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. 

[source]

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

[source]

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.

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.

1. What inspired you to write this book?

I was inspired to write this book by the same thing that inspired me to write the other two: a need that wasn’t being addressed. In this particular case, the need is for people to develop data sense-making skills. People throughout organizations are responsible for this to some degree, but few have been trained. Most analyses that are done in the normal course of business can be handled using simple visual analysis techniques that anyone can learn, but few people have. They haven’t, because until now no resource existed that they could turn to. Plenty of available resources teach statistics, but nothing teaches the simple and practical methods that rely on our eyes to explore and make sense of quantitative data.

2. What makes this book different from "Show Me The Numbers"?

Show Me the Numbers focuses on effective ways to present quantitative information using tables and graphs. Now You See It focuses on effective ways to explore and make sense of quantitative data. Before you can present data, you must figure out what it means. This new book will help people discover and understand the stories that live in their data more quickly and thoroughly.

3. A common question about your past books, what software did you use to create the visualizations in this book?

I used Excel to create most of the examples in this book, not because it’s great visual analysis software—it isn’t—but because everyone has Excel and I wanted most of the analytical methods that are taught in the book to be accessible to everyone. Probably about a third of the examples illustrate visualizations and techniques that require better visual analysis tools than Excel, including Tableau, Spotfire, Panopticon, JMP, Advizor Analyst, and R. I included these to show people how much more they could do if they had a better tool. The techniques that require better software aren’t difficult, but they either involve graphs that aren’t available in Excel (for example, box plots) or ways to dynamically interact with data that Excel doesn’t support.

4. The Amazon description says it’s valuable for those trying to understand business data.  Would those outside of the corporate/business sector benefit from this book?

The data analysis techniques that I teach in this book apply to data of all types, not just business data. Just as in Show Me the Numbers, most of the examples that you’ll find in Now You See It involve business data because most of my readers are businesspeople, but everyone who works with quantitative data in the non-profit sector, government, academia, or anywhere else will find this book easy to understand and applicable to their work.

5. What specific question or problem does this book help solve for a reader?

The primary questions that Now You See It addresses are:

• What aptitudes, attitudes, and skills are needed to analyze data effectively?
• What are the basic types of quantitative data analysis?
• For each type of analysis, what are the most effective graphs for visualizing the data?
• For each type of analysis, what patterns in the data are most meaningful?
• For each type of analysis, what are the most effective ways to interact with the data to bring the meaningful patterns to light?
• What are the most effective ways to navigate through the process of analysis?
• What should software tools do to help us to explore and analyze data effectively?
• How should software be designed and function to support us in these useful ways?
• What are the most promising trends that will take visual analytics to the next level of productivity?

6. If someone doesn’t use Microsoft Excel, should they pass on this book?

Assuming that you have some software tool available to you that can be used to view, explore, and analyze data visually that is as good as Excel or better, this book was written for you. If you don’t currently have such a tool, this book will reveal what you need in a tool, so you can get a good one and get started.

7. The title of the book, “Now You See It: Simple Visualization Techniques for Quantitative Analysis” suggests that this book may be an introduction to data visualization.  If someone is at a more advanced level, will they benefit from this book?

People who are already experts in visual analytics will already know much of what I teach in this book. No one who exclusively uses Excel for data analysis is already an expert in visual analytics, however, because Excel doesn’t support many of the most useful and powerful visualizations and functions of visual analysis. Even most people who are well trained in statistics will benefit from this book, because most courses in statistics don’t teach many of the analysis techniques that are necessary when you need to explore and examine trends, patterns, and exceptions with your eyes, not just as a number produced by a statistical formula.

8. Who designed the cover, it’s really exquisite?

I’m glad you like the cover. As you can imagine, as someone who’s involved in visual design, I want book covers that emulate the design principles I teach but are also compelling in a way that says “Pick me up and take a look” when they catch people’s eyes. I think Keith Stevenson accomplished this beautifully with his cover design. Keith and I became acquainted originally as we exchanged opinions about wine in a local wine bar. He works as a graphic designer at the University of California, Berkeley.

9. What’s one thing that the average person doesn’t know about Stephen Few that you can share?

Perhaps the fact that people are most surprised to learn about me is that I started my professional life as a minister. My personal and professional journey since then has taken a long and winding road. What remains of those days, I suppose, is the fiery passion that I bring to my work.

