The n00b taught the teacher a lesson: Changing the field on the color or size shelf without changing the shelf itself.

If you ever think you know everything there is to know about Tableau, then I guarantee you that you are wrong.  I’ve never once thought that I know it all.  The training class I ran today in Dublin is proof that even someone with almost six years of experience using Tableau can learn from someone who has been using it for less than one hour.

Picture this.  You have a simple bar chart that shows Sales by Container colored by Profit Ratio.

But now, instead of coloring by Profit Ratio, you want to color by a different field, perhaps Discount or Customer Segment.  I was always taught to drop the field on the Color shelf to replace the existing color.  But there is another way, which the n00b taught me today.

Instead of dropping the new pill on the Color shelf, you can drop it on the color palette itself.  Like this.  Notice how I’m dropping Discount onto the color palette (it’s very faint).  Also notice how in Tableau 8, all of the places where you can drop the pill are outlined in an orange border (except the chart area).

And viola, you have your updated viz.

I’ve confirmed that this works in versions 7 & 8 as well as on both the Color and Size shelves.  This might very well be THE reason why I love teaching so much.  I truly feel that I learn more from teaching than I do from working on my own.  And this is also why I love Tableau; I learn something new every single day.

How many ways can Facebook Dublin team visualize simple data?

After an incredible Tableau conference in London, I headed to Dublin for two more days of data viz and Tableau training.  Today’s class was about brain games and data visualization, my favorite class to teach.

I end the class with a simple exercise that I want to share.

I don’t remember where I found this idea on the web, but credit to whoever came up with the idea.  (UPDATE 14-Jun: Thank you to readers Michael Cristiani and Joey for reminding me that it came from this post from Santiago Ortiz on the visual.ly blog!)

It always amazes me how many different ideas people come up with.  The purpose isn’t necessarily to get them to only use what they’ve learned; it’s more of a way for them to be creative and have some fun.  Inevitably several people create pie charts, the only reason being that they know I hate them.

There were about 25 people in the class and they came up with 73 ideas in about 10 minutes.  Pretty good ROI!

My agenda for #TCCEU13. Did I mention we’re hiring?

#TCCEU13 is now nearly upon us.  I have two primary objectives for this conference (in this order):

1. Find a BI Engineer.  We’re actively look for a BI Engineer to work out of our Dublin office.  This person will be the first BI Engineer in our Dublin office, so you’ll have a ton of freedom to make the job what you want it to be and to build the team there.
2. I’ll be speaking along with my colleague Namit Raisurana.  Come see our session in the Westminster Ballroom at 11:30 on Wednesday.  It looks like Tableau learned from #TCC13 in San Diego and moved Facebook to the big room.  The crowd better turn out.  We have swag to give away.

Similar to all previous Tableau conferences, it’s hard to decide which sessions to attend because there are so many great topics and speakers.  I’m shooting for:

Tuesday
* 9:15-10:45 – Keynote (Christian Chabot and Chris Stolte)
* 11:00-12:00 - Targeted Marketing: Turning Visa Data into Actionable Marketing Insights (Simon Gatenby, Visa)
* 12:30-1:10 – Rapid Fire Tips & Tricks (I’m debating presenting something)
* 1:15-2:15 - Show Me the Money: Data and its Visualisation, Post-Financial Crisis (David Bholat, Bank of England)
* 2:30-3:30 - Genomes, Tableau and Huge Data at the Wellcome Trust Sanger Institute (Matt Francis, Welcome Trust Sanger Institute)
* 3:40-3:50 – Rapid Fire Tips & Tricks: When is PowerPoint Tableau and When is Tableau PowerPoint (Craig Bloodworth, Zen Master, The Information Lab)
* 4:00-5:00 - Exploring Data, Generating Insight & Driving Action at Barclays (Carl Allchin, Peter Gilks and Lee Mooney, Barclays)

Wednesday
* 9:00-10:00 - Keynote: How Data Intake is Like Dietary Nutritional Consumption (JP Rangaswami, salesforce.com)
* 10:15-11:15 - Getting From Data to Decisions: Decreasing Time to Capability at Cisco (Dan Murray, Interworks; Paul Laza, Cisco; Rob Higgins, Cisco)
* 11:30-12:30 - Creating a Culture of Data at Facebook (Andy Kriebel and Namit Raisurana, Facebook)
* 12:30-1:10 – Rapid Fire Tips & Tricks
* 1:45-2:45 - Keynote: Future Global Trends: A Fact-Based View (Hans Rosling)

If you’re going to the conference, come find me…especially if you’re interested in talking about coming to work at Facebook.  Find me on Facebook or Twitter and let’s grab a drink.

