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. 

So what are the alternatives?  The answer is a very common one, it depends.  Is the goal to show parts of the whole or is it to show a trend?  I think there isn’t really an optimal chart that shows both as well as a specific one for each.  Do I dare suggest that a pie chart would be an improvement at showing the parts of the whole for each period?  The alternatives below do not use the same data set and are just made up.

The first Alternative below uses Tableau Public to show each segment as a separate column chart.  This does a better job at showing the parts of the whole and trend of each segment.  By using the interactive version, which isn’t really fair when compared to the image above, it also allows the reader to see values on the hover.  I do like Tableau for creating these trellis type charts quickly and easily.  I did find a bit of trouble navigating Tableau and couldn’t seem to get rid of the “number of records” series that shows up in my version after Feb 10.  Regardless, I think you get the point that this may be a better alternative. 

The next alternative below is a line graph with multiple segments and one for the total of all the segments.  The total line would show what the total height of a stacked column would show.  This is a better way to show the trend over time, but it’s very difficult to make out the lines that fall below the 100 value on the y-axis.   Also, you can’t really tell how each of the parts make up the whole using this method.  I typically label each line instead of using a legend, but that wouldn’t work here because of the tight cluster at the bottom of the chart.

Another option would be to use a dynamic line graph that has a slider feature to quickly change between the segments.  With this feature you could click the up/down arrow to scroll through the data series.  This makes it quite easy to see each segment, but doesn’t allow you to compare one to the others.

The chart below is another alternative where the overlap option is set to “separated” for the stacked column chart.  I think this is a pretty good option because it’s much easier to see the parts of the whole and also the trend to some extent.  If you have more data series it could easily get unwieldy.

Finally, an alternative would be to use a panel chart (trellis display or small multiple) to show each month using the same axis.  This example uses bar charts, which make the comparison between the segments stand out better.  You could also use line graphs with the axis being the months and a separate panel for each segment.  The line graph panel chart would be better at showing the trend, but would fall short on the comparison aspect.

Below is an example of the panel chart using line graphs that show the trends quite well.  The downside would be the lack of comparing parts of the whole and also the small values do not show up well when the scale is the same for all charts.

In conclusion, I have scaled back my approach of never using a stacked column chart because when used correctly it can work as seen in the example where the segments are separated.  Using a default formatted Excel Column chart is not a good option.  If the true point of the data visualizations is to illustrate either parts of the whole or trend, then a panel display using bars or lines would be a better option.  Another option is to use a software package like Tableau.

What method would you use to show this data?

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Analyzing a Stacked Bar Chart is a post from the Support Analytics Blog

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.

Below is a screen shot from Tableau Public.

Next, you can see a screenshot from The Seattle Bubble where Tableau Public was used to create an interactive data visualization on their website.

Finally, the last screenshot shows a line graph created with Tableau Public.

There are a bunch of options and functionality that impressed me with this software.  Some of them that stand out are: low chart junk visualizations, stunning charts and graphs, interactivity of the data visualization once published online and finally, the ease at which the visuals can be shared and socialized online.  Sharing a chart or graph is as easy as sharing a YouTube video.

Tableau has some limitations that they set on the size and transfer of data using Tableau Public.  The current version maxes out at 100,000 rows of data and the ability to publish up to 50 megabytes online.  Those numbers don’t appear to be Google-like, but they probably don’t want this version used for commercial purposes, which I understand.  Also, any data visualizations that you create and publish to the web will not be private, they are public.  Did I mention the software is free?

Honestly, one of the questions I was going to ask Elissa during the demo was, “Do you know about other similar resources on the web that allowed for data uploads and online data visualizations (Many Eyes, Verifiable, Protovis) and if so how is this different?”  Seriously, after a few minutes of seeing the functionality and aesthetics, it was clear there is little comparison.  See for yourself and I look forward to sharing visualizations!

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Online Data Visualization: Tableau Public Released Today is a post from the Support Analytics Blog

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. 

Also, please feel free to comment with additional insights.

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

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Information Visualization or Data Visualization, You Decide [Poll] is a post from the Support Analytics Blog

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.

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Online Data Analysis and Visualization Tool [Poll] is a post from the Support Analytics Blog

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?

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Investment Growth Chart is a post from the Support Analytics Blog

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

[source]

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?

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Data Visualization – Don’t Overcomplicate Charts & Graphs is a post from the Support Analytics Blog

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. 

[source]

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?

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Highlighting Segments of Recession Data is a post from the Support Analytics Blog

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. 

[source]

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.

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Data Versus Information – Financial Bailout (Part 2 of 2) is a post from the Support Analytics Blog

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?

[source]

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.

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Data Versus Information – Financial Bailout (Part 1 of 2) is a post from the Support Analytics Blog

If you’re like me, this Dilbert comic may give you a chuckle.  I typically read Dilbert because there are some pretty funny strips and you need to laugh every once in a while, right?  As of late, a little laughter might offset some of the crying from looking at investment or 401(k) returns [or lack of] from 2008 and year-to-date. 

With the one below that was posted on March 9th, there is a message that I think is important to those folks that are creating charts and graphs and those that are viewing them.  My rule of thumb is to question the data or designer when I see a lot of "dressed up" data.  The more glitz and chart bling, the more it makes me question the data and creator.  A good data visualization or data presentation will speak for itself and doesn’t need chartjunk or gaudy colors to send a message. 

The old saying goes: "If you can’t dazzle them with brilliance, baffle them with buIIshit".  Well, if you apply this saying to data, it might go: "If you can’t enlighten them with elegance, baffle them with bling".

[source]

Updated per Jon’s link in the comments.  This one never gets old…

[source]

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Dress Up Your Data is a post from the Support Analytics Blog