10. Is there going to be a book signing tour?

I’m still waiting for my invitation from Oprah. Until she puts me on her book list, I’ll continue to assume that a live tour would mostly give me a chance to catch up on my reading. Come to think of it, that’s not such a bad idea.

Related Books:

1. Show Me The Numbers: Designing Tables and Graphs to Enlighten, Stephen Few
2. Information Dashboard Design: The Effective Visual Communication of Data, Stephen Few

There are referral links within this post to Amazon. However, there is no endorsement from Stephen Few for this post or any reference to his books.

• 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?

Line Graph: "display trend over time…. useful when there are many data points and the order is important"

Area Chart: "display the trend of values over time"

Below I have taken two separate data visualizations showing the same type of data.  The first is an area chart with the second being a line graph.  The data sets are different, but the use is nearly the same – showing a trend of a percent over months or years.  There is the Tufte concept of data/ink to consider if you really want to get technical.  Me, I just stick to a simple well designed line graph.

[source]

[source]

Simply put, I think you can’t go wrong with either of the two above if your intention is to visualize the trend of data over time.  Even though I rarely use area charts, I would much rather have someone use one of these over the misuse of trying to fit a column chart in its place.  One use of a variation of the area chart that I typically see is to show the range of average temperatures throughout the year, where the area represents the average high and average low temperature.  Conversely, I’ve also seen the temperature data represented with two line graphs for the average high/low (www.weather.com uses this method and can be seen here).  You can also see an example of an area combination chart on Jon Peltier’s site found here.

Which chart do you prefer when showing data over time, the line graph, area chart or other?  Does one sector tend to use area charts over line graphs?  My experience in the business world leads me to think lines are preferred over area charts.

[source]

Below, I added up the six slices of the pie and only came to 99 or 99%.  Am I the only one who does this for fun?  For whatever reason, I can’t pass up on doing these checks and math when seeing a chart like the one above.  Now, I’m sure the reason for the missing 1% is rounding.  Instead of 41%, I’m sure the actual number is 41.4% and so on.  Whenever making data visualizations, be sure to check the details and do the simple math to ensure everything adds up correctly.  Something as simple as the pie chart slices not adding up to 100% could lead to questioning the competency of the creator.

Is this being too critical or do you think the minor details matter?

[source]

Update: Here is the story that accompanied this data visualization in BusinessWeek.

If you click on the source or image above you will get a larger view that is a bit easier to read.  Let’s start with the map on the left, which shows a flaw in the design of these maps in terms of accuracy.  Specifically, lets focus on the orange bubble in the middle of Florida.  Does this large bubble represent the state or the actual region (city) within Florida that has a large number of Circuit City closings?  Honestly, I don’t know.  By looking at all of the states, I guess I can logically deduce that the bubbles appear in the middle of each state, meaning the value is for the entire state. 

Now, look at the value for Texas and tell me with corresponding value in the legend it matches up with.  It looks like it’s about the same size as the 56+ bubble, but what would California’s value be?  It’s clearly the largest bubble on the map and must represent the largest value in the legend, right?  Something isn’t right.  Also, the smaller bubbles are nearly impossible to quickly tell apart on the map.

Another good example of a flaw is the map on the right that shows the number of KB Toys store closings.  Check out the Northeast region and tell me which value (bubble size) represents Massachusetts? It’s pretty hard to tell, huh.  The map in the middle is actually the easiest to read because there aren’t any states with a large number of closing leading to a large bubble. 

Now let’s put this data visualization in context.  These three maps appeared in a section of BusinessWeek a few weeks back.  What I do like about these maps is the ability for me to quickly visualize regions or states with large values, to some extent (Northeast).  For KB Toys, I can only really tell that the Northeast has a lot of stores.  I don’t think there’s much in depth analysis that can be done with these static bubble maps. 

Here’s where I contradict myself.

Benefit – Quickly see regions and states with large values making them the outliers.

Drawback – Can’t quickly differentiate the small bubbles from state to state and in reference to the legend.

Now a question – do you think these bubble maps are more valuable than the more traditional color shading (heat) maps? 

Most Poplar Posts YTD

1. Presenting Data on a Map
2. New Stephen Few Book along with New Book Update
3. Advanced Placement Exam Chart
4. Dress Up Your Data
5. U.S. Infrastructure Dashboard 
6. Strategic Use of Colors in Charts

Thank you for your support and I value your visits, comments and help spreading the word!

Like what you see?  Subscribe to Support Analytics (DSA Insights) to stay updated on what’s new.

Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few