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

The two charts below show the S&P 500 Net Income by Sector for both 2008 and 2009 and recently appeared in BusinessWeek.  What really caught my attention is that these column charts are a little bit like a waterfall with the only difference being that these start from zero.  Prior to using the Waterfall chart utility, I created these by hand.  The workaround to get a hidden or shaded set of bars is more difficult than it really needs to be.

[source]

Below you will see my versions of the charts using the same data and Excel.  I didn’t include the gray shaded series because I do not think that it adds any value.  Also, I do not have the text box calling out the title and final value because there is a column at the end that shows what the value is upon finish.

Below is the 2009 version in Excel.  Another aspect to this chart that I do not like, even in my version, is the x-axis label being set at a 45 degree angle.  The angle of the labels along with the removal of the gray shaded bars makes it a little difficult to align the label with the value up the chart.  This was not a default option, but something that I changed.

What I really wanted to share was the layout of my data before clicking on the Waterfall chart utility.  All I had was labels and values for each year as shown below.  The chart utility did all of the work by setting up a table with all of the data needed to construct the chart properly.  Here are the steps I went through:

1. Setup the data in a table
2. Click on the PTS Charts tab in Excel’s ribbon
3. Select Waterfall Plot

1. The range of data is already populated if you were within the data before clicking on the utility
2. Change any of the options (I used the defaults)
3. Click OK and that’s it.

I did change some of the default formatting options after the waterfall charts were created to get them more aligned with the charts at the top of this post.  Overall, I was very pleased at the ease of creating these charts with the utility.  A waterfall chart is a great option when showing how different segments make up the total.  For example, an Executive could use a waterfall chart to show how each of the business segments impact the revenue or profit numbers like, A/R, Cost of Goods, Transportation, Fix Costs, etc..

If you are interested in this utility, visit the PTS site by clicking here.

Note: This utility is an affiliate product where Support Analytics will receive a small commission if this product is purchased via one of the links.

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Waterfall Charts is a post from the Support Analytics Blog

Over the past few years I have accumulated a bunch of different resources that I find extremely valuable.  Most of these utilities/products are for Excel, but not all of them.  Historically, you could find an ad or link to these products scattered throughout my blog pages.  I have combined all of the affiliate products I support onto one page that is called e-Store.  Now the main pages of this blog should appear less cluttered.

You can find this page by clicking here or by clicking on the e-Store link in the header of this blog.  There are some great utilities, Excel add-in products and e-books that you should check out.  Many of them will make life easier and some will enlighten.  Here is a summary list of what is available.

1. Waterfall Chart (Excel Add-in)
2. Box and Whisker Chart (Excel Add-in)
3. Dot Plot Chart (Excel Add-in)
4. Learn Excel Formulas (Tutorial)
5. Dashboard Reporting with Excel (Tutorial)
6. MicroCharts (Excel Add-in)

If you have any products that you would like to have included on this new page, please contact me.  The links above contain affiliate products that, if purchased, generate a small commission for Support Analytics.

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Support Analytics e-Store 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

I wanted to take a minute and announce the Support Analytics Facebook Fan site.  The screen shot below shows the link on all of the blog pages and it also shows the fans.  Simply click on the section of the sidebar to become a fan.

Below is a screenshot from the fan site.  One of the reasons I created this site was to bridge the gap between twitter and this blog.  By that I mean the following. Twitter only allows 140 characters, making any kind of meaningful interaction limited.  On the other end, this blog has much lengthier posts.  By now having a Facebook fan page, this will allow another medium to exchange ideas that can be short and quick. 

In the screen shot below, you can see the tabs across the top of the fan page that contain a News tab and Twitter tab.  The News tab streams this blog’s RSS feed to Facebook while the Twitter feed goes to the Twitter tab.  Now all of the information is streamed to one source that many people are already using.

On the Fan page I will be sharing ideas, tips and insights that I may not post here on the blog.  I recently posted a link to a page on Tableau’s new product that will be released soon. Please take a minute to check out the Fan page and become a fan!

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I want to thank you for your continued support and subscription. This blog is better because of you.