Was Theo Walcott’s performance for Arsenal driven by motivation for a new contract?

With two matches to go in the EPL season, Arsenal is in a battle for a Champions League spot with Chelsea and Tottenham and Theo Walcott is leading scorer.  He started out the season in great form, then suddenly tapered off immediately after he signed a new deal with the club.

In Arsenal’s last two matches, Theo has scored two great goals very early on in the game, at the 2’ mark against the champions Manchester United and 20 seconds into the most recent game against relegated QPR.  Theo is back in form.

I was interested to see how Theo has impacted Arsenal’s overall performance.  Use the viz below to see how Arsenal performs when Theo does or does not play (Arsenal have a higher winning % when he doesn’t play, though in a small sample size) and when he does or does not score (their winning % increases by 20% when he plays and scores vs. when he plays and doesn’t score).

In other words, Arsenal need Theo to play and score.

Learn About Tableau

Download the data here and the Tableau workbook here.

Done is better then perfect

I was going through files on my laptop this morning and stumbled upon this picture I had captured back in December.  I can’t recall where I found it, it may have been here, so apologies to the creator in advance for not giving proper credit.

Many of the projects I work on are prototypes, which makes this “law” especially true for me.  And this is one of our mantras at Facebook, basically get stuff done. 

Keep this in mind for your next project.  I can guarantee you that any project you’ve ever worked on is not perfect and any project you will ever work on will not be perfect, so don’t waste your time trying to be perfect.  Good enough is perfectly acceptable and helps you move onto to other projects where you can continue to make an impact.  Focus on impact, not perfection.

And yes, I did intentionally use improper grammar in my title.

Tableau Tip: Creating a primary group from a secondary data source

Mike Roberts, our Tableau consultant from InterWorks was helping one of our users last week and sent along a great tip for creating a field in your PRIMARY data source with a field in your SECONDARY data source.

Step 1: Add ‘Helper’ sheet and drag desired field from your PRIMARY source on to the ROWS shelf.

Step 2: Add matching field from SECONDARY data source and nest it next to the existing field on the ROWS shelf.

Step 3: Right-click the SECONDARY field on the ROWS shelf and select ‘Create Primary Group’.

NOTE: make sure you check the Include ‘Other’ check box

Step 4: Verify the group is now in your PRIMARY data source.

That’s it! You no longer need to blend or use the SECONDARY SOURCE field.

There is one gotcha to be aware of: If a record is added to the dimension you’re grouping in the primary data source, a new person in this example, you will have to regenerate the group or the new person will automatically get put into the “Other” group.  Creating a primary group does not dynamically update.

Artic Sea Ice Volume: A Radar Graph vs. Line Graphs

UPDATE (May 2, 2013): I have removed 2013 from all charts.

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I posted an article on my Facebook page the other day asking if this radar graphs about artic sea ice volume works.  The comments were mixed.

I think this radar graph is merely ok.  Radar graphs, in general, are hard to read because relationships and trends are not easily discerned.  Some of the issues I see with this graph include:

• It’s difficult to see the entire pattern over time.  I see the pattern is spiraling in, but does it change year by year.  Ask yourself this: How does 1999 compare with 2004?  It takes a lot of work. 
• It’s very hard to follow the ice volume labels around the chart.  I bet you found this as well in the example above.
• The author only included September.  Why?  The data is available by day.  Get the data here.
• Are there any seasonal patterns?  You can’t answer this question.

I created a couple of different views for your consideration in Tableau.  You can download the workbook here.

Learn About Tableau

In my version, I’ve addressed all of the problems I mentioned above.

• In the upper left graph, you can easily see that the artic sea ice volume is trending down.  
• The upper right chart allows you to see two things:
  1. The seasonal patterns
  2. Comparisons by year:  The comparisons across years can be see because the years get darker as the data gets closer to 2013.  You can see the lines get darker as you look down the graph.
• The Daily Volume graph makes the cyclical patterns much, much clearer.

How would you visualize this data?  Do you agree that these line graphs work better than the radar graph?

Notes from the Visual Business Intelligence Workshop: Day 3 – Now you see it

Day 3 of the Visual BI workshop was the day I was looking forward to the most.  I was very interested in hearing Stephen’s approach for analyzing data, which he covers in his book Now you see it.  There was one common theme throughout: keep things simple and clear, but don’t dumb it down.

