Last week the Dick’s Sporting Goods ad with Arnold Palmer popped into my head. If you haven’t seen it before it’s worth the 50 odd seconds.
In the add Palmer says:
“Swing your Swing.
Not some idea of a swing.
Not a swing you saw on TV.
Not the swing you wish you had.
No, swing your swing.
Capable of greatness.
Prized only by you.
Perfect in its imperfection.
Swing your swing.
I know I did”
I see “swing your swing” as be true to yourself in your approach. In golf all that truly matters is the contact with the ball. How you get to that point is where you “swing your swing”. There are methods and instructions that make it easier to square the club at impact, however, do what works for you. Explore your swing. Figure out your tendencies good and bad. Swing your swing. Don’t become so mechanical that you lose sight of you.
Because I have a golf “issue” I immediately applied this my data viz work. My goal in data visualization is to depict data in a manner that clearly communicates the insights in the data. Like golf, there are a number of best practices and methods on how to do this. And like golf there are different approaches to get to the same end point. Take a look at #makeovermonday or #dataforacause and you will see an number of people approach the same data set in a number of different ways. You’ll see things that run the range of simple bar charts to radial charts. This is where people “viz their viz”.
In both golf and data viz your personal style is important, however, if your style trumps your success or ability to communicate effectively you need to refine your style. It is hard to square the club consistently when you come over the top and it is hard to communicate data effectively when you have a pie chart that uses 20 different colors.
So how do you get there?
Practice, practice, practice.
Experiment – #makeovermonday is a great opportunity for this.
Learn from others – be inspired by their work but don’t seek to duplicate someone else’s style.
December 12th this year will be a pretty special day for me – it will be 10 years since my last radiation treatment at Brigham and Women’s.
I had a disease that a lot of other people around the world have and I’m lucky that I had a great team of doctors and nurses and that their treatment plan for me (knock on wood) has worked so far.
To celebrate I created some BANs!
If you don’t have the Big Book of Dashboards yet you are missing out. Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave put together a fantastic resource for Business Dashboards.
I used the BBOD to help solve a request to show both the ranking and magnitude for a 12 week snapshot and also show that by different groups. Chapter 22 in the book walks through this type of scenario.
This is an example of what I created – all of the information below is fake. The chart on the left shows the ranking and controls what is highlighted in the chart on the right. The bar chart about the chart on the right appears when something is selected on the left. This was added to make it easier for the user to compare the volume across the groups.A big thanks to Steve who helped me with this addition!
This design isn’t flashy but it serves a specific purpose and it has been well received by the end users. I will probably adjust the fonts, titles, and colors a bit more but will keep the main design as it is.
I’d love to hear your feedback or ideas on this!
I spent time with a couple of co-workers yesterday going through revisions on an existing dashboard and received valuable constructive feedback. One person said the visuals are great and you built exactly what I asked for but I find it hard to answer questions – I end up having to do the work in Excel.
The thing they struggled with the most was looking at before and after a specific date. They needed the ability to pick the date to pivot off of and the ability to select the number of days back and forward to look at.
This was a good challenge for me and after thinking it over for a while I thought that parameters were exactly what I needed.
My idea was to create a parameter for the X date (the date to look back and forward from) and then create a parameter for the days back and the days forward and use those to limit the dates in the chart and use them to calculate the before and after measures they were looking for.
My Parameters are:
- Selected Date – date to pivot back and forward from (date)
- Days Back – # of days to go back (integer)
- Days Forward – # of days to go forward (integer)
My Calculated fields are:
- Back Date – [Selected Date] – [Days Back]
- Forward Date – [Selected Date] + [Days Forward]
- Custom Date Range – IF [Order Date] >= [Back Date]
AND [Order Date] <= [Forward Date]
THEN [Order Date]
I then added the Custom Date range to the filters to exclude the nulls. To exclude the nulls I added the Custom Date dimension and selected Range of Dates > Special > Non Null
I used these fields to create a line chart to show the trend during the custom date range and added a reference line for the selected date. I also create a metric tile to show the before and after counts and changes in those counts. I added a few other charts to help them see what changed by different dimensions.
I love the flexibility with parameters!
I wanted to see if I could figure this out without Googling how to do this. I’d love to know if there is an easier way to do this or if I over engineered the solution.
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!