Data Analysis & Visualization on Facebook 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

Back in December I wrote a post about using the arithmetic mean and median when analyzing data.  This is a follow up post that shares some insights from Naomi Robbins, author of Creating More Effective Graphs.  The following paragraph was Naomi’s response to my question, “what would you provide if asked what is a typical customer size?”

“I’d give the median together with the graph below. The graph, modeled after Figure 4.1 of Creating More Effective Graphs, shows all the data, as Jon suggested. I’d say something like:

"I provided the median (shown by the black line in the figure) rather than the mean (shown by the light cyan line) since as you can see from the figure, the mean is not a typical value. There are no actual customers who have revenues near to the mean value because customer 5 influences the mean so strongly since its revenues are so much higher than the others. Half of our customers have revenues less than the median while the other half have revenues greater than the median. The middle half of our customers have revenues that are between the dotted lines."

For a slightly larger customer base I’d jitter the points. For a much larger customer base I’d replace this strip plot (also called a one-dimensional scatter plot) with a box plot. By box plot, I mean a Tukey box plot. I object to every software program and every author redefining box plots so that the reader can’t read them without an explanation.

The figure was drawn with R. However, it is easy to reproduce it using Excel or other software. [Robbins]

I think this goes back to one of my original points, which was that many people just to provide the mean and it can be very misleading.  The graph above that Naomi provided illustrates this point clearly.  The light cyan line [mean] isn’t even close to the majority of the data points.  The median is much more representative of a typical customer value, but also not perfect.  Combine the median, mean, quartiles and actual values and now you’re providing real value.  Looking at this chart, it clearly shows the grouping of typical customers, outlier and where the median and mean fall.  Thank you Naomi for the insights!

If you are interested in providing a guest post, please contact me for more information or to submit a proposal.

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Mean and Median – Part 2 is a post from the Support Analytics Blog

There is quite a lot going on this Holiday season and I hope to be back with a more regular posting schedule after New Year’s.  I hope you have a safe and happy Holiday(s)! 

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I want to thank you for your continued support and subscription. This blog is better because of you.

Happy Holiday’s, Merry Christmas and Happy New Year! is a post from the Support Analytics Blog

In the corporate world I see this issue quite frequently.  Specifically, I will hear a request where the verbiage doesn’t align to what the requestor is ultimately looking for.  To illustrate, I have included an example below that shows ten different customers within a territory.  For each customer the total revenue year-to-date is listed.  To make the illustration relevant for this example, I listed Customer 5 with revenue that is exponentially higher than the rest. 

Now here’s the question I typically hear:

"What is the average customer size (revenue) for Territory A?"

Here is what that really means most of the time:

"What is a typical customer size (revenue) for Territory A?"

You may think it’s semantics, but it’s really not.  I don’t want to turn this into a statistics lesson, but average (mean) doesn’t always translate into typical.  Because Customer 5 is such an outlier, the average (sum of all customer revenue divided by count of customers) will be higher than if that customer fell into the typical range like the rest.

I have included the median revenue amount for the ten customers, which I think is probably a better predictor (in general) than the mean or average.  The median is simply defined as the number in the middle.  In reality, Customer 5’s revenue could be 875 zillion dollars and the median amount wouldn’t change.  When there are thousands of records and you need to know what the typical amount is, it’s often safer to choose median unless you want to take the time to calculate min, max, median, std deviation and mean to compare.

"In probability theory and statistics, a median is described as the numeric value separating the higher half of a sample, a population, or a probability distribution, from the lower half." [source]

Now the real question that would need to be answered is, can a typical territory have one very large customer or is this a unique situation and should not be considered normal?  Answering the preceding question will make all the difference in what calculation to use.  Most often I will include both.

It’s my belief that most people are simply familiar with the term average because it’s so commonly used.  The underlying reason that average is more prevalent in analysis is probably due to the fact that it’s very easy to calculate.  Before spreadsheet software was available that automated the median calculation, it was much more difficult to get a median amount even with a calculator.

As a data analyst, it’s prudent to know the difference between mean and median and when each is applicable.  Telling the CEO/CFO that the typical customer is roughly $131,000 when one customer is atypical and the true amount is more like $57,000 can be a career changer.

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Data Analysis – Do You Really Mean Average? is a post from the Support Analytics Blog