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

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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

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

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

Below you will see a replica that I made using Excel and the fill effects formatting option.  It looks alright, but something still isn’t right.  What is the problem with this chart?

The problem is the y-axis and the scale that was used.  I don’t think this is a straight out misrepresentation in order to mislead, but it could be.  That’s the risk you face when manipulating the axis.  Yes, the columns take up a lot of space when the axis starts at zero, but that’s the correct method here.  To help illustrate my point, check out the exact same chart (below) with the y-axis starting at zero.

This version using the correct axis setting accurately shows that October, year-over-year, is not three times as much, but only about 1.5 times greater.  Also, look at the trend of the first replica chart.  The upward trend definitely has a greater slope compared to the replica with the correct axis.  To help prove this visually, check out the side-by-side comparison below using a trendline in the chart.  The slope of the chart on the left is much greater than the one on the right.  If you were presenting this data in something like PowerPoint or SlideShare, and quickly went to the next slide, the audience might not catch the axis starting at 5 and the steep trendline would be the point taken from the data.

Furthermore, forget the gradient fill and go with something like the chart below if you want to highlight the current year.

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Gradient Fill and Deception with Charts and Graphs is a post from the Support Analytics Blog

Besides the overpowering and inconsistent labels, I think the Baltimore Sun did a good job with its recent display of the percent change in the number of passengers from 2008 to 2009.  It may have been better to stick with the airport codes, like BWI, instead of writing out the airport names, but let’s not nit pick.  I bring this topic up again because I think small multiples or panel charts can be much more effective at visualizing data and, in my opinion, are under utilized in the business world.

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There has been some great work done by a few experts in the Excel and R fields on creating panel charts.  Here are a few resources that have examples and information on how to create panel charts in Excel and Trellis Displays in R.

• Peltier Tech Services
• Process Trends
• Charts & Graphs

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Visualizing Multiple Data Series’ in a Chart is a post from the Support Analytics Blog

Click here to view more details

I personally use 63 or 84% of these formulas on a regular basis (yes, I counted).  It’s amazing to see the results when I show someone how to use the NETWORKDAYS, VLOOKUP. CONCATENATE or TRIM formula.

This post contains an affiliate link.

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Excel Formulas From a Microsoft MVP! is a post from the Support Analytics Blog

Taken directly from their site, they claim the benefits of heat maps are:

• Increase Agility – Improve business agility through quicker analysis, better decisions and more effective communication.
• Reduce Risk – Rapidly identify trouble spots, before they are out of control
• Maximize Value – Ensure that attention and resources go where they bring the best return.
• Identify Opportunities – Discover underlying trends that point to high-value opportunities.

[source]

I don’t really mean to pick on this company; it’s just the one that I got an email about today.  I think the power of heat maps is actually part of its ineffectiveness, which is too much data.  The benefit of a heat map is suggested to be that you can fit a lot of information in a relatively small visualization.  If you were to create the same data using a bar chart, it would take up a few pages.  The downside of this heat map is there is too much going on with it.  The only information I can make out of it are the large outliers.  Ironically, the same is probably true if you were to create a visualization using a bar chart.  At least you would save $1295 by using a standard bar chart in Excel versus this software.

This heat map shows a ton of data where size and color of the boxes matter.  Intuitively, the bigger the box, the larger the number must be, right?  But what the heck to the colors stand for?  I cannot tell.  Also, note the logo images within the boxes – they make the label and value very hard to read.

Do you see value in heat maps?  Is this just a bad example that uses too many data points?  Please share your thoughts.

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Heat Map for Excel is a post from the Support Analytics Blog

• Ability to slice and dice using 20 million rows of data on a fairly basic PC (not server)
• The other was how quickly you can filter on 20 million rows
• Create Pivot tables quickly with 20 million rows and on the fly
• It’s free and will be included in Excel 2010

Here is another demo using Gemini and SharePoint.

[thanks to SmartData Collective]

What do you think? Do you think this is a huge breakthrough or just Microsoft/Excel getting with the times?

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Excel and Project Gemini is a post from the Support Analytics Blog

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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.

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