## VizWiz

Launch, grow, and unlock your career in data

## Tableau Visual Guidebook - simple techniques for making every visualization useful and beautiful

Interested in learning best some data viz best practices?  Check out Tableau’s Visual Guidebook.

## Heat Map: Manchester Derby Results Since 1907

The Soccer by the Numbers blog continues to provide me great inspiration. After Manchester City destroyed Manchester United 6-1 on October 23rd (at ManU), they blogged about the results and provided a bit of statistical analysis.  City has long been the “noisy neighbors” to United, but they now are doing their best to buy a title, rather than grow their own talent.  Soccer by the Numbers posed this fundamental question:

We now know that the outcome of the match was truly unusual. But how unusual?

They created the table below, which is a frequency distribution of scored lines since 1907 of matches at Manchester United.  To use the chart you simply identify the score line by going across City’s scores then down ManU’s scores.  So the 6-1 scoreline has occurred 2.99% of the time since 1907.  Clearly this was an unusual result.

But I think this table could be improved.  I made these changes:

1. Changed the numbers to percentages and rounded to one decimal.  Two decimals is unnecessary precision.
2. Removed most of the gridlines so that the lines separate the data from the categories
3. Formatted the results as a heat map.  I chose a red-white two-color scheme since Red is ManU’s color.  This makes the largest percentage of result very obvious.  For example, you can now easily see, without having to scan across all of the data points, that 1-1 is the most common score line…boring result!
4. Formatted the totals as a second heat map.  I chose a brown-white scheme for these.  The totals show you the % of the total goals scored for each time.  ManU has scored one or two goals 64.2% of the time while City has scored one or two goals 58.2% of the time.

Which format do you like best?  Does one make the story easier to interpret than the other?

## Using a non-zero-based axis: I don’t understand why “experts” can’t get it right

Nielsen is widely regarded as providing exception analysis of consumer data.  In fact, many of the best analysts I work with spent many years at Nielsen and they have been very interested in learning data visualization best practices because they now understand the benefits.

I agree that Nielsen’s insights are often fantastic, yet I don’t understand why they can’t present their analysis more appropriately.  I guess my larger concern is that when a company with the influence on analysts like Nielsen has presents data visualization, even simple ones, so poorly, it make the abuse more and more pervasive.  I’m seriously considering send a link to this post to the author of the presentations I’m about to review.

I received two annual reports the other day, both authored by a high-level employee who was supplemented by many other resources, so I can’t lay the blame on any one person, but more on Nielsen as a whole.  Here is a summary of the charts they presented:

Presentation #1
Zero-based axis = 6
Missing or non-zero-based axis = 88
Pie chart with no color to differentiate the slices = 2
Charts well done = 6/96 = 6.3%

Presentation #2
Zero-based axis = 37
Missing or non-zero-based axis = 36
Smoothed line charts = 7
Charts well done = 37/80 = 46.3%

Hopefully this means someone told them presentation #1 had a lot of chart junk and they made an effort to improve presentation #2, but I doubt that’s actually the case.  It’s easy to see that most of the charts were created in Excel, which will automatically set the axis to start somewhere other than zero if the numbers in the chart are large.  I don’t know the specific business rules that Excel uses, but they should be changed.

Here is a representative example of the charts they created which did not have a zero-based axis. I’m holding out hope that this wasn’t done to intentionally deceive the reader, but to emphasize the subtle differences between the data points.

I recreated the chart as a dual-axis chart with the primary axis starting at zero and the secondary axis set to Excel’s default.  Also note that I created a line chart since this is time-based series data, which typically means you’re wanting to see the overall pattern.

Clearly these imply a very different story.

Ok, we see what’s wrong, but how could Nielsen have presented the data more effectively? You have two primary options.

Dot plot

• You can replace bar charts with dot plots so that the sequence over time is de-emphasized
• Dot plots don’t require a zero-based scale
• Dot plots force the reader to refer to the scale before comparing two values
• Sizing and coloring the bubbles by change over prior year would speed up the reader’s analysis of variances between variables

Non-zero-based line chart with special alerts

This is the most effective method if you insist on NOT using a zero-based scale.  Stephen Few sums it up best in Show Me the Numbers (page 169):

You should generally avoid starting your graph with a value greater than zero, but when you need to provide a close look at small differences between large variables, it is appropriate to do so.  Make sure you alert your readers that the graph does not give an accurate visual representation of the values so that your readers can adjust their interpretation of the data accordingly.

Nielsen can learn a few lesson by reading some of the great data visualization books by Few and Tufte, or they could hire resources that know what they’re doing and allow those resources the freedom to make the best practices viral.  The alternative isn’t good for any of us.

