness and acceptance of change, feedback, and improvement.

One thing circling the rounds that I felt compelled to iterate off of, is @visualibrarian’s recent blog post that has interview style questions and answers around the topic.  I am a big believer in self reflection and exploration and was drawn to her call to action (maybe it was the punny and sarcastic nature of the ask) to answer the questions she put forth.

1. Tell me about yourself. What is your professional background? When did you participate in Iron Viz?

My professional background is that of a data analyst.  Although I have a bachelor’s degree in Mathematics, my first professional role was as a Pharmacy Technician entering prescriptions.  That quickly morphed into someone dedicated to reducing prescription entry errors and built on itself over and over to be put in roles like quality improvement and process engineering.  I’ve always been very reliant on data and data communication (in my early days as PowerPoint) to help change people and processes.  About 2 or 3 years ago I got fed up with being the business user at the mercy of traditional data management or data owners and decided to brute force my way into the “IT” side of things.  I was drawn to doing more with data and having better access to it.  Fast-forward to the role I’ve had for a little over 8 months as a Data Visualization Consultant.  Which essentially means I spend a significant amount of my time partnering with organizations to either enable them to use visual analytics, improve the platforms that they are currently using, or overcoming any developmental obstacles they may have.  It also means I spend a significant amount of time championing the power of data visualization and sharing “best practices” on the topic.  I often call myself a “data champion” because I seek simply to be the voice of the data sets I’m working with.  I’m there to help people understand what they’re seeing.

In terms of Iron Viz – I first participated in 2016’s 3rd round feeder, Mobile Iron Viz.  I’ve since participated in every feeder round since.  And that’s the general plan on my end, continue to participate until I make it on stage or they tell me to stop 🙂

2. Is Tableau a part of your job/professional identity?

Yes – see answer to question #1.  It’s pretty much my main jam right now.  But I want to be very clear on this point – I consider my trade visual analytics, data visualization, and data analytics.  Tableau is to me the BEST tool to use within my trade.  By no means the only tool I use, but the most important one for my role.

3. How did you find out about Iron Viz?

When I first started getting more deeply involved in my local User Group, I found out about the competition.  Over time I became the leader of my user group and a natural advocate for the competition.  Once I became a part of the social community (via Twitter) it was easy to keep up with the ins and outs of the competition.

4. Did you have any reservations about participating in Iron Viz?

Absolutely – I still have reservations.  The first one I participated in was sort of on the off chance because I found something that I want to re-visualize in a very pared down elegant, simplistic way.  I ended up putting together the visualization in a very short period of time and after comparing it to the other entries I felt my entry was very out of place.  I tend to shy away from putting text heavy explanations within my visualizations, so I’ve felt very self-conscience that my designs don’t score well on “story telling.”  It was also very hard in 2016 and the beginning of 2017.  Votes were based off of Twitter.  You could literally search for your hashtag and see how many people liked your viz.  It’s a very humbling and crushing experience when you don’t see any tweets in your favor.

5. Talk me through your favorite submission to Iron Viz. What did you like about it? Why?

Ah – they are all my favorite for different reasons.  For each entry I’ve always remained committed and deeply involved in what the data represents.  Independent of social response, I have always been very proud of everything I’ve developed.  For no other reason than the challenge of understanding a data set further and for bringing a new way to visually display it.  My mobile entry was devastatingly simple – I love it to death because it is so pared down (the mobile version).  For geospatial I made custom shapes for each of the different diamond grades.  It’s something I don’t think anyone in the world knows I did – and for me it really brought home the lack of interest I have in diamonds as rare coveted items.

6. What else do you remember about participating in Iron Viz?

The general anxiety around it.  For geospatial 2017 I procrastinated around the topic so much.  My parents actually came to visit me and I took time away from being with them to complete.  I remember my mom consoling me because I was so adamant that I needed to participate.

Safari and Silver Screen were different experiences for me.  I immediately locked in on data sets on subjects I loved, so there was less stress.  When I did the Star Trek entry I focused on look and feel of the design and was so stoked that the data set even existed.  Right now I am watching The Next Generation nightly and I go back to that visualization to see how it compares to my actual perception of each episode (in terms of speaking pace and flow).

7. Which Iron Viz competitions did you participate in, and why?

Everything since 2016 feeder round 3.  I felt a personal obligation and an obligation to my community to participate.  It was also a great way for me to practice a lot of what I tell others – face your fears and greet them as an awesome challenge.  Remain enthusiastic and excited about the unknown.  It’s not always easy to practice, but it makes the results so worth it.

