The Float Plot

One of the more interesting aspects of data visualization is how new visualization methods are created.  There are several substantial charts, graphs, and plots out there that visualization artists typically rely on.

As I’ve spent time reading more about data visualization, I started thinking about potential visualizations out there that could be added into the toolkit.  Here’s the first one that I’ve come up with: The Float Plot.

The idea behind the float plot is simple.  Plot one value that has some sort of range of good/acceptable/bad values and use color banding to display where it falls.  It works well with percentage values.

I’ve also made a version that incorporates peers.  Peers could be previous time period values or they could be less important categories.  The version with peers reminds me somewhat of a dot plot, but I particularly appreciate the difference in size to distinguish the important data point.

What’s also great about the Float Plot is that it doesn’t have to take up much space.  It looks great scaled short vertically or narrow horizontally.

Enjoy the visualization on my Tableau public profile here.

Statistical Process Control Charts

I’ve had this idea for a while now – create a blog post and video tutorial discussing what Statistical Process Control is and how to use different Control Chart “tests” in Tableau.

I’ve spent a significant portion of my professional career in business process improvement and always like it when I can integrate techniques learned from a discipline derived from industrial engineering and apply it in a broader sense.

It also gives me a great chance to brush up on my knowledge and learn how to order my thoughts for presenting to a wide audience.  And let’s not forget: an opportunity to showcase data visualization and Tableau as the delivery mechanism of these insights to my end users.

So why Statistical Process Control?  Well it’s a great way to use the data you have and apply different tests to start early detection.  Several of the rules out there are aimed at finding “out-of-control,” non-normal, or repetitive parts within a stream of data.  Different rules have been developed based on how we might be able to detect them.

The video tutorial above goes through the first 3 Western Electric rules.  Full details on Western Electric via Wikipedia: here.

Rule 1: Very basic, uses the principle of a bell curve to put a spotlight on points that are above or below the Upper Control Limit (UCL) or Lower Control Limit (LCL) also known as +/- 3 standard deviations from the mean.  These are essentially outlier data points that don’t fall within our typical span of 99.7%.

Rule 2: Takes into consideration surrounding observations.  Looking at 3 consecutive observations are 2 out of 3 above or below the 2 SD mark from the average.  In this rule the observations must be on the same side of the average line when beyond 2 SD.  Since we’re at 95% at 2 SD, having 2 out of 3 in a set in that range could signal an issue.

Rule 3: Starts to consider even more data points within a collection of observations.  In this scenario we’re now looking for 4 out of 5 observations +/- 1 SD from the average.  Again, we’re retaining the positioning above/below the average line throughout the 5 points.  This one really shows the emergence of a trend.

I applied the first 3 rules to my own calorie data to see detect any potential issues.  It’s very interesting to see the results.  For my own particular data set, Rule 3 was of significant value.  Having it in line as the new daily data funnels in could prevent me from going on a “streak” of either over or under consuming.

 

Interact with the full version on my Tableau Public profile here.

#MakeoverMonday 11/22/16 – Advanced Logging Edition

And it’s time – my first ever Makeover Monday.  I’ll admit, I’ve attempted to catch up in the past, but always lost steam.  I think the first data set might be related to sports and I struggle to focus on making something interesting.

Despite my follies, I’m proud to say that I’ve participated in this week’s Makeover Monday in honor of the special advanced logging that is taking place.  Along with submitting work with the hashtag on twitter, Tableau has asked for us to upload a copy of our log files and workbook.  Contained within the advanced log files are .PNGs that show analysis iterations.

I went into this Monday with the idea of doing a basic “best practices” version.  One that would mimic something I might create for ultimate exploration and zero data journalism.  I tried to stick with one element that I thought worked well and create the dashboard around it.

Looking at the other participants, I’m already thinking that my time heatmap could be improved.  My mind was stuck on the day numbers and quarters.  I should have switched to days of the week!  Irrespective – here it is:


And the GIF:

makeover-monday-112116

#data16 Data Dump

Last night was our monthly Phoenix Tableau User Group (PHXTUG) meeting and as part of the post-excitement of Tableau’s 2016 conference we took some time to go through their strategy and some upcoming features.

Full video is available here:

Interested in reusing the slides? Find the deck here:

#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.

#data16 Day 1

What better way to commemorate my first day at #data16 than sharing the highs, lows – what has met expectations and what I didn’t expect.

The community – Probably the one thing I couldn’t anticipate coming into #data16 was how the virtual community (mainly via Twitter) compared to reality.  Like internet dating, you never really know how things are going to be until you meet someone in real life.  Not that I am shocked, but everyone that I’ve met from the blogosphere/twitterverse has been even more amazing than I imagined.  From sitting next to an Iron Viz contestant and forming a friendship on the plane, to getting a ride to my hotel, to meeting up with friends in a crazy food truck alley, to someone shouting my name in the Expo Hall – it’s been a wave of positive energy.

One unexpected component was the local Phoenix community.  It’s been awesome to see familiar faces from Phoenix wandering around Austin soaking in every moment.  I wanted to come to Austin and feel surrounded by familiar and that is definitely something that’s been accomplished.

The venues – When I was 18, I redecorated my childhood bedroom to be more “adult.”  Part of the process was finding the perfect desk for my space.  I somehow stumbled onto an Ikea webpage (mind you, I grew up in a small-ish city in Indiana).  Not knowing too much, I convinced my mom to road trip to Chicago to go to Ikea and buy my perfect desk.  What I expected at the time was to walk into a normal size furniture store.  I couldn’t fathom or anticipate the sheer size the store turned out to be.  That’s been my experience in Austin so far.  Overwhelmingly massive in size with everything being on a grand unexpected scale.  Not bad, just unexpected.  The registration desk had 50+ smiling faces greeting me.

Logistics – I’m still early in the game, so I will have to elaborate after a full day of conference.  So far I’ve been extremely impressed.  I was intimidated by being south of campus.  How would I get around, would I be able to be “in it?”  This has been a non-issue.  Details on transportation have been very transparent and well organized.  There’s been food at every turn, plenty to sustain even the weirdest of diets.

The weather – This has been my only let down!  I can tell it has been rainy off and on, so it is super humid.  For someone used to the dry Arizona air, it’s a little different feeling the moisture in the air.  I’m sure my skin in thankful!  But, tonight I’m left running the A/C to compensate for the moisture.  A huge change from swimming in Phoenix on Sunday to heavy humidity on Monday.

First up for my very full day Tuesday is hopefully a meetup for Healthcare followed up by the morning keynote (I really need to eat some breakfast!).  After that – we’ll see.  I originally anticipated spending the majority of my time in Jedi hands-on sessions.  I love seeing how people solve data problems and figuring out things I can take back, tweak and tinker with.  After today, I’m wondering if I should reevaluate.  The one thing I won’t be able to recreate after this experience are the people, so anytime there’s a schedule clash – for me I am prioritizing networking above all else.

#data16 day one 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