## When you use a smoothed line chart, your data is not affected, it’s misrepresented!

This past week, I was watching a presentation on Q3 performance and up pop a bunch of charts that were clearly created in Excel with smoothed lines.  I hadn’t seen smoothed line charts in quite a while, so I was taken aback.  I almost, but thankfully didn’t, stand up and call out the junk.

It was incredibly clear to me that the smoothed lines were distorting the data, not much, but distorting it nonetheless. And I have a problem with THAT!

Let’s first take a look at some examples to see how badly the data can be distorted.  The first chart is obviously the smoothed lines.  Nice and pretty, I agree.  It makes me feel like I’m going up a chairlift then skiing down the slopes of Keystone, Colorado.

I added the gridlines, though I would never do this if I were presenting this for real, so that you can see where the points truly intersect.  It should be abundantly clear now that the line is trying to connect points that don’t exist.  Look between July & August 2009 or between August & September 2008.  In both of these instances, and many more across the chart, the lines go beyond where they should in an attempt to make the chart nice and smooth.

If I were to look at this quickly, I might think that my sales increased from July to August 2009, but in fact, there was a slight decrease.  In order for the line to connect smoothly to September 2009, the line has to go around August 2009.  Think about all of the people that don’t used zero-based axes.  Imagine how distorted their data could look.

Contrast the smoothed line chart to this standard line chart.

You now easily see that sales decreased from July to August 2009.  It’d be tough to interpret anything from this chart between the months because the lines clearly connect month to month.  The smoothed lines lead you to believe that there is more data being connected.

Now, let’s look at how the smoothed and straight lines look on the same chart.  For illustrative purposes, we’re only looking at 2008.  Now that dip after August really stands out.

Jon Peltier of the Peltier Tech Blog sums it up best in his post about the charts to choose and avoid in Excel 2010:

Smoothed lines are abused. If you are plotting measured data, the only valid connecting curve between points is a straight line (or a line which is fitted to a function that comes from a physical model of the data). A smoothed curve implies that the data goes places where it has not been measured. Smoothed lines without points are even worse, because the person trying to interpret the chart doesn’t even know what points on the smoothed curve belong there.

My advise?  NEVER use smoothed lines.  The ONLY possible outcome is misinterpretation.

Let me wrap up with what I find to be a bit of a funny line from Microsoft’s help for creating smoothed lines:

When you use this procedure to soften the jagged edges of a line chart, your data is not affected.

This is very true.  Your data is not affected, it’s merely misrepresented.  Semantics?

## Is it possible to share 101.4% of Facebook? Chart of the Day thinks so!

There's a bad stomach bug going around this part of town and I think I might know part of the reason why. Today, my good friends over at Chart of the Day published this pinwheel pie chart and I think the filling might be bad, because the pie sure looks ugly.

Here are some of the problems with this chart:

1. IT'S A PIE CHART!
2. Colors are re-used, or maybe they are so similar it's hard to tell they're different
3. The slices are not in order, making it even hard to look up the values (notice how Microsoft is listed ahead of Peter Thiel and some others)
4. The dollar amounts are based on their portion of \$100B, yet they total up to \$101,350,000,000.
5. Correspondingly, the percentages add up to 101.4%. How can you have more than 100% of a total?

To highlight the differences, I created the following charts with Tableau.

What I attempted to do here was show the Stated % Share (gray bar) from the pie chart compared to the "Restated % Share" (black bar). I calculated the Restated % Share with the following formula:

SUM([Stated \$ Value])/TOTAL(SUM([Stated \$ Value]))

NOTE: A special thank you to Marc Reuter (@tableaujedi) for enlightening the ATUG crowd today with some Jedi magic and for showing how to use the TOTAL function. I had never used it before (and didn't know about it either), but I find it totally awesome! It'll be so useful!

Basically, I'm taking the value stated on the pie chart and dividing it by the total value of the pie chart. This gives you the Restated % Share. The label is the difference between the Restated % Share and the Stated % Share.

The chart on the right represents the % variance number (as identified by the label on the left) multiplied by \$100B (the estimated total value of Facebook).

If I were one of these shareholders, I'd be a bit concerned about the math. This isn't chump change! In the end, Chart of the Day may have made a \$1.35B miscalculation. Oops!

Download the Tableau workbook here and you will see the original and restated data like this:

## What does it take to survive in the English Premier League?

If you love soccer, then it’s likely that you follow the EPL.  My favorite team?  ARSENAL!  Did you see the incredible goal by RVP Saturday against Everton? You may never see better technique and now he’s only one goal behind Thierry Henry’s team record for goals in a calendar year.  Please Lord, keep RVP healthy for a full season!

And if you love soccer and you love stats, then check out Soccer by the Numbers. Chris Anderson writes many quality posts and recently he blogged about points and relegation.  I wanted to take Chris’ ideas a step farther.  I needed a richer dataset than what Chris was able to provide, so I downloaded the final tables (i.e., standings) from the EPL back to the 2001-02 season from ESPNSoccernet. You can download the full dataset here.

