Posts tagged as:

human factors

Chart-Making: Cures for Loss Aversion

by Jorge

Loss aversion – wrong chart example

Monney Income - wrong chart

JunkCharts writes an interesting post on how loss aversion can happen in chart-making. The general concept of loss aversion tells us that “people strongly prefer avoiding losses than acquiring gains”. Translated to chart-making, it means that there is a “tendency to avoid losing data at any cost”.

“To clarify, add detail” says Tufte. Corollary: you should make data-dense charts and maximize the data-ink ratio. Problem is, this fits too well into the loss aversion tendency. Take the above chart, for instance: does it make any sense to add those nine series to a single chart? What insight do you get from it? Only one: the designer don’t know how to handle a larger number of data series.

Remove irrelevant data series and you risk a mutiny on the Bounty, even if relevant trends are easier to detect. It is absurd, but very human.

So, how can you give the users all the data they expect while keeping the chart clean and readable? Well, to clarify, add detail to existing patterns (that’s what I just did to Tufte’s sentence…).

Tufte talks about “data layers”; Ben Schneiderman’s Visual Information-Seeking Mantra (“overview first, zoom and filter, then details-on-demand”); the focus+context technique. All they convey a simple idea: prioritize your data. Know what is relevant and what is nice to have. Don’t give the user a final product. Make an interactive chart and let her discover what’s inside.

I see this loss aversion tendency at work every day at the office. Do you too? How do you handle it?

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Fibonacci, working memory and information overload

by Jorge

More data = better decisions, right? Not always. When you are getting more information than you can process within a specific time period information overload starts creeping. Confusion, stress, anxiety and low motivation usually follow. Can we prevent that?

information-overloadIn general, the more information you have, the more accurate your decisions will be. But at some point, the trend reverses, and the more information you have, the less accurate your decisions are (recommended reading: this paper for causes and consequences of information overload and the – poor – Wikipedia article).

Too often you can root causes of information overload to poor information and report design and poor information management skills. Let me exemplify. Can you memorize this sequence?

1123581321345589

It is not easy. Let’s try again:

1123-581-321-345-589

Better, but not good enough. Let’s try this one:

1+1=2+3=5+8=13+21=34+55=89

You’ll probably recognize these as the Fibonacci numbers, a sequence of numbers where each is the sum of the two preceding numbers.

So, you’ve tried to memorize a string of 16 digits. Then five strings of three or four digits. Then a word, “Fibonacci”. Which was the easiest?

Small scale information overload: working memory management

Let’s assume for the sake of discussion that information overload takes place when the information you try to manage exceeds the capacity of your working memory (it goes much beyond that, of course). Let’s also assume that there are five slots of working memory that you can use to store chunks of data.

As you can see, there is no room in working memory for the first sequence, the second barely fits and