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If you ever sat through a live presentation that contained at least one chart, chances are that you have heard the term “eye chart”.  This is an expression used to make light of the fact that people sitting farther than about two feet away probably can’t see or make out the details of the chart due to its size.  Technically, it refers to the chart that is used by physicians or others to judge a person’s vision.  Personally, I’ve heard this term way too much and it prompted this post.

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Below you will see a stacked column (vertical bars) chart that has nine different segments covering eight months.  I have absolutely nothing against HubSpot and actually think they have some great services, tools and products.  I am simply using their chart to illustrate the problems with stacked bar charts and some alternatives.

I can think of a few reasons off the top of my head as to why people would use stacked bar charts. 

1. To show how each segment changes over time
2. To illustrate parts of the whole at any given time
3. A combination of 1 and 2 above

The problem is that a stacked column chart is not good for either of these requirements.  My feelings on these charts is not really anything new, as I’ve stated before on this post and also this post.  In my opinion, after you get beyond two series with like scales, a stacked column chart is pretty and pretty useless. 

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A special thank you goes to Elissa Fink of Tableau for providing me with a demo of Tableau Public.  With being a big fan of Tableau desktop, it should be no surprise that Tableau Public impressed me for many of the same reasons I like the desktop version.

Today is the official release of Tableau Public and the latest version of Tableau Desktop 5.1.  Below you will find some quotes from today’s press release:

SEATTLE, WA, February 11, 2010 – Tableau Software today launched a new product that brings public data to life on the web. Tableau Public, available for free, lets anyone who posts content to the web easily create interactive visualizations and publish them to blogs, web sites, Twitter feeds or anywhere online. Instead of viewing static charts or tables, Tableau Public lets people answer questions and share data interactively on the web.

Current alternatives for sharing data online are clumsy. Typically, data is pasted into tables and lists, or posted as files or catalogs that are difficult to use. Available at Tableaupublic.com, Tableau Public is helping to solve this challenge – bringing data to life on the web for ordinary people. With its interactive visualizations and dashboards, Tableau Public helps people start conversations based on data that is useful, beautiful and shareable. No special plug-ins are required, all that’s needed to see and use the data is a web browser.

In conjunction with the general availability of Tableau Public, the company is also releasing today Version 5.1 of its Tableau Desktop and Tableau Server product suite. Version 5.1 provides more analytic richness, better publishing, and increased scalability and performance. Analytical features include reference bands that provide context to a user’s analysis, bullet charts to evaluate related data, and intelligent data labels to call out the most critical data. New publishing features include rich formatting, streamlined toolbar design, more filter options, and a flexible layout.

I have yet to try out the newest version, 5.1, but I did see that it will have the ability to produce Stephen Few’s famous Bullet Charts without any tricky workarounds.  I can tell you that I’m excited to start using version 5.1.

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In the February 1, 2010 edition of BusinessWeek, I found the illustration shown below.  It certainly contains some good information and data that can be easily read.  My question is, would you consider this a Information Visualization, Data Visualization, Information Graphic, Statistical Graphic, Chart/Graph or something else and why?

Sorry about all the polls lately, but I think this is an easy way to respond without having to comment. 

[poll id="4"]

Also, please feel free to comment with additional insights.

Note: RSS readers have to go to the website to view the poll.

Not too long ago, I got a tip from someone on Twitter with a link to a site called Verifiable.com.  Upon further investigation, I learned that this site is similar to Manyeyes.com in that you can upload a data set and, using the tools on the site, create data visualizations. 

At first glance, the site seems somewhat plain.  After digging into what the site is about, I quickly learned that they utilize sound and popular theory in the data visualization field.  On their about page, the first line that explains the features is:

“A clean, low-chartjunk philosophy — no shadows, no pie charts, no 3-D bar graphs, just the ink you need. [verifable.com]”

Well, simply reading that peaked my interest because they use similar principles that I follow when creating charts/graphs.  No frills.  I like the fact that you can create charts that don’t have excessive grid lines, shadows, weak labeling and limited charting options.  Below you will see a few examples from their site.  You can also follow the links to see the visualizations in an interactive environment.  As you will see, there is a lot of data (hover over), many different options and some good visuals.  Granted some of the charts I had no idea what they were trying to show, but in general this site gives you a seemingly good tool to apply charting/graphing best practices.

[Interactive version]

[Interactive version]

Verifiable also offers a Pro version of their tool where you can keep your data and visualizations private and receive premium support.  The cost is minimal with the Pro version going for $29.95/year. 

I didn’t try to upload a data set to give the site a full trial, but it definitely looks interesting.  I am not sure how much demand there is for online data visualization using a tool like Verifiable.

One of the benefits I truly enjoy is having USAA as my insurance company.  It only took me one phone call to their customer service center to know why they consistently rank in the top of companies for customer service.  I can think of a few big companies that could learn a lot from how USAA treats their customers/policy holders. 

In their recent magazine, I quickly noticed the chart below that is called, "The Snowball Effect" while flipping through.  The heading that was cut off states the following:

"What’s the hardest-working investment tool you can use? The power of time. Beth, Bob and Bridget all invested $2,500 at the same 6 percent rate of return.  But see how compounding made Beth’s account grow? That’s the value of starting early."

