I haven’t touched a club since the first weekend in December and it looks like it will be a couple of more weeks until the snow is finally gone (I hope). Can’t wait to get out there!
I was an early Tableau adopter in work and tried to push my end users out of their Excel data dump comfort zone. I would correct people who referred to Tableau as Excel on steroids on a regular basis. I was excited about giving my users a visual representation of their data. But, I kept getting asked to add table view of the data. I would add these views, but, I wouldn’t make them as pretty as the dashboards in hopes that people would use the dashboards instead. But that wasn’t the case, when I looked at Tableau server the most viewed sheets were the boring pivot table views.
I didn’t give up and kept plowing ahead and improving my Tableau skills. I’ve been trying to learn as much as I can about colors, charts, telling stories, LOD calculations, parameters and all the other things that go along with Tableau. I started to convert more users to the visual side and away from the table views. When someone wanted to know how to export the raw data my canned response was what do you need it for. I wanted to make the dashboard helpful for them. I felt like I was making some progress.
My goal with Tableau was to make it easier for the end users to quickly see what they need and allow them to interact and customize the dashboards but I felt like I was missing something – was I really giving them what they needed? Was I giving a solution without really knowing what they needed?
To help me solve that question I attended a Design Thinking Bootcamp class at General Assembly. The class was helpful and gave me a number of ideas on how I can change my approach.
Here are a few of the concepts that stood out to me:
- ask open ended questions to the end user
- silence is fine – if you ask a question and the user stops to think – let them think don’t interject other ideas or options
- pay attention to work-arounds – these are areas the need isn’t being met
- after you understand the need develop your point of view with the user and their need (not solution). someone who can’t reach the top shelf doesn’t need a ladder they need the item on the top shelf.
- get the topic and ideas out on paper – make this a free flowing exercise – no judgements!
- prototype – shouldn’t be a finalized version. It should be used to communicate the idea. be prepared to scrap it and start over – we don’t always get it right on the first go round.
- build your story – who is the user, what is the challenge they face, how does that challenge impact them, what is the solution, how does it meet the need
It was helpful for me to be reminded that you need to fully understand the need and develop a lot of ideas around the need before jumping to a solution.
This week’s exercise looked at Valentine’s day spending in the US. I liked the original viz – the color scheme seemed appropriate for the topic. I liked the images and felt the size and images conveyed what they were intended.
After setting up the data set I started creating the calculated fields I needed:
- The first was to create a date field from the Year field in the dataset – DATE(“02″+ “/” + “14” + “/” + STR([Year])) .
- I then created a couple of measures fields for the % Buying and the Avg Net Spend – IF [Metric] = ‘Percent Buying’ THEN [Measure] END and IF [Metric] = ‘Net Average Spend’ THEN [Measure] END
I wanted a custom shape for whatever I ended up creating – I found a free clip art heart online bought that into PowerPoint did a couple of updates to it and saved it to my custom shapes file.
After creating a few different views I decided to keep it simple and focus on what people were buying for Valentine’s day from 2010 – 2016. I tried line charts and bar charts with the hearts and then thought this may be a good time to give a bump chart a try.
Matt Chambers has a great post on his site that walks you through how to create a bump chart and I used that as a refresher. In order to get the bump chart to work I had to create a couple of more calculated measures:
- Rank for the % Buying – RANK_UNIQUE(SUM([% Buying]))
- Prior Year Ranking – LOOKUP([Rank % Buying],-1)
- Difference From Prior Year – IF [Rank % Buying] > [Prior Year Ranking] THEN ‘down from the prior year’
ELSEIF [Rank % Buying] = [Prior Year Ranking] THEN ‘the same as the prior year’
ELSEIF [Rank % Buying] < [Prior Year Ranking] THEN ‘up from the prior year’
I wanted the prior year and difference from prior year for the tooltip.
After getting the bump chart working I tested out a couple of different color schemes and found the purple to be a bit easier on the eyes than the red I had intended on using.
There is a lot more I could have done with the dataset this week, but, overall I’m pretty happy with what I created. The color scheme is different for me and I was happy with the custom shape and the bump chart. For the next few months I’m going to experiment more with the design side.
I hadn’t been to a restorative class in awhile and hadn’t been to a Mary O’Toole restorative class in ages so last night was a treat. Mary is holding classes in a great spot in Quincy and I highly recommend attending her classes – the schedule is on her meetup site.
Trying to get caught up on the Makeover Monday exercises from the last couple of weeks. I just finished week 5 which was on employment in the G7 countries. The original viz showed two pie charts on employment share and net employment growth in the G7 countries from 2010 – 2016
From reading the article the more important data point appeared to be the share of net employment growth in the US. I decided to turn that pie chart into a bar chart because I find it easier to see the differences in values with a bar chart than in a pie chart. I also adjusted the thickness of the bar to correspond to the measure. I kept the share of total employment in my remake as a reference point – I wanted the focus to be on the net share of employment so I added the share of total employment as a table. You can view the workbook on my Tableau public site.
In both my MOM and work dashboards I am starting to use the same theme. I like the clean look and find it easier to read dashboards on a light background. I think the darker backgrounds can be beautiful when they are done right but I have a hard time reading them and sometimes find them distracting. I’m getting new glasses in a few weeks and maybe when I get my progressives I’ll change my mind but for now I’m sticking with the light background!
Back from vacation and catching up on the last two Makeover Monday exercises. I tried the New Zealand RTI one from week 4 first.
The first thing I created was a line chart that showed the RTI by visitor type by period. I filtered the dashboard to just the Total Region and excluded 2008 from the chart. The definitions listed RTI as the change in expenditure by Region comparing the month to the average month in 2008, so, I figured 2008 didn’t need to be included.
There were a lot of options with this data set and I spent longer than it may seem on this one. The thing I kept coming back to was the difference in the RTI in Winter between domestic and international visitors. So after reading a lot about New Zealand weather I decided to go with that.
The viz itself is simple – a line chart to show the RTI trends by visitor type and a list of the 10 Regions with the largest difference in RTI between domestic and international visitors during the winter months.
Let me know if you think I missed the mark here or have any suggestions or feedback!
Saw a great article on Golf Digest about how to get more power in your swing.
I love that they are highlighting Ariya Jutanugarn here and this is my favorite quote – “It’s why a 5-foot-7 woman hits it by 90 percent of male club players…”
Think I know what I’m working on this winter!
This week’s MakeOverMonday was the first time I have used Twitter data. The objective was to rebuild a Buzz Feed depiction of Trump’s retweets. The thing I love about MakeOverMonday is that it pushes me outside of the data sets I use in work – I’m not sure I got everything right this week but because this is my “Tableau play time” I’m going with what I built.
This is the dashboard I ended up with:The retweets by day chart is a Gantt chart and I’m not sure that it is the best visualization but I thought it looked like a city skyline and thought that fit into the Trump theme. I think it conveys the point that the number of Tweets Trump is retweeting has decreased since he announced his candidacy in June 2015.
The original data set looked at the users Trump was retweeting and I wanted to keep with that so I created the bar chart that shows the number of times he has retweeted that user with a color coding for before and after. I filtered this to people he retweeted 10 or more times. I thought it was interesting that the only person in his inner circle who met that criteria and who he retweeted before the announcement was @danscavino. I don’t have enough time or information to figure out why that is but I thought it was interesting.
I exceeded the time limit I set for myself this week so I’m going with what I built.
Thanks for stopping by! I’m planning to use this blog as a place to share my Tableau work and other various musings (which will probably all be focused on golf and yoga).