Thanks for taking the time to read my thoughts about Visual Business Intelligence. This blog provides me (and others on occasion) with a venue for ideas and opinions that are either too urgent to wait for a full-blown article or too limited in length, scope, or development to require the larger venue. For a selection of articles, white papers, and books, please visit my library.

 

Oracle/PeopleSoft Enterprise Service Dashboard — A disservice to people who desperately need better

July 30th, 2007

Thanks to a press release from Oracle last week, I found the two dashboard screen shots that appear below in a Product Sheet that describes the features of Oracle’s new PeopleSoft Enterprise Service Dashboard software. These examples feature a dashboard that is designed for monitoring call center activity—or at least this is their intent. I’m frustrated that software vendors continue to produce such anemic and poorly designed dashboard examples. Whether the software is capable of doing better, I don’t know, but these screen shots demonstrate no understanding whatsoever of effective dashboard design. Oracle should be embarrassed.

Oracle/PeopleSoft Enterprise Service Dashboard (Small)
(Click the dashboard images to enlarge.)

Due to a busy schedule, I asked Bryan Pierce, who works with me, to critique these examples. Until a few months ago when he began to work at Perceptual Edge, Bryan had no experience with graphical communication and had never seen a business intelligence dashboard. Today, he spends his days managing the day-to-day operations of Perceptual Edge (website, bookkeeping, etc.), but has been picking up data visualization skills on the side, mostly by reading my books and articles. I’m pointing this out because it’s worthwhile to note that it doesn’t take years of experience to develop the skills of graphical communication. Bryan’s critique begins below my signature, which I’m confident that you’ll find useful. Oracle could learn a thing or two from Bryan.

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This review is based on the two images that were included in Oracle’s product data sheet. Unfortunately, due of their low-resolution, some of the text was illegible, so I wasn’t able to conduct as thorough of a review as I would have liked. Below is a list of the problems that I found.

Overall:

  • All of the graphs use 3D to encode 2-D data. The third dimension adds no value but does succeed in making the values harder to decode.
  • Neither display is sufficiently data-rich. In creating a dashboard, it’s important to put all of information that people might want to compare together on a single screen. Even when people probably won’t compare values, it’s easier and more efficient for them to work with a well-designed single page than it is to work with multiple data-sparse screens. The size of the graphs alone—giant pies and single bars that take up a quarter of the screen—indicates that much more information could have be included on the display. It’s likely that, given a proper layout, multiple screens would not even have been necessary.
  • The borders between graphs are unnecessarily salient, which makes it more difficult to track between them to make comparisons.
  • All of the tick marks used are redundant; they are not necessary when gridlines are used.

Main Screen (first image):

  • Very little of the information is encoded visually, undermining much of the strength of a dashboard. The only information that is encoded visually is the information contained in the pie chart and the bar & line graph. The multiple tables require us to read the information (slow serial processing) instead of allowing us to see the information like graphs do (fast parallel processing).
  • The bar and line graph appears to display call volume (bars) and call capacity (line) as it changes through the day (although, due to the low resolution, I can’t be sure of this). By using bars and a line together, it creates the potential for confusion when they cross. For instance, look at the 5th bar from the left. If its magnitude was slightly higher, the top-left side of the bar would be above the line, but the top-right side would not. It could difficult to quickly determine whether the bar or the line represented a greater value, in some cases. The 3-D effect only adds to the problem. If only bars or only lines were used, this wouldn’t be a problem. If bars and lines are used, this problem could be alleviated by adding small data points to the line to assist in comparisons.
  • The pie chart appears to display a breakdown of calls by the originating region (US – Midwest, United Kingdom, etc.). Pie charts don’t work well because people have a difficult time accurately comparing area. When a third dimension of depth is added, this becomes even more difficult because the slices at the top of the pie are shrunken slightly while the bottom slices are slightly enlarged to provide the 3-D perspective. 2-D bar charts work much better than pie charts because, instead of relying on our poor ability to compare areas, they are based upon our ability to compare lengths, which we do quite well.
  • The gradients used in the background of the graphs are more visually salient than a solid light color would be and in some cases, they can misleadingly alter our perception of the data.
  • The background images are distracting and make it more difficult to read the data.

Drill-Down Screen (second image):