I borrowed (or is it stole?) Steve Wexler’s technique for providing instructions (hover over the EPL logo to see what I mean).  There’s lots of interactivity in the viz, so first check out the instructions, then start clicking around.

Answer this: How many teams have qualified for the Champions League with a negative goal differential?  Who were they? What else can you tell me about the team(s)?  Post your answer in the comments.

## ATUG Webinar – Jedi Tricks and Brilliant Dashboarding (Thursday Dec 15, 1-4pm ET)

While we prefer that you join us in person, this month we are offering the Atlanta Tableau User Group meeting as a webinar.  Anyone is welcome to join.

RSVP (if you plan to attend in person) – http://www.tableausoftware.com/usergroups/atlanta-dec15-11

Please join 5 minutes early as we will start promptly at 1pm.

Agenda:

1. Jedi Tricks – Marc Rueter, Tableau Software
• In this session, Marc will show ten powerful tricks that will make you truly the master of your analytics.
2. Hands-on Training: Tips & Tricks – Andy Kriebel, Coca-Cola
• Authoring Brilliant Dashboards
• Ten powerful tricks that will speed up your work
3. Information - January & February Meetings

-- This will be a hands on session --

URL: http://bit.ly/drY3e5
Toll free: (877) 906-9811
Conference code: 2561415367

## Join us at the next ATUG meeting – December 15th 1-4p @ Coca-Cola

The next ATUG meeting will be December 15 @ 1PM ET

Where – Coca-Cola - 1 Coca Cola Plaza, Atlanta, GA 30313, Nicholson Room – Central Reception Building

Important – You must check-in with security and tell them you are there for the Atlanta Tableau User Group meeting.

Plan to arrive at least 15 minutes early!!

Agenda:

1. Jedi Tricks – Marc Rueter, Tableau Software
• In this session, Marc will show ten powerful tricks that will make you truly the master of your analytics.
2. Hands-on Training: Tips & Tricks – Andy Kriebel, Coca-Cola
• Ten powerful tricks that will speed up your work
• Authoring Brilliant Dashboards
3. Information - January & February Meetings

-- This will be a hands on session - Bring your laptop and Tableau with you --

## Sports Chart of the Day: When you want to emphasize rank, sort appropriately…please!

Dear Sports Chart of the Day,

I’ve been patient. I’ve added comments (which never seem to get posted/approved). And I’m frustrated. I love your blog, but you really need to make some simple changes to your charts.

You often post charts like the one below (from this blog post).  You often say things like you said in this post:

Here are the top 17 teams in the NFL based on points scored and points allowed and how many wins those teams would be expected to have based on those numbers (actual record in parentheses)

As a reader, it’s very clear that you are emphasizing top-to-bottom rank.  Those of us that live in the western part of the world have learned to read left to right.  You want us to look at the “top 17 teams”, yet the chart reads right to left.  Please, please start sorting your ranking charts in the appropriate order, descending in this case.  Trust me, this will improve your message.

Respectfully,
Andy

## When income grows, who gains? Find out for yourself.

Anyone who follow this blog know how much I despise pie charts, but there are times when I tip my cap to someone that does them well.  As much as it pains me to say it, pie charts are not ALWAYS evil.

The viz below is a great example of how to use a pie chart well (from the State of Working America blog):

1. There are a maximum of four slices to this pie chart
2. You can very quickly see how dominant one slice is versus the others
3. The colors contrast well enough to not have to constantly refer to the legend

Click on the image to interact (you will be taken to the source site).

I guess what caught me most off-guard about this chart is the summary text when you choose 2002-2008.  All income growth went to the top 10%.  I had no idea!  A great chart can indeed tell a great story, or better yet, let the reader discover the story for themselves.

## Choosing a good chart type – A Cheat Sheet

Charles Schaefer’s session at TCC11 “Understanding and Working with Chart Types” included this decent flow diagram for choosing a chart type.  As the title says, it’s “A Thought-Starter”.  In other words, use it as a way to get you going down the right path.  This absolutely shouldn’t be taken as gospel; use your common sense.  For example:

1. Don’t ever create 3D area charts.  In fact, don’t ever create a 3D chart of ANY kind.
2. Never create a circular area chart
3. When created a stacked column chart don’t connecting the same colored bars with lines.

The flowchart originates from a blog post back in 2006 on The Extreme Presentation Method blog.  This blog, in turn, links to an interactive version of the chart chooser on Juice Analytics.  Although Tableau does a lot of this thinking for you, definitely check out the interactive tool to get a feel for some best practices and download the sample excel workbooks to quickly recreate these charts yourself.