Here are my key takeaways/notes:

• The word “see” in the title represents the analytical thought process: Search => Examine => Explain (SEE)
• Things to look for in a skilled data analyst: interested in the data, curious, self-motivated, imaginative, open minded and flexible, skeptical, honest, has a sense of what’s worthwhile, attentive, methodical, analytical, synthetical, has an eye for patterns, knowledge of the data, knowledge of effective data analysis practices
• The context we perceive is influenced by the surroundings.
• Exceptions can be a result of:
1. Erroneous data
2. Extraordinary events
3. Extraordinary entities
4. Randomness
• Highlight exceptions that are out of the range of “normal” or “standard”
• Always ask “Compared to what?”
• The tools we use need to make common interactions easy.  The tools should allow the train of thought to continue.
• Cycle plots are useful for cyclical and linear patterns.
• Linear trend lines on time series can be misleading; use with caution!  Consider moving averages as an alternative.
• Log scales are useful for measuring rates of change.  Lines with similar slopes will have similar rates of change.
• When looking for leading and lagging indicators, it can be useful to shift the time of one of the indicators.
• Bump charts are a good way to see how rankings change across different dimensions or measures.  Learn how to build one in Tableau here.
• The mean represents the quantitative center and is highly influenced by outliers.  If you want to look at dispersion around the mean, use standard deviation.
• The median represents the ordinal center and is better than the mean for showing the “typical” value.  If you want to look at dispersion around the median, use percentiles.
• It’s a good to idea to start an analysis by looking at a distribution of all values.  This will help you quickly identify outliers and the overall shape of the data.
• You shouldn’t remove outliers from an analysis until you understand why they are outliers.
• This is a really cool analysis of pay ranges by level and gender.  You could easily include a strip plot on this.
  
  

That’s it!  Three days of learning that I’ll never forget.  These courses were easily worth the money.  You’ll be able to apply so much immediately upon returning to your regular job.

Notes from the Visual Business Intelligence Workshop: Day 2 – Information Dashboard Design

Day two of Stephen Few’s three-day Visual Business Intelligence Workshop centered around his book Information Dashboard Design.  This class included quite a bit of critiquing of dashboards from “BI” vendors, a look at some of the better work, and a bit of hands on creating our own designs.

Like day one, these are the key points I wrote down (nowhere near the entire content of the course) that we should all reinforce in our work.

• Well designed dashboards and well designed software allow for rapid visual monitoring, which has a three-phase analytical approach:
1. Scan the big picture
2. Zoom in on important details
3. Links to supporting detail
• The visual display of a dashboard needs to match the reader’s mental model.  If the reader does not have a mental model, then you should sit with them to develop one.  Avoid asking them “What do you want your dashboard to look like?”, rather get a sense for what questions the reader expects to be able to answer.
• Be aware of the 13 common mistakes in dashboard design
• A great way to convince people of how simple data visualize can be is through Stephen’s “Graph Design IQ Test”.  Answering the questions wrong is pretty funny.
• There are four characteristics of a good dashboard design:
1. Exceptional organization
2. Data is condensed in summaries
3. Data is specific to and customized for the task at hand
4. Concise, clear and often contain small display mechanisms
• Never ask people what they want their dashboard to look like.
• Common dashboard data consists of:
1. Measures of what’s currently going on
2. Each compared to something to provide context
3. Each evaluated to declare its qualitative state
• Don’t design a dashboard only to highlight problems and exceptions.  The dashboard should be meaningful even when all is well.
• Objectives of visual design:
1. Eliminate clutter and distraction
2. Group data into logical sections
3. Highlight what’s most important (Place what’s always important on the upper-left)
4. Support meaningful comparisons / give your data context (this was a them that came up over and over again)
5. Design for aesthetic appeal (but don’t add fluff to add fluff)
• Use soft, natural colors
• Soften the background of the dashboard (Stephen likes to use a soft yellow)
• Charts and text should be crisp and clear
• Use good fonts (stick to Sans Serif on dashboards)
• Only include one font style per screen
6. Navigating to additional important needs to be easy and should support our train of thought.
1. Scan the big picture
2. Zoom in on important specifics
3. Link to supporting details

Stephen Few’s Financial Statement Bullet Graph – Every CFO should have one of these!

Stephen showed this incredibly intuitive example of a financial statement built with bullet charts during the Information Dashboard Design course on Wednesday.  I love how it clearly walks you through the statement.  It almost reminds me of the NCAA tournament bracket.

With Stephen’s permission, here it is. Click on it to see a larger, crisper version.