  • Notice the legend below the bottom-left bar graph. On many dashboards the colors green, red, and yellow are commonly used for encoding data. Red is usually used to represent “bad,” yellow “borderline” or “satisfactory,” and green “good.” The problem with this color-coding is that the 10% of men and 1% of women who are color-blind might not be able to tell the good from the bad. In this case, it appears that Oracle might have tried to avoid this by using red, blue, and light green (which could potentially be differentiated by the color-blind because the light green has a lower intensity than the red). Unfortunately, look at the labels on the legend. The red box is labeled “Green,” the blue box is labeled “Red,” and the light green box is labeled “Yellow.” This creates a big problem for the display. Studies have found that when the word for a color is different from the color of the word (for instance “green” is written in red text) it is significantly slower and more difficult to read. How many times would you need to go back to the legend before you would easily remember that the blue bar means “red?” This cross-coding is a significant problem for everyone, whether color-blind or not.
  • The red and blue colors used to encode the bars mean different things in different graphs. When the same color is used in multiple graphs, people naturally tend to assume that there is meaning behind this; in this case, they would attempt to find meaning where there is none. When multiple colors are used, care should be taken to prevent people from looking for connections that don’t exist. There is an exception to this rule: If everything is the same color. All of the bars could be the same color without any confusion. When everything is encoded with the same color, instead of just certain things, people aren’t inclined to see false relationships.
  • There is little point in encoding a single value as a bar. It takes at least as much time to decode as it would to just read the number (because you must refer to the scale). Once multiple bars are added, bar charts begin to shine because the approximate value of every bar can be understood by one or two quick glances at the scale and you can quickly make some comparisons between the bars without referring to the scale at all.
  • The bars on this screen all use transparency, which is another gratuitous effect that serves no positive purpose.
  • The gridlines are probably unnecessary in all of these graphs. If they are required, they should be lightened so that they are just light enough to be visible and no more.

Below, I have included an image of a call center dashboard that Steve created for his book Information Dashboard Design. Notice how much more information is provided on the screen: It’s not cluttered, but it is much more data-dense than the previous examples. Notice how much cleaner it looks; color is not being used gratuitously and there are no special effects to distract from the data.

Sample Telesales Dashboard by Stephen Few (small)
(Click the image to enlarge.)

Enhanced Gantt Charts with Excel

July 27th, 2007

This morning a business intelligence consultant from Finland, Janne Pyykkö, posted an example of a Gantt Chart that he created with Excel that uses a heatmap approach to display quantitative values along the timelines, such as the number of hours worked. This brings the Gantt Chart to life with additional information, which allows you to see patterns that often remained buried in the data. Janne’s example points out one of the useful visualization features that was added to the latest version of Excel: the ability to encode quantitative values as color in spreadsheet cells. You can see a full explanation of Janne’s example at his Blog and read the discussion surrounding it in my Discussion Forum.

Here’s a picture to whet your appetite:

Heatmap Matrix Gantt Chart.png

In this example, varying intensities of blue represent the hours worked per week for individual projects. The row at the bottom uses varying intensities of red to summarize the hours worked for all the projects. As you can see, this makes it easy to see the varying use of human resources on projects through time. The point isn’t to precisely compare the hours worked from one week to the next, but to view the pattern of change through time and to spot exceptions (for example, extreme values on either end of the scale).

Take care,

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Examples of poor graph design, along with redesigns that work

July 24th, 2007

Those of you who are familiar with this website already know that it includes an Examples section, which shows several ineffectively designed graphical displays, along with critiques to explain why they don’t work, and proposed redesigns to show how they could be improved. Many people find these examples helpful, both for raising awareness about the mistakes that people often make when designing graphs, and for instruction in how to effectively communicate graphically.

I have just added two new examples, which you might find worthwhile, which are the first two that appear at the top of the Examples section of this site.

Take care,

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Microsoft vs. Oracle business intelligence – Does Dundas make a difference?

July 10th, 2007

Today I ran across a story published in Australian IT, a web-based news site, entitled “SQL Service put Oracle on Notice,” by Barbara Gengler (July 10, 2007). In it, Gengler pitted the growing business intelligence (BI) capabilities of Microsoft against Oracle, citing the acquisition of Dundas’ data visualization product for SQL Server Reporting Services as a significant boon for Microsoft. I’m not prepared to compare these two behemoth’s BI capabilities (both lack much of what I consider vital), but I can’t resist stating that the acquisition of Dundas’ so-called data visualization capabilities doesn’t count for much. In fact, in my opinion, the inclusion of Dundas in SQL Server Reporting Services is in many respects a setback for Microsoft.

Rather than demonstrating even the slightest understanding of data visualization, the folks who made the decision to acquire Dundas’ software have reinforced my opinion that they still haven’t got a clue. Typical of most producers of visual display widgets, Dundas offers a vast library of charts that look like the work of engineers who sit around saying “Hey, look at what I can make a chart do…isn’t this cool?!” They forgot to include designers who understand that the goal of charts is to communicate data clearly, efficiently, and accurately, not to scream, “Forget the data, look at how cute I am.”

Here are two examples:

Dundas Chart
Dundas Dashboard

I want more from a software company with the resources of Microsoft. I would love to see Microsoft advance BI to a new level by introducing thoughtful and innovative data visualization capabilities. There are some very talented people at Microsoft Research who have the ability to do this, but I have yet to see any evidence of their influence in Microsoft’s products. Rather than banking on its ubiquitous presence and influence throughout the world as an assured fast track to BI dominance, how about demonstrating some of the innovation and thoughtful work? If Microsoft understood data visualization and took pride in its work, the addition of Dundas’ charts to SQL Server Reporting Services would be seen as a source of embarrassment rather than featured news in its BI marketing campaign.

