#data16 Day 3

Admittedly I’m jumping from day 1 to day 3.  I hit a micro wall on Tuesday.  But now that I’ve pushed through to Wednesday – it is time to focus on the amazing.

First up – paradigm shift.  I had a very novel vision of expectations and how to “get the most” out of the conference.  This involved the idea of attending several hands-on sessions and maximizing my time soaking in how others solve data problems.  The ‘why’ behind the initial decision: I have a particular love for seeing how other people pull apart problems.  I was once asked what my passion was by a colleague – I said that I loved understanding the universe.  Pulling apart anything and everything, understanding it, cataloging it, figuring out how it fits into existence.  So faced with the opportunity to see how others tackle things was something I had to do.

So what was the paradigm shift?  The conference isn’t just for seeing people solve problems.  It’s about seeing people communicate their passion.  And this happens in a million different ways.  This morning it happened with Star Trek and making data fun and serious.  Later it was 300+ slides of humor secretly injected with sage wisdom.  The word that comes to my mind is intensity.  I think really what I started seeking out was intensity.  And there’s no shortage.

My takeaway: Focus more on the passion and intensity from others.  Soaking this in becomes fuel.  Fuel for improvement, potential, and endless possibilities.  I can always go back and learn the intricate, well documented solutions.  I can’t recreate magic.

Second item – commitment.  Commitment is accountability, following through, sticking it out, dedication.  Commitment is daunting.  Commitment is a conscious choice.  I made a commitment to myself to be present, to engage with others.  Following through has been difficult (and very imperfect), but it has been unbelievably rewarding.  Thinking back to my day 1 thoughts – I fall back to community.  Committing to this community has been one of the best decisions I’ve made.

My takeaway: Human connections matter and are second to none.  Human connections make all the gravy of data visualization, playing with data, and problem solving possible.  (Also when you commit to dancing unafraid at a silent disco, you end up having an amazing day.)

Final item – Try everything that piques interest.  (This one I will keep short because it’s late.)  If you sense something is special, RUN TOWARD IT.  Special is just that: special.  Unique, one-of-a-kind, infrequent.  I think the moments I’ve had while here will turn into what shapes the next year of my life adventures.

My love note for Wednesday – in the books.

Tableau Conference 2016 – full prep details

Earlier in the week I wrote a blog post promising to share with you a slide deck put together that walks through what you should prepare yourself for with regards to the Tableau Conference in Austin, TX.

I’m happy to share with you not only the slide deck, but also a video of me presenting this information to the Phoenix Tableau User Group.  This was originally recorded live via Periscope and broadcast on social media.  I’ve saved the recording, cut it down a little, and packaged it on YouTube.  The video is completely raw – true to life video taken on my iPhone 7 Plus.

In tandem I uploaded the slide deck to SlideShare connected to my LinkedIn profile.  Check it out if you get a chance:

Prepping for #data16

The last 6 months have been a huge whirlwind for me in terms of Tableau and the Tableau community.  I started out the year attending a Saturday workshop on Tableau and am now a Desktop Certified Professional and two month veteran Tableau User Group leader.

The whirlwind has been part of my 2016 vision – to get more involved in Tableau and reaffirm (maybe strengthen is a better term) my commitment to data visualization.

Right around this time last year (think TC15) is when the rumbling of ideas mentioned above started to take shape.  I wish I had been a little more agile and pushed to go to TC15.  I know it would have been an overwhelming newbie experience.  Alas, I didn’t do that, so I’m now jumping in to TC for the first time this year.

To help usher others into the conference and to leverage my community, I thought a great topic for our upcoming user group session would be the conference.  Selfishly it has multiple purposes: get seasoned Tableau users interested in coming to our monthly user group, not be lonely in Austin, and (most significantly) force myself to dig deep into the heart of what I can expect at #data16.