## Tableau Tip: Adding dynamic Top X labels in 9 easy steps (add Bottom X for even more goodness)

There a good chance you’ve run into this scenario before, maybe in a past life in Excel or with Tableau:  You have a chart with a bunch of bars or columns or maybe a line chart, and you want the top 5 values labeled.  Ideally, the chart would look something like this:

With Tableau, you can manually assign labels to these points, but wouldn’t it be better for the points that are labeled to change dynamically based on the data you have selected?  There’s no easy way to do this in Tableau, but as always, there’s a workaround that’s quite simple once you implement it once or twice.

Be patient as you read your way through this; it might seem a bit complicated, but I’ll detail every step.  Let’s get started.

1. Drag the Order Date dimension onto the Columns shelf, right-click on the pill and choose All Values (This changes the Order Date field from a Discrete dimension to Continuous; not a critical step, simply personal preference)

2. Drag the Sales measure onto the Rows shelf (I’ve filtered the Order Date to 2010 only, but that’s not necessary either)

3. Create a parameter, I named mine Top X, with the following properties:

NOTE: I could have chosen to always label the top 5 or top 10 values, but I want the consumers of the dashboard to be able to select the number of values they want to see labeled, thus the need for a parameter.

4. Right-click on the “Top X” parameter and choose “Create Calculated Field…”  Name the field “Top X Label” and enter this formula:

IF INDEX()<=[Top X] THEN SUM([Sales]) END

I’ll explain the need to use the INDEX function in a bit.

5. Right-click on the “Top X” parameter and choose “Show Parameter Control”

6. Drag the Top X Label calculated field (from step 4) onto the Label shelf on the Marks card.

7. Here comes the trick: Right-click on the Top X Label measure and choose “Edit Table Calculation”

8. In the Table Calculation dialog box, change the Compute Using option to Advanced

9. In the Advanced window, change the Order Along settings to the Sum of Sales Descending.  This will force the Top X Label field to index the values based on Sales from highest to lowest (thus the reason the calculated field compares to the INDEX() function).

That’s it!  The top 5 points are now labeled. You’re chart should look like this:

You can use the Top X parameter to pick the number of values you want to label.  Even if you filter the data, maybe to only show the East Region, the labels will still work properly.

But this is Tableau, so let’s take it a step farther.  Maybe you need to label the top 5 and the bottom 5.  There’s a neat little way to do this too.

1. Duplicate the Top X parameter and rename it Bottom Y and the show the Bottom Y parameter control

2. Duplicate the Top X Label calculated field, update it to reference the Bottom Y parameter and rename it to Bottom Y label:

IF INDEX()<=[Bottom Y] THEN SUM([Sales]) END

So now what?  There’s no way to add a second label!  True, but there IS a way to add a secondary axis.

3. Drag Sales onto the Rows shelf, right-click on it and choose Dual Axis

4. Remove “Measure Names” from the color shelf (we don’t need different colors since we’re using the same measure twice)

5. On the Marks card, click on the carrot on the upper-right of the card and choose Multiple Mark Types

6. Click the right arrow twice until you see “SUM(Sales) (2)”, then drag the Bottom Y Labels calculated field onto the Label shelf

7. Right-click on the Bottom Y Labels field, choose Edit Table Calculation, then repeat steps 8 & 9 above.  The only difference is that the advanced table calculation should be in ascending order for the Bottom Y Labels:

You’re done!  You now have an interactive chart that allows the user to pick the number of top and bottom values they want to see.  Interact with it, download it and see how it works for yourself.

Finally, I would like to thank Joe Mako for his help in walking through this situation.  He helped me with the formula and advanced table calculation for the Top X Label field.

## The Best NFL Kick Returners Ever!

Using Tableau is a never-ending journey of learning and today was no exception.  It began with this chart from the Chart of the Day:

Overall, this chart is well executed, except I would have sorted the players in descending order.  But I wanted to take it one step farther, so I downloaded the data from pro-football-reference.com and got to work in Tableau.  I wanted to be able to compare:

• Not only the combined kick returns, but also the top punt returners and kickoff returners separately.  I wanted to know which players were the best in each category.
• Players that played for one team versus more than one team
• A player’s kick return ability compare to his punt return ability

Finally, I wanted to be able to filter each chart by the Top X Players for that chart.  This is where parameters come in handy.

I started this post by saying I learned a few things.  I learned to:

• Make the user experience easier by creating a list of instructions like Steve Wexler at DataRevelations.com always does. Hover over the NFL logo to see the instructions for this viz.
• Filter by a Top X parameter when there’s more than one item on the color shelf (like on the Total TDs chart).  Check out this discussion on the Tableau forum for an explanation (thanks to Joe Mako for the link and help making it work with a scatter plot).

To answer the question in the title of the viz, Devon Hester is very dangerous…a clear outlier, he’s a player that is “numerically distant” from the rest of the players.

