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.

Here are a few spreadsheet type apps for the iPhone:

• Quick Office
• ispreadsheet
• Mariner Calc

In my opinion, the best option is still to view web-published visualizations from a company like Spotfire or Tableau to see near real-time data, trends and visualizations.  Let’s forget about trying to build spreadsheets on your phone, because that isn’t going to happen.

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

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?

One question that I often ask myself when performing a year over year or period over period analysis, is if the values really matter or just the change.  Sometimes the values for Q2 of this year and last year are important and sometimes only the change matters.  A good example of when the values do matter is typically when showing market value changes.  It’s often important to know where a company falls in the overall market as well as how they are trending or changing over time.  Many times showing the values will make the most sense and give the reader a full picture.

On the other hand, sometimes the total values don’t matter as much and you just need to highlight the changes.  In this scenario, it’s often cleaner and more effective to simply show the change from year to year as a percentage or value.  This removes the extra data series and also gives a clear picture of the change where the graph below does not.  For example, can you tell if the year-over-year change for Information Technology is the same as Energy in the chart below?

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Now, if only the change was shown, the values would be both positive and negative.  You could then sort in descending order to see which sectors did the best and those that did the worst.  I often see charts like the one above, which are well designed.   I rarely see a chart with only the change.  To meet somewhere in the middle, I see charts like the one here, but with the percent change as a label for each sector.

Below the chart in BusinessWeek appeared this text:

“Profit margins at health-care, utility, consumer staples, and consumer discretionary companies have actually improved in the past year.”

By only showing the change in descending order, the sectors highlighted by the above text, would be much easier to see and would include change amounts.  One other note – I think the wider gap between Materials and Telecom is just an oversight. 

What do you think – show only the change, show the values or show both the values and change?

There are two features that I think are valuable and can help you visualize conversions on a website or blog.  They are:

1.  Heatmap

The heatmap option allows you to visually see where on the site most visitors click.  This can be extremely helpful for trying to sell advertising, place key products or featured items.  Some other features of the site include:

• The ability to exclude your own IP address so the results are not skewed
• Setting up tests to see what people are doing on a page
• Reports
• Email notifications

2.  Confetti

The confetti option allows you to see dots in color where visitors are clicking.  The list option lets you see who the top 15 referrers are, search terms, operating systems, browser and so on.  If you use one of these services, please share your feedback in the comments section.

Check out their demo here.

Some other providers that have similar services are:

• Labsmedia
• Clickdensity

A new book on data analysis was released yesterday by Michael Milton called Head First Data Analysis.  The Head First books have gotten rave reviews and I’ve never actually read one until now.  I was able to be involved in the technical review of this book, which gave me a first glimpse into the content.  I will say there were a few of pleasant surprises as I went from chapter to chapter.  Some of them being; exercises that utilized the open source statistical program R, examples using relevant data, theories and ideas, and simply hitting on all the major topics without going so deep it was no longer relevant.  I also liked the fact that almost anyone could pick up this book and start applying the concepts immediately. It is designed for a very easy read with rich content.  Here is a note from Amazon that describes what this book is about:

“Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you’ll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others.

Whether you’re a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool.
You’ll learn how to:

• Determine which data sources to use for collecting information
• Assess data quality and distinguish signal from noise
• Build basic data models to illuminate patterns, and assimilate new information into the models
• Cope with ambiguous information
• Design experiments to test hypotheses and draw conclusions
• Use segmentation to organize your data within discrete market groups
• Visualize data distributions to reveal new relationships and persuade others
• Predict the future with sampling and probability models
• Clean your data to make it useful
• Communicate the results of your analysis to your audience ” [source]

I think the Head First series methodology and design creates an experience where the reader is able to learn and apply the concepts quickly.  It was a pleasure working with the folks at O’Reilly.

1) How much did the Subprime (yellow) dollars change from month to month or between any points?

2) Is there even a value for Subprime after about March 2013?

3) How many times do you have to reference the legend to figure out which color is what label?

The only value that is easy to visualize and analyze is the Agency data that is in black and the first data series.  Beyond Agency, this data visualization is useless.  I tried to get the data to make a better chart, but couldn’t locate it.  Instead I ask that you visualize 7 lines in a line chart, one for each value and the 7th one for the total.  Using that chart, you could easily see the trend in each value and also the overall change. 

Yes, this chart is colorful and caught my eye, but it’s also worthless. 

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Below is another example using similar data where the data visualization is completely ineffective. 

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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.

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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.

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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.