8. What competitions did you not participate in, and why?

Anything before mobile – and only because I (most likely) didn’t know about it.  Or maybe more appropriately stated – I wasn’t connected enough to the community to know of it’s existence or how to participate.

9. Do you participate in any other (non Iron Viz) Tableau community events?

Yes – I participate in #MakeoverMonday and #WorkoutWednesday.  My goal for the end of 2017 is to have all 52 for each completed.  Admittedly I am a bit off track right now, but I plan on closing that gap soon.  I also participate in #VizForSocialGood and have participated in past User Group viz contests.  I like to collect things and am a completionist – so these are initiatives that I’ve easily gotten hooked on.  I’ve also reaped so many benefits from participation.  Not just the growth that’s occurred, but the opportunity to connect with like-minded individuals across the globe.  It’s given me the opportunity to have peers that can challenge me and to be surrounded by folks that I aspire to be more like.  It keeps me excited about getting better and knowing more about our field.  It’s a much richer and deeper environment than I have ever found working within a single organization.

10. Do you have any suggestions for improving representation in Iron Viz?

  • Make it more representative of the actual stage contest
  • Single data set
  • Everyone submits on the same day
  • People don’t tweet or reveal submissions until contest closes
  • Judges provide scoring results to individual participants
  • The opportunity to present analysis/results, the “why”
  • Blind submissions – don’t reveal participants until results are posted
  • Incentives for participation!  It would be nice to have swag or badges or a gallery of all the submissions afterward

And in case you just came here to see the visualization that’s set as the featured image, here’s the link.

Makeover Monday Week 10 – Top 500 YouTube Game(r) Channels

We’re officially 10 weeks into Makeover Monday, which is a phenomenal achievement.  This means that I’ve actively participated in recreating 10 different visualizations with data varying from tourism, to Trump, to this week’s Youtube gamers.

First some commentary people may not like to read: the data set was not that great.  There’s one huge reason why it wasn’t great: one of the measures (plus a dimension) was a dependent variable on two independent variables.  And that dependent variable was processed via a pre-built algorithm.  So it would almost make sense to use the resultant dependent variable to enrich other data.

I’m being very abstract right now – here’s the structure of the data set:

Let’s walk through the fields:

  • Rank – this is a component based entirely on the sort chosen by the top (for this view it is by video views, not sure what those random 2 are, I just screencapped the site)
  • SB Score/Rank – this is some sort of ranking value applied to a user based on a propriety algorithm that takes a few variables into consideration
  • SB Score (as a letter grade) – the letter grade expression of the SB score
  • User – the name of the gamer channel
  • Subscribers – the # of channel subscribers
  • Video Views – the # of video views

As best as I can tell through reading the methodology – SB score/rank (the # and the alpha) are influenced in part from the subscribers and video views.  Which means putting these in the same view is really sort of silly.  You’re kind of at a disadvantage if you scatterplot subscribers vs. video views because the score is purportedly more accurate in terms of finding overall value/quality.

There’s also not enough information contained within the data set to amass any new insights on who is the best and why.  What you can do best with this data set is summarization, categorization, and displaying what I consider data set “vitals.”

So this is the approach that I took.  And more to that point, I wanted to make over a very specific chart style that I have seen Alberto Cairo employ a few times throughout my 6 week adventure in his MOOC.

That view: a bar chart sliced through with lines to help understand size of chunks a little bit better.  This guy:

So my energy was focused on that – which only happened after I did a few natural (in my mind) steps in summarizing the data, namely histograms:

Notice here that I’ve leveraged the axis values across all 3 charts (starting with SB grade and through to it’s sibling charts to minimize clutter).  I think this has decent effect, but I admit that the bars aren’t equal width across each bar chart.  That’s not pleasant.

My final two visualizations were to demonstrate magnitude and add more specifics in a visual manner to what was previously a giant text table.

The scatterplot helps to achieve this by displaying the 2 independent variables with the overall “SB grade” encoded on both color and size.  Note: for size I did powers of 2: 2^9, 2^8, 2^7…2^1.  This was a decent exponential effect to break up the sizing in a consistent manner.

The unit chart on the right is to help demonstrate not only the individual members, but display the elite A+ status and the terrible C+, D+, and D statuses.  The color palette used throughout is supposed to highlight these capstones – bright on the edges and random neutrals between.

This is aptly named an exploration because I firmly believe the resultant visualization was built to broadly pluck away at the different channels and get intrigued by the “details.”  In a more real world I would be out hunting for additional data to tag this back to – money, endorsements, average video length, number of videos uploaded, subject matter area, type of ads utilized by the user.  All of these appended to this basic metric aimed at measuring a user’s “influence” would lead down the path of a true analysis.