## Telling a Good Story: Effective presentation of analytical results

Another great TDWI webinar today: “Effective presentation of analytical results” by Jonathan Koomey. Much of the content will be review for those that have read books by Edward Tufte and Stephen Few (many of the examples used were taken from Show Me the Numbers), but it’s always good to hear someone else’s take on effective presentations.  My key takeaways:

• Don’t forget the decision maker
• Use structured storytelling to present the key results
• Document your work (results & methodology) so that others can recreate it
• Break up graphs and tables into two categories
– Those that give you insights
• Charts and figures must focus on the data
• Improving poor visualizations is a great way to teach best practices
– Even simple changes can make a huge difference in communication
• My favorite quote: “Data are dull only when chosen poorly and presented badly”

UPDATE (11/11/2011): The webinar video is now available for replay. Watch it here.

## Ammunition: Driving Smart Decisions at Your Organization

Lyndsay Wise of Wise Analytics and Francois Ajenstat of Tableau hosted a terrific webinar today about self-service BI.  I especially appreciated the discussion about IT’s role as an enabling and support organization, rather than bureaucratic and controlling.

There are lots of battle wounds from sparring with IT about BI and if you are in an organization where it’s challenging to get IT support for self-service BI, rather than resistance, then this might be the ammunition you need.

UPDATE (11/11/2011): The webinar video is now available for replay. Watch it here.

## Batch Geocoding: Convert addresses or locations into latitude-longitude coordinate pairs

I’ve been adding customers to the Penetration Reporting I presented at TCC11 (word is spreading and people love it!), but there are often records in our internal systems that do not match up with the master address list for our customers from Nielsen Spectra.

One of the great features of Tableau is data blending and this project is a perfect example of how you might use it. I have my sales and internal customer list in SQL server, but the location information, including latitude/longitude are in an Excel file.  Tableau allows me to integrate these data sources via a key field, store number in this case.

As I referred to earlier, this works perfectly for ~95% of the records, but there are ALWAYS stores in our internal system that do not exist in Spectra.  Here’s a sample of missing stores:

To prepare the data to address this problem I take the following steps:

1. Concatenate the address fields together in Excel with the formula CONCATENATE(TRIM(Address)," ,",TRIM(City),",",TRIM(State)," ",TRIM(Zip))
2. Copy all of the rows in the new “Full Address” column

Now the fun and magical part begins.  A colleague led me to the tool Batch Geocoding, which basically takes text strings and returns the latitude/longitude coordinates.  Here’s how it works:

1. In the “Input” box, paste the data you copied from Excel above

2. Choose your output format and click the “geocode” button

3. Watch the magic as the tool populates the Output box.
4. Copy all records from the Output box and paste into Notepad

5. Save the Notepad file in txt format
6. Open the TXT file in Excel
7. Copy the records from the TXT file and append to the end of the master customer list from Spectra

That’s it!  So simple!  I have no idea how it works, but it does.  Of course, it’s not going to be 100% accurate, because the addresses may not exist, but it does tell you how well it was able to match the records.

Definitely keep this link in your toolkit.  I’ve also added a link on the right side of this blog under “Useful Data Sources”.

## Makeover of a Makeover – Waterfall vs. Side-by-side Bar Chart

One of the great things about the data viz world is that people are always willing to listen, learn and share.  Cole Nussbaumer over at storytelling with data (a great blog you should follow) recently conducted a visual makeover on some horrible charts submitted during a class she was teaching. and she was willing to share her data with me so that I could make my own viz.  Where else do you experience such camaraderie?

The improvements she recommends are fantastic, but I recommended one improvement to this chart she created:

Typically when I see side-by-side bar charts I’m looking to compare the bars that are next to each other.  However, in this case, the bars are not necessarily related; they are simply a list of expenses and income next to each other.  I recommended she create a waterfall chart like this one done with Tableau:

To me, a waterfall chart communicates the expenses vs. income story of this data more effectively

1. The bar sizes make comparisons easy.  It’s clear that Programs are the largest expense and Grants are the largest income.
2. You can easily see the total variance without having to do the math in your head.
3. Other Expenses are much larger than Other Income.  I wonder what’s included in those expenses.  Looks like an area for investigation.
4. This group should probably focus a bit more on Sponsorships so that they’re not so dependent on Grants.

I could have included labels for all of the bars, but I wanted to show the patterns and relative sizes without the numbers being a distraction.

You can download the original Excel data here and/or the Tableau workbook here.

P.S. I chose red/green bars for two reasons: (1) most people understand red as negative and green as positive when reading financial figures and (2) to annoy my friend Steve Wexler of the Data Revelations blog (another you should follow), who hates this color scheme more than anyone I know.

## November 4, 2011

TCC11 has passed, but everyone in attendance is anxious to get their hands on all of the great presentations, workbooks, etc.  For those of you not able to go, now I’m making them available to you as well.  Enjoy!

