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

Me vs. ETL

I had a great moment today.  A moment where I felt like I conquered the beast known as “ETL.”  For those of you who don’t know (lucky you!) ETL stands for Extract, Transform, Load.  Like it suggests: it is the term used to move data around and taking elements from “raw” forms and making them usable data.  And it is has been a beast in my life because I feel like it gets used for “gotcha” moments in the data/IT/technical world.

Let me side-track and say that I have a background in physics and mathematics AKA problem solving.  I didn’t go to school for computer science and I have no formal training in the proper terminology of data management, ETL, data warehousing.  I appreciate those who did, because they have the benefit of being taught the history and terminology that drives a lot of what goes on.  Unfortunately, I never did that.

I was driven to a job in data because of my ability to problem solve and communicate.  Math and physics are very algorithmic, they require you to assess your surroundings, identify the problem, and work on a solution.  I started out in operations, moved to quality of operations, then process improvement of operations, and finally into what I would consider the “IT world.”  Each layer that I dove into required more and more data access and to get closer and closer.  You can imagine in my quality role, I received flat files from existing front end systems to do analytical work.  Once I got to process improvement, I actually interacting with reporting associates who would pull custom SQL queries for me out of our transaction databases.  Today I work in that same type of “reporting” environment.

What’s kind of funny is – now that I’m here I realize it’s all the same.  If you’re good at understanding canned flat files and deriving value (typically analytically, but also just in general), you’ll be just as good as you get closer to the data.

So today I feel like I conquered ETL.  Once in an interview someone asked me how good at ETL I was.  I had to say that I didn’t have much ETL experience, and I think that’s what cost me the job.  I wish I would have known that an ETL tool like SSIS leverages a GUI that hauntingly resembles Microsoft Visio.  I wish I would have known that.  Because you know what I am really good at?  Visualizing processes, making process maps, streamlining processes, communicating and understanding what processes are happening.

What makes me feel like I conquered ETL?  Today I had a meeting where I used my communication skills to bridge the gap between two groups of people knee deep in the data weeds.  I moved past the barrier of never having developed an SSIS package, making a stored procedure, or developing in SSRS to solve the problem we were facing.  Does knowing SQL make it easier?  Slightly – only in the sense that I can find the fields and tables the data comes from.  But you want to know what the “problem” we were facing was?  It was “are we deriving the right fields to answer the business ask?’  Huh.  SSIS doesn’t know the answer to that.

So if you’re out there and want to get more into data, and someone says to you “rate your ETL skills.”  Push back and let them know that you are an amazing communicator, that you take time and effort to fundamentally understand data (from both the system capture end and how business users interpret it).  And today I give my ETL skills a 10, because I bridged the gap between everyone while leveraging knowledge I’ve picked up along the way and not being intimidated by ETL.

Okay – my “yay” moment is done.  Those of you that do ETL, I thank you.  There are a lot of important components that go into making data systems that contain the information people need to do their jobs and for businesses to succeed.  By no means is my aim to discredit you.  I love your ERDs.

 

 

Dot Plots

Today I was reading Stephen Few’s Information Dashboard Design aloud while Josh was doing some fall PC clean up and was on the chapter “An Ideal Library of Graphs.” As Stephen describes it there are several charts or graphs that should make their way onto dashboards and he goes into detail on the reason behind each and how to properly apply them.

We stopped to discuss the dot plot, because it is one that both of us under utilize.  From my perspective there’s a lot of under utilized space below each dot.  As we were exploring it deeper, I also felt uncomfortable with dots above a certain threshold, because at that point the actual data point was lost.  We decided to open up Tableau and start playing around.  In my mind I was thinking that using a Gantt bar as the point would better represent the data.  (Mind you, Stephen says that you use dot plots when you may not go to zero, otherwise stick to bar graphs).  I thought Gantt bars presented the perfect “happy medium” between the dot plot and the bar chart without causing the end data reader to incorrectly visualize length.

Below are the four different representations we came up.  All things equal, I think I might be most partial to the ‘Plus Plot.’  I think the general shape tends to draw your eye directly to the center point, and even if they were larger you’d still know where the center was.  I can see how this might get cluttered quickly, or if more encoding were done things could go awry.

I also found that I liked the Gantt bar, but needed to shorten the bar significantly to eliminate mentally drawing drop lines and visualizing as bars.

Getting started in data

Continuing on the path I started the other day, here’s another blog post that I first wrote internally back in March of this year.

The question: How do you think you can encourage more women in data?

I think the toughest part of getting started in data is understanding the field and feeling confident working within it. It took me some time to realize that working with data is a mindset. It isn’t a functional area reserved for someone who has amazing coding or technical skills. It is for people who are detail oriented and want to understand the “why” behind things.