Take care,

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Visual Statistics — A worthwhile new book, but one that is definitely for statisticians

July 2nd, 2007

I returned late last week from nearly three weeks of work in Europe, which ended with a two-day workshop that I taught for the Swiss Statistical Society. Nestled in a majestic valley in the Swiss Alps, we spent our days talking about how these talented statistical analysts could enhance their work by learning to communicate their findings more clearly and by using their eyes to supplement abstract statistical techniques. Later this year at their annual conference, they will hear a keynote presentation from Michael Friendly, Ph.D., who is a professor in the Department of Psychology at York University in Toronto, Canada. Among his many talents, Friendly is a trained statistician and an aficionado in the use of visual techniques for statistical analysis. Along with two other authors, Forrest W. Young, Ph.D., of the University of North Carolina (recently deceased), and Pedro M. Valero-Mora, Ph.D, of the University of Valencia in Spain, Friendly has written a new book on the topic entitled Visual Statistics: Seeing Data with Dynamic Interactive Graphics (John Wiley & Sons, Inc., 2006). Always eager to find new sources of insight into data visualization, especially as it applies to analysis, I read the book during my recent stay in Europe.

I don’t intend to review the book comprehensively in this brief blog post, but I would like to comment on its potential usefulness for my primary audience, which consists largely of business people who work with data, but lack advanced statistical training. I was encouraged when I began to read the introduction that this might be a book I could recommend to this audience. The authors’ message rang true to my experience and seemed to share my goals:

Statistical data analysis provides the most powerful tools for understanding data, but the systems currently available for statistical analysis are based on a 40-year-old computing model, and have become much too complex. What we need is a simpler way of using these powerful analysis tools.

Visual statistics is a simpler way. Its dynamic interactive graphics are in fact an interface to these time-proven statistical analysis tools, an interface that presents the results of the hidden tools in a way that helps ensure that out intuitive visual understanding is commensurate with the mathematical statistics under the surface. Thus, visual statistics eases and strengthens the way we understand data and, therefore, eases and strengthens our scientific understanding of the world around us.

It is our aim to communicate the intrigue of statistical detective work and the satisfaction and excitement of statistical discovery, by emphasizing visual intuition without resorting to mathematical callesthenics [sic]…Seldom is there mention of populations, samples, hypothesis tests, and probability levels…This book is written for readers without strong mathematical or statistical background, those who are afraid of mathematics or who judge their mathematical skills to be inadequate; those who have had negative experiences with statistics or mathematics, and those who have not recently exercised their match or stats skills. Parts I, II, and III are for you.

The book only seems to consist only of Parts I, II, and III, so I interpret the final statement to mean that non-statisticians should find the book non-intimidating and accessible. What I discovered in reading the book, however, is that, despite how useful it might be as a primer in visual analysis for statisticians, it is steeped in the concepts and language of statistics, and lacks the explanations that would be needed by non-statisticians to make use of the material. I have no doubt that the authors attempted to reach out to non-statisticians. I suspect, however, that they are too immersed in an academic statistical mindset to recognize when they are using terms and discussing concepts that are unfamiliar to the uninitiated. Terms such as Box-Cox transformation, Euclidean space, kernel density curve, p-value, and Pearson’s chi square are par for the course. Early in chapter 2, which provides some actual data sets and analytical challenges that are used throughout the book, the reader is already faced with material like the following:

The spreadplot (a kind of multiplot visualization that is introduced in chapter 4) for the initial model, (GPE)(M) is shown in Figure 2.9 (on the following two pages). This model fits very poorly, of course (G2 = 107, df = 7, p < 0.001). The G2 measure is a badness-of-fit measure. Low values are good, high values are bad. The empty model, reported here, has a very large value of G2, meaning the fit is very poor, which, of course, it must be, since it has no terms. The hypothesis test, when rejected, as is the case here, indicates that the model does not fit the data. 

At this point, as someone whose statistical knowledge can fit comfortably in a thimble, my eyes began to glaze over. Please don’t misunderstand me. I am not saying that this is not a good book. I suspect that this is a very important book for statisticians, because it introduces them to the power of visual analysis, which most statisticians under-appreciate. This just isn’t a book for non-statisticians.

One more observation that I want to make about this book is one that applies to many books on data visualization: the value of books on this topic is dramatically undermined when they are not printed in color. I felt badly for the authors when they bemoaned this unfortunate decision by the publisher to save costs by printing the book in black-on-white:

Unfortunately, mosaic displays are best viewed in color, and we are forced to use black and white. (We do the best we can, but to be honest, the black-and-white versions…do not do justice to the mosaic displays. If you can view this online, please do; it will help).

It wasn’t only the mosaic displays that would have benefited from color. Perhaps the authors already had their contract in place with John Wiley & Sons, Inc., before they realized that color was not an option, and then found that they had no power to change this. If you ever plan to write a book about data visualization, get an up-front guarantee from the publisher that the book will be printed in color, or you’ll end up having to make sad disclaimers to your readers like the one above.

Take care,

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