Session Materials
We packed the 2011 schedule with so many great sessions and learning opportunities that it may have been difficult to choose which session to attend. Visit our conference content page to watch the video recordings and download session materials. We will be adding more materials as they become available.

Save the Dates for our 2012 Tableau Customer Conference
European Customer Conference – April 2-4, 2012: W Hotel, Barcelona, Spain – apply to speak
North American Customer Conference – November 5-8, 2012: Hilton Bayfront, San Diego, CA

The next ATUG meeting will be November 17 @ 1PM ET

Who - All ATUG members and guests
What – The November in person hands on meeting
Where – Coca-Cola - 2 Coca Cola Plaza, Atlanta, GA 30313, USA-G

Important – You must check-in with security and tell them you are there for the Atlanta Tableau User Group meeting. They will then call me to come get you. I will have people in the lobby to help usher you to the room.

Plan to arrive at least 15 minutes early

Agenda:

1. Tableau Customer Conference Presentation – The Holy Grail of Strategic Decision Making (Andy Kriebel, Coca-Cola)

2. Training – Iron Viz Challenge: Create a beautiful viz with unfamiliar data in less than 30 minutes (David Newman, CSE Inc.)

3. Open forum

• Tableau Customer Conference feedback, highlights, etc.
• December meeting – location, time, agenda

-- This will be a hands on session - Bring your laptop and Tableau with you --

## Tableau is about to knock your socks off again – A sneak peek at Tableau 7 features

Now that TCC11 has ended, the cat is out of the bag.  Tableau 7 is coming, and coming soon; it should be available for download around the beginning of the year.  Christian Chabot demoed many of the new features (but I’m sure not all) of Tableau 7 Desktop and Tableau 7 Server.

Some of my favorite new features (which we got to play with at TCC11) include:

• Filled maps!! (though they don’t go down to the zip code level)
• Wrapped maps
• Using parameters in filters, bins, reference lines/bands
• Improved statistics
• Lots of improvements to make the most prominent features more easily accessible

But there is one sad feature: the end of Null Island (this is the place where invalid geographic data are placed).

Here is what’s coming in Tableau Desktop:

## TCC11 session feedback positive, except for the guy drinking the Pepsi

Wednesday I received the feedback from my session/presentation at the 4th Tableau Customer Conference.  I had a blast presenting my case studies (The Holy Grail of Strategic Decision Making) and the interaction from the audience made it a wonderful experience.  I presented three case studies:

1. Collaborating for Value – An era of winning (this was an interactive analysis of retail prices by store by price point)
2. Customer Segmentation – Planning for growth and identifying opportunities
3. Penetration & Voids – Growing the business by finding the gaps

As has been my experience with presenting this same material both internally to our customer teams and externally to our buyers, the audience was able to gleam insights by “seeing” the data.  It’s a pretty cool experience when the data and the tool tell the story without you having to say a word.  Tableau makes it oh so easy.

What surprised me most about many of the customer presentations I attended was the lack of using Tableau live to show off their work.  Maybe my material lends itself more to that type of interactivity, but I would highly encourage anyone presenting findings from Tableau to use the tool live; you’ll get immediate feedback and you’ll be able to answer questions on the fly.

So how’d I do?  First up is a chart from Tableau that ranks all of the presentations, not only the customer presentations.  I’m the circle that the red line points to.  According to Tableau, this placed me in the top 3 of the customer presentations.

But how about the feedback on my presentation?  The response was overwhelmingly positive. I always get anxious waiting for the feedback!

I asked someone that was drinking a Pepsi to leave, jokingly of course, but I wonder if he gave me the “poor” ranking.  I tend to be a bit too directly and sarcastic and sometimes it’s taken the wrong way.  Or maybe it was someone that didn’t win the t-shirt I gave away.

Anyway, thanks to all of you that attended my session.  It was an awesome dialogue with the 75+ of you.  Hopefully we’ll catch up again in Europe.

Gratefully,

Andy

## Sex, Politics, Disasters and Cheating - What I’ve learned in 800 days of the VizWiz blog

It’s hard to believe that I started this blog on August 13, 2009, 800 days ago (yes, I know my first post wasn’t until Aug 17).  The time has absolutely flow by and I’ve learned so much.

For example, I’ve never shaded an axis in lieu of a color legend until today.  Heck, I never even thought about until I saw a bunch of vizzes on Tableau Public that were using this style. I’ve always included the color legends, but it’s really not necessary on a dual-axis chart like this with only two colors.  You might even call the use of a color legend chart junk in this case.

From what this viz tells me, I should stick to some specific topics if I want to drive traffic:

1. Sex
2. Politics
3. Cheating
4. Disasters

Does that surprise you?  It doesn’t surprise me since those are topics that generally dominate the news.