Finding the reason or purpose behind causes is one of my favorite components of working in data. Within this field, you get the opportunity to do that everyday. I am also extremely passionate about taking the data and creating a visual story with it.

If you’re interested in data or want to know if it is the right field for you, ask yourself if you like knowing the “why.” I’d also be curious if you are driven to mastery. Within the data field there are daily opportunities to learn and apply that newly found knowledge to your job. If that’s something you like doing, data and data analysis is the right place for you.

I’ve been in several analyst roles and they have meant different things. The technical skill set and applications have always varied widely, but there have been a few universal constants. Those constants are extensions of your personality – being a great problem solver, a critical thinker, and someone who can both reach solid conclusions AND (more importantly) communicate them. These few skills are what make people great analysts. The tools they use are just there to help them broadcast their findings.

If you’re on the fence, I would also encourage you to go find some data available in your daily life. There’s literally mountains of data out there. Find something that you’re interested in knowing more about and analyze it. It is a great exercise to start thinking like a data analyst and I guarantee you that you’ll find something new and interesting.

Once you’ve analyzed one data set, find something totally different. See what commonalities in the structuring of the data you can find. After having received hundreds of different data sets, I can tell you there are several commonalities. The more you practice with any set of data, the better you’re going to get at overall data analysis.

My last comment would be to remember there’s never one right answer in data analysis. It is a creative and interpretive field. Your interpretations should be based on the truth of the data, but realize once you’re in the analysis driver’s seat it’s up to you to showcase what’s important.

Quick and easy – parameters to aggregate dates


(Now with video – video uses different data as an example)

One of my favorite uses of parameters is to dynamically change date aggregations without the pesky drill symbol.  Sometimes I want to just see week or month, quarters tend to be pretty worthless.  Especially if I’m doing something discrete, I really don’t like the way Tableau breaks apart the data view.

The creation of the parameter leaves me with a very clean user experience and ensures users don’t negatively interact with the viz.

Here’s the setup – first create a parameter of data type string and type in your allowed values.  Mine is going to have week and month:

parameter1

(note I changed the casing on the words later on for look/feel)

Next create custom dates based on your primary date field.  Mine is called “Start”:

dates

Right click on the field, go to Create -> Custom date…

For the sake of this viz, I made both a Months and Weeks (both are date value).  At one point I had days (shown as “Exact”, but I abandoned it)

Last step is create the calculated field.  Right click on the parameter at the bottom, create calculated field:

dates2

Place it on your designated shelf.  You may have to do some wrangling on the field to get it to display right.  For the viz I ended up with, the field is set to “Exact Date” and “Discrete.”

Check out the final experience below:

Data Analysis – First Steps

Earlier in the year (March), I participated in a panel discussion for a data+women series at the Phoenix TUG.  As prep I was given a list of questions and asked to pick a few I felt comfortable answering.

To facilitate the sharing of my experiences, I wrote a blog post on some of the more intriguing questions.  What comes next is a blog post I originally published within my company’s social media site on 3/23/16.

What is your first step of investigation when you are given a dataset?

So before investigating a data set, I think it is critical to understand what the data output contains. I think it is important to meet with the business owners or subject matter experts that are responsible for creating, maintaining, or using the data. This helps to ensure you understand what they are looking for and what the different fields mean.

Assuming the pre-work has been done with business owners, I always approach a new dataset the same way. There’s three things I do every time.

The first is to find a categorical dimension that seems to be at the highest level. Typically I’m looking for a category that has less than 20 members (10 to 15 being ideal). This data can set a great foundation to understanding the overall population of records. Once I’ve found that categorical dimension, I immediately create a Pareto chart. Recall that a Pareto chart is a bar chart where the bar volumes are ranked from greatest to smallest. Additionally it has a line chart on the secondary axis displaying the cumulative percentage. The goal of the tool is to see immediately where most (or a majority) of problems are. And it usually follows the 80/20 rule logic.

The next thing I do is plot records over time, usually by month, sometimes by week. This is great for understanding any seasonality and for finding immediate trends. Often systems are changing and new processes are implemented. By plotting records over time, you can immediately take any funny data points back to your business owner and ask for clarity.

The more clarity you have around your dataset, the more successful you’re going to be at understanding the information you can retrieve from it.

The last thing I do is take that categorical dimension and plot each one against time. This really is the combination of the first two to a large extent. From this you’ll be able to further pinpoint any trends that are category specific and find even more potential seasonality or process changes.

 

By creating the three visuals I mentioned above, I usually feel pretty confident about the “vitals” of the dataset. I get a great sense of being able to communicate in the same language and accurately reference volume. I’ve found that this is a great first step when working with almost any set of data. It makes an excellent conversation starter with business owners and also proves itself as a useful communication tool when bringing new team members into a project.

That’s my initial analysis workflow, what’s yours?

#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.