Going back to high school and college one of the first lessons one learns is the time value of money and compounding effect.  I won’t get into that, but what I did want to touch on is the chart below that left me speechless for a bit.  There are so many things wrong with it that it wasn’t even worth taking out my red pen.  

I recreated the data from the chart in Excel (shown below) and used fictitious numbers for the middle of the graph.  Each person starts with the same money and each has an end amount.  So I basically filled in the blanks.   I know my chart doesn’t have Beth with her arms raised in celebration or decimals, but it’s definitely a lot cleaner and easy to understand.  This isn’t rocket science, is it?

One blog that I visit regularly deals with creating better PowerPoint presentations and is written by Jan Schultink.  I am in the data analysis and visualization business, but typically use presentation software to run meetings, webinars or communicate data analysis results.  So, creating effective and powerful presentations is important so the hard work that goes into the analytics isn’t lost in the communication.

Below you will find the data visualizations that Jan showed on his site.  I would agree that the second image is better at showing the difference versus the first one, but not by much.  The probability of 1 in 76 is definitely more meaningful (puts it into context) than the 1.3% data point.  What I have an issue with is that the area of both columns (2nd image below) is almost the same with the one on right being slightly larger.  What makes the two columns represent different values is the number of spheres within each column.  The one on the left contains 8,000 circles (I’m assuming because they are not very countable) while the one on the right has 76 circles.

Honestly, I’ve never created a data visualization like this to even know how to make this chart with so many spheres that are microscopic.  My guess is that it could be done using Adobe Illustrator or similar software.

Risk of maternal death as a percentage:

Risk of maternal death as 1 in x number

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Here’s where I think we can improve the visualization without losing effectiveness.  In a fairly popular post that I did a while back, I suggested the best method for showing a single data point is the following image.  I think that the 1 in 8,000 compared to 1 in 76 data points are powerful enough and take up very little space to convey the message.  Another option would be to compare the two data points and only show the difference as a single metric.   It would go something like this:

Illustration for showing a single data point:

So my question would be – which method do you think would be more effective in a presentation?

Below you will find some interesting ways to highlight sections in charts or graphs.  I typically like to know more about data visualizations when I see certain trends in the data.  This method of "calling out" certain sections is nothing new or earth shattering.  One aspect that I really like about the area chart below is that the sub charts utilize the unused space. 

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The series of area charts below also use a method of highlighting sections of the chart that denote specific events that took place in the history of the stock market. I’m not sure I like the color choices or texture, but the ability to highlight sections can help a reader get to the point of the chart(s) a little quicker.

[source]

Once a chart is created in Excel, these highlights can be done easily by using text boxes and setting the transparency higher.  In the first example above, these callouts can be created using the drawing features.  It’s fairly manual, but you can accomplish these feats without a graphic design program.

Do you think this is an effective method for "calling out" data?

1. Data Versus Information – Financial Bailout (Part 1 of 2)
2. Data Versus Information – Financial Bailout (Part 2 of 2)

In the first post on Tuesday, I discussed that the original set of data (shown below) didn’t go into some of the basic things I would do mentally when first seeing the matrix. 

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The figure below shows the same data set with the 2007 revenue spent on lobbying removed.  I don’t think there’s too much value in the trend from what I can see, especially with only two years worth of data.  What I think may be valuable is the ratio of money spent on lobbying to the bailout awards.

To better see how the bailout awards were given, I looked at the money spent by these companies in 2008 on lobbying compared to the awards.  I computed a lobbying return, which is highlighted below and sorted in descending order.  Bank of America faired the best by spending just over $4 million on lobbying and getting a $35 billion award.  I’m sure there are many other factors that come into play and data needed to do a full analysis, but these are just the types of things I do when looking at data.

I think sorting is one of the most useful formatting tools you can use when displaying data.  Yet, I often see it not used correctly.  In the original matrix, the sort is descending based on the 2008 lobbying spend.  Maybe it’s just me, but the first thing I did was visually sort the data by bailout award in descending order.  That’s really what we’re after, right.  I want to know who got the largest bailout.  Now the context of the article plays an important part of the way the data should be displayed, which I’m not going to get into.  The overall message is to be conscientious of the sort and ensure it makes sense for what you are trying to depict.

The Financial Lobbying information below is a great example of the difference between giving someone data and providing them with information.  The designer stopped far too short when putting this matrix together because they left all the work for me to do.  If you’re like me and you see this grid, what are first few things you do?

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When I saw this, I immediately did these things:

1. Quickly read the title and sub title
2. Scanned the companies looking for a familiar one
3. Started calculating percentages of each to the total
4. Thought about how much these bailouts are of the total bailout package

I am only looking for some basic statistics and context for this data.  I need to put it into perspective and try to tell a story.  I recreated this data in Excel and added a few simple columns to illustrate my points.  Also, we aren’t even talking about charts or graphs, just a simple matrix.

First, I have the same matrix with one additional column for the percent each company is of the total financial bailout spend.  Also, you’ll notice I abbreviated the numbers in the millions to save space.  Finally, I removed the zebra striping because it really isn’t needed in such a small data set.

In the next example below, I added an additional column that represents the percent each company is of the total bailout package.  Now I can see that these eight large financial companies make up 26 percent of the total bailout spend assuming a $700 billion total.  What this does, is put the data in some perspective versus just showing a bunch of numbers. 

In part 2, I will show you a few more changes that I made to the matrix that speaks to the revenue columns.