One final stat of interest: 90% of the total traffic to my blog has occurred since I posted this viz as part of the Flowing Data contest about STDs.  Nathan must be a pretty popular dude.

## Tableau Whitepaper: 5 Best Practices for Creating Effective Dashboards

The latest Dashboard Insight contains a whitepaper written by Tableau that walks you through a five-step process for creating effective dashboards.  Basically, it’s a recipe card.  I’d encourage you to read it below or here.

The whitepaper below is pretty much identical to a whitepaper Tableau wrote in 2008, except it looks cleaner and shows some of the features added in Tableau 6.

## Introducing the #TCC11 Excitometer

What’s an Excitometer?  It’s a handy little column chart that measures your level of excitement for all Tableau Customer Conferences.  It averages all of your results and provides you some feedback if you need a pat on the back or a kick on the tail.

Fill it out for yourself.  Maybe the Excitometer has a message for you.

## Practicing what I preach – A self-critique

Back in August I critiqued and improved a viz created with Many Eyes.  Some of my comments included:

1. The map incorporates both color and size, which are set based on the measures picked on the right.
2. Brushing a State(s) in any chart highlights the State(s) on the other charts.
3. Hovering over a bubble or bar reveals the details of the stats chosen.  The stats update based on the selections made.
4. I’m able to use the color scale consistently through all of the charts.

Now that I have learned how to use simple state polygons in Tableau, I realized that what I previously created was unnecessarily complex.  Here’s take 2:

I made a few easy to implement improvements:

1. States are now polygons instead of bubbles
2. The option to choose a size has been removed as it no longer would add any value
3. The table on the right is now shows only the top and bottom 10 (I learned how to do this today via a discussion on the Tableau Forum that, not surprisingly, included a sample workbook for the solution from Joe Mako)

I also didn’t lose this set of functionality:

1. Brushing a State(s) in any chart highlights the State(s) on the other charts.
2. Hovering over a state or bar reveals the details of the stats chosen.  The stats update based on the selections made.
3. The color scale is consistent through all of the charts.

In the end, I’ve reduced the viz from four charts to two and made it much easier to interpret the results.

See, I can practice what I preach and I love learning something new everyday.

## Global Consumers Go Bubble Popping

Nielsen is at it again.  Just when I thought they were making strides in the right direction, they bring me back to reality.  As with most Nielsen articles, this one is well written with lots of facts that are explained in an easy to understand manner (kudos to them for writing well).  There’s even a nice bar chart that ranks survey responses.  But then, they throw this junk in there:

Is it just me, or is this screaming out for another bar chart?  Maybe they’re afraid they’ll bore their audience with another bar chart, so they throw in an unnecessary ranked bubble chart to gain your attention.  Are they THAT desperate?  Why not represent it like the bar chart below?

Or better yet, show the data itself.  It tells the same story, but only simpler and you don’t have to try to infer any additional meaning from the size of the bubbles.

My faith and resolve are not shaken though.   Eventually they’ll get annoyed by my emails and write me back.

## Tableau Customer Conference Pocket Agenda (and the can't miss sessions)

Four days!  Need I say more!  I can’t wait.  Tableau sent out the pocket agenda below so that we attendees can prepare ahead of time.

Here are my plans for the breakout sessions (my intent is to attend those that I think will give me the most ideas to take back to work):

 Tuesday 9:45-10:45 Tableau Inside…Intel Brahms 4 11:00-12:00 Revenue Management @ American Airlines Brahms 4 3:15-4:15 The Holy Grail of Strategic Decision Making(I better attend this one since I’m the presenter) Brahms 3 4:30-5:30 How to Start a User Group(I’ll be one of the hosts for this session) Mozart Wednesday 9:45-10:45 Which Way Business Intelligence? Brahms 1-2 11:00-12:00 Best Practices for BI Success Brahms 1-2 3:15-4:15 Tableau & Statistical Analysis Puccini 4:30-5:30 Tips & Tricks from the Wild Brahms 3 Thursday 9:45-10:45 Your Brain & You Brahms 1-2 11:00-12:00 Making the Business Case for Analytics Brahms 1-2

I hope to see you there.  Please find me and say hi.

## October 4, 2011

It's almost here...the 2011 Tableau Customer Conference.  If you're anything like me, the anticipation is making you positively giddy!

I'm taking the Tableau Desktop certification test during the conference and the study materials were just released.  I've embedded them below.  It's a great guide, even if you're not taking the test.

## Is your work hard or challenging? Don’t ever say hard (it’s a weasel word)!

Ted Cuzzillo over at datadoodle posted an intriguing cliche on his latest blog postTed said “Simple is hard.”

I immediately recalled a conversation we’ve had with our kids and their Target teachers (Target is the gifted program in Georgia).  In the Target classrooms, the students are not permitted to say something is “hard”; they say that it’s “challenging”.  This struck me as a bit revolutionary in the use of those two words.  Many of use them interchangeably, but should we?

