Here’s my promise to the readers of this blog – this will be the last chart that I pull from BusinessWeek because it was poorly designed.  Take a look at the bar chart below that was shown in the October 18/24 issue that I just picked up.  Folks, this isn’t even charting 101, this is a pre-requisite.  A multi series 3-D bar chart, really?  I’m disappointed and they should be embarrassed because they need to be better than what’s shown below.  I probably won’t renew my subscription that I’ve had for at least 5 years, not because of the charts, but because I find less and less interesting articles with each new issue.  Then, throw in the awful charts and my choice is an easy one.

[source]

Tip: Four line graphs for each segment with time on the x-axis would be a much better choice than this even if this one wasn’t in 3-D.  Boring? Maybe.  Effective? Yes.

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How to Make A Graph or Chart Really Bad is a post from the Support Analytics Blog

[BusinessWeek, Oct 4-10]

I am pulling for the new management to do a better job, but I’m not very hopeful.  Want to make this chart better?  Simply remove the third dimension and make it a simple, plain old line graph.  If I had to guess, I’d say this might just be a case where the functionality lets me create it in the software, so it must be good, right?  Wrong.  Maybe Stephen Few is right in that great data visualizations start with well designed software that do not let the creator choose an option like this.

What do you think?  Blame it on poorly designed software or uneducated designers?

P.S.

Here’s another gem from the most recent copy of BW.

[Source]

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Just Another Illustration Why Data Visualizations Need Help is a post from the Support Analytics Blog

This grid shows how unorganized data can really cause a problem for the reader or viewer of the data.  This list is great if you want to check out the health benefits of eating a certain food item.  However, if you know that there is a history of heart disease in the family and you want to eat as much food that will protect your heart as possible, you need to scan each item to figure it out (as shown below to illustrate). 

[Publisher of chart]

The creator of the chart didn’t even put similar benefits in the same column as shown in my illustration above.  An interactive version would have been best so that the reader could sort or filter as needed to get what they want.  The next best route may have been to publish a few lists with the most prevalent benefits listed in descending order.  Another option would have been to list all of the health benefits in their own column and put check marks where that benefit is found in each food. 

Organizing or optimizing data that makes sense will go a long way with the readers.

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Eat Healthy Chart is a post from the Support Analytics Blog

I think the psychedelic layout of the bubbles adds a confusing element to the visualization that is unnecessary.  There are a bunch of different ways to show this data that would be more straight forward and easier for the readers to understand.

[source]

Do you think the visualization layout/design adds or takes away from the data?

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Acquisition Speculation Chart is a post from the Support Analytics Blog

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

[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

A better use of color may be for each quarter within the year.  Using the chart below, it would make more sense to have every first week of the month always in one color, like blue.  Then, at least you could easily compare the first week of each month quickly.  I’m not even going to touch the chart title not matching to what is actually being displayed in the graph – days vs. weeks.

You really can’t make the color mistake if you used a line graph, just saying.

[Source]

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Rainbow Chart – Twitter Messages Per Day is a post from the Support Analytics Blog

[image source]

[image source]

It appears that small vendors scored better than large vendors in every single category of complaints as shown in the chart above.  In the chart below, you will see that small vendors appeared to provide more benefit to the customer/client than large or medium vendors.

[image source]

There’s a lot more to the article if you are interested in business intelligence. 

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Business Intelligence Vendor Size is Important is a post from the Support Analytics Blog

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

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