So here’s what I’ve learned so far, and the mistakes I’ve made so far:

  • Pre-conference starts Sunday/Monday – I scheduled my flight pretty late on Monday and may miss out on portion of the Data + Women meetup
  • Hotel booking during conference purchasing – I didn’t do this because I was afraid to have my corporate card charged, didn’t realize there was no charge until October (now I am maybe 6 miles away from the epicenter)
  • Hands-on training sessions are 2 hours – that really eats away with sheer bulk learning opportunities
  • The month leading up to the conference is zooming- I should have prepped for a Phoenix mixer pre-conference, and scoped out a time/place for us to meetup in Austin.

Tips and tidbits I’ve picked up along the way that I think will be extremely valuable:

  • Pack extra battery/power for electronic devices
  • Bring business cards – I actually caught this one in time to get some printed
  • Ramp up social media – I’m trying!
  • Plan out food
  • Get the app – the app is all powerful
  • There are social/networking opportunities not to be missed, meetups and the nightly gatherings
  • Prepare for swag (admittedly I need to get more courageous about asking vendors for swag)

And one of the most valuable tips I read was to step outside of my industry comfort zone.  I think this is one key piece of advice that will go the distance.  I love understanding how people solve their problems and then using their solutions to help solve mine.  Some of the heartache in the healthcare industry may be easily solved by a perception shift on tools and techniques used in the financial world.

My game plan for #data16 is to be as transparent as possible (without acquiring a stalker) about my whereabouts and keep everything casual.  I’m committed to minimizing FOMO and maximizing living in the moment.  And as part of my mission to enable others to harness the power of data visualization/visual analytics (and the power that Tableau has toward that), I feel it’s my duty to demonstrate and make the entire experience accessible.  Some of my favorite UG feedback has been that I make Tableau and data fun and accessible.

Look for me to share my humble deck after Thursday’s PHXTUG meeting and I hope to hang out with you at #data16 (even if that means virtually!).

Funnel Plots

As I continue to read through Stephen Few’s “Signal: Understanding What Matters in a World of Noise” there have been some new charts or techniques I’ve come across.

In an attempt to understand their purpose on a deeper level (and implement them in my professional life), I’m on a mission to recreate them in Tableau.

First up is a funnel plot. Stephen explains that funnel plots are good when we may need to adjust something before an accurate comparison can be made. In the example video, I adjust how we’re looking at the average profit per item on a given order compared to all of the orders.

What’s interesting is that in tandem with this exercise, I’m working on an quantitative analysis class for my MBA, so there was quite a bit of intersection. I actually quickly pulled the confidence interval calculation (in particular the standard error equation) from the coursework.

I find that overall statistical jargon is really sub-par in explaining what is going on, and all the resources I used left me oscillating between “oh I totally get this” and “I have no idea what this means.” To that end, I’m open to any comments or feedback to the verbiage used in the video or expert knowledge you’d like to share.

Link to full workbook on Tableau public for calculated fields: https://public.tableau.com/views/FunnelPlot10_2_16/Results?:embed=y&:display_count=yes

Thoughts on sorting in Tableau

Now with video 🙂

Last week I ran into an interesting situation with Tableau.  I wanted to sort dimensions within larger dimensions by a measure.  After that sort, I wanted to encode an additional dimension on color.  Here’s what that would look like using Superstore:

Sorting Figure 1

In the view I am looking at sub-categories by each segment, hoping to rank them by the sum of Sales.  I’ve encoded an additional measure (discount) on color.

This could be a great visualization for understanding demographics within hierarchical type dimensions.  Like say the gender breakdown of who has diabetes at hospital A.

The issue is, getting to the view shown above is somewhat more complex than I had originally thought.  So let me walk you through what happened.

  1.  Set up my view of Customer Segment, Sub-category, by sum of sales
  2. Created initial rank calculation (index()) and then did the typical sorting.
  3. Table calculation is set as follows (Custom Sort = sort by Sum of Sales, descending order):

sorting-2

4. Gets me here:

sorting-3

5. Now when I add Discount to color, my whole viz breaks:

sorting-4

6. To correct this a few things have to be done.  Initial table calculation needs to be modified to ensure the Discount is taken into consideration, but not considered for the sorting:

sorting-5

Super important to notice here that Discount is at the lowest level, but we’re computing at the “Sub-Category” level.  We’re still restarting every “Segment.”

That gives us this:

sorting-6

So now we have the sub-categories correct, we’re looking by region.  But we’re back at that original point of our sort isn’t computed correctly.  This is because we’re sorting by the highest sum of sales for a given discount in a segment.  The first sub-category is found and grouped together.  Then the next sub-category is found with the next highest (not 2nd, just ‘next’) sum of sales for a given discount.  Check it out in comparison to the crosstab, the pink highlights how the index() is working:

sorting-7

To fix this last step, we need to let Tableau (the table calculation, the world!) know that we don’t care about discount for the sum of sales.  We only care about sub-category and segment.  To resolve let’s pull in a simple LOD:

sorting-8

Now finishing it all up, replace the measure used in the table calculation for sorting:

sorting-9

And we’re back at what we originally wanted:

Sorting Figure 1

Full workbook with story point walk-through here: https://public.tableau.com/views/LearningMoment-sortingwithtablecalculations/Learningmoment-tcalcs?:embed=y&:display_count=yes

#IronViz Entry – Mobile Design

Part of being involved in the Tableau community means publicly publishing visualizations to learn and grow.  It’s also a great way to find inspiration.

As I’ve pushed myself to be more active within the local Phoenix Tableau community and social (Twitter) community, I knew it was time to “step up” and make an Iron Viz.

My design aesthetic tends to be minimal, slightly formal, and geared toward (in my mind) elegance.  I like to make dashboards and visualizations that highlight the data, but don’t jump to many conclusions.  I’m very conclusion agnostic, so leading people too far down a path doesn’t always seem right.

All that being said – I wanted to make an Iron Viz entry, but it needed to be simple.  The deadline is September 18 (today as I’m writing this).  So I wanted to develop something relatively straightforward that got to the heart of mobile design.

The data inspiration for the viz actually came from an animated bar chart .gif showing the “Top 10 Economies” growth from 1969 to 2030 that I saw on Twitter.  I thought it would make a nice bump chart or slope chart, and the conclusion of the data was already compelling.

First the data gathering process – relatively simple on this one.  The .gif referenced the source, a quick Google search led me to the results.  I’m going to loosely promise to publish the excel file at some point.

Next up was diving right in.  I recently made a “micro viz” in Tableau 10, designing it exclusively for a very tiny space.  I actually didn’t use the device designer for this one, instead opting to develop the whole thing with my intended sizing.  With the sizing set, development was similar to what I’ve done in the past in v9.3 (version of Tableau I use at my job).

Transitioning to device specific design was different than I thought.  Since I knew the final product (in my mind) would need to have more emphasis placed on the mobile view.  It is after all a mobile design challenge!

Like I mentioned above, I had a pretty good idea of what I wanted the final viz to look like.  I knew there needed to be a bump chart and I was going to call attention to China and India.  What I didn’t realize is that the device designer is really geared toward creating a “master view” and then augmenting that master for the device.  This makes sense to me as I rethink the way the feature was presented.

What this meant for the creative process?  I wasn’t able to make visualizations (sheets) and quickly drop them on the mobile layout.  For each new “potential viz” I had to first drop it on the overall dashboard and then bring it on to the mobile specific dash.  It made the whole process kind of clunky.

I also struggled a bit with getting to formatting features quickly.  I can’t double click on titles to adjust font sizes in device preview, gotta go back to the default layout.  I’ll have to adjust my thought process next time and really think about starting from the default view and optimizing a mobile version.

I probably cheated the viz out of more depth that could have been added if I had truly started with the default dashboard and then made a mobile design.  It is fair to say that the default dashboard has real estate for more data insight and data depth.  I am still very pleased with the overall final results.