Definitions (limited to those that pertain to this conversation):

hard
1. difficult to do or accomplish; fatiguing; troublesome: a hard task.
2. difficult to deal with, manage, control, overcome, or understand: a hard problem.

challenging
1. offering a challenge;  testing one's ability, endurance, etc: a challenging course; a challenging game.
2. stimulating, interesting, and thought-provoking: a challenging suggestion.
3. provocative; intriguing: a challenging smile.

Read them again. I find the subtle differences fascinating.  My “work” is incredibly challenging, but is it hard?  I love that I get to test my abilities everyday.  My work is stimulating, thought-provoking and intriguing.  Why would I ever say it’s hard?  Maybe that’s why I don’t see my work as “work”.  It’s fun and it’s CHALLENGING.  The challenge is what makes it fun.

At our house we have a couple of weasel words: hard and try.  We do our best to never use these two words because, in our opinion, they’re cop out words.

When you say you’ll try to do something, do you really mean it?  How about saying “I’ll do my best”?

## State Polygons in Tableau – A must have, useful, simple template

I recently read a post by Albatrosa Analytics that included a map of the continental US with the states shaded.  I’ve see many people do this in the past, but the data set created by all of the points was HUGE.  Now with the polygons feature of Tableau you can easily create a shaded map.

In the viz below, I’ve used the data blending feature to combine the polygons with data by state.  I can see so many uses for this.

Download the data for creating the polygons here and the sample state data here.

## September 2, 2011

Please join Mischa Uppelschoten of UPS and Dan Murray of Interworks as they host the Atlanta Tableau User Group on Wednesday, September 21 from 2:00 - 4:00 pm at UPS.

This event is a time to share, listen, network and discuss experiences and opportunities with other Tableau users.

Information:

• Wednesday, September 21 from 2:00 - 4:00 pm
• UPS, 55 Glenlake Parkway NE, Atlanta, GA 30328

Agenda:

• Introductions / Networking
• Webinar – Tableau @ Oxford University: A Classic Tale of “Land & Expand” – Andy Cotgreave, Tableau Software
• Break / Networking
• Exploratory Vitas: Ways to Become Acquainted with a Data Set for the First Time – Hands-on activity – Dan Murray, Interworks
• November Meeting Discussion

Bring your laptop equipped with Tableau because this will be a hands-on session.

## Overcoming resistance & building champions - Albert Birck, Maersk Line - as presented at ATUG

The August Atlanta Tableau User Group meet-up featured Albert Birck from Maersk Line as a special guest speaker, all the way from Europe. Albert gave a wonderful, humorous presentation that focus on:

Overcoming Resistance in BI Projects
• Outgunned and outnumbered
• Feeling like an outlaw
• No funds, only problems to solve
• Locked-in

If you have a few minutes please participate in Albert's Performance Management survey done together with Henley Business School. The results will be available free of charge and he expects it to be very useful also for how you use business analytics to support performance management projects:

https://www.surveymonkey.com/s/perf_mgmt

Thank you, Albert, for sharing your insights and time with us.

## Tableau Tip: 7 easy steps to create a combination chart with overlapping bars & a line

UPDATE (13-Sep-2016) - I have created a video tutorial for this tip, which you can find here.

As a follow up to my previous post, which showed how a dual-axis chart looks in Tableau (compared to the Excel version I wrote about in the post previous to my previous post), I was asked by someone named Anonymous (I can never seem to identify him/her) to create similar instructions for building the dual-axis overlapping bar and line chart in Tableau.

Here are the steps I used to produce the chart (there are a billions ways to skin the cat, so take it for what it is):

Step 1: Place the Week dimension on the Columns shelf and the Measure Values measure on the Rows shelf

Step 2: Drag the Number of Records pill off of the Measure Values shelf to remove it

Step 3: Drag the Units pill from the Measure Values shelf to the right edge of the chart (i.e., the secondary axis).  You know you’re on the secondary axis when you see the dashed vertical line

You’re entire canvas should now look like this:

Step 4: Right-click on the Units pill, choose Mark Type => Line

You’re viz should now look like this:

Step 5: Right-click on the Measure Values pill, choose Mark Type => Bar

STOP!  Pop the top off that next bottle of Blue Moon.  Double-check your work.  You’re viz should now look like this.  If not, click the Undo button until you get back to a good spot.

Step 6: Drag the Measure Names dimension onto the Size shelf

Step 7: At this point, the bars are stacked.  So on the Analysis menu, choose Stacked Marks => Off

That’s it!  You’re done!  There’s some formatting you could do like adjusting the width of the bars, changing the number format on the axes, etc., but the essence of the chart is now complete.  Seven simple steps in 15 seconds (I wrote 90 seconds in my last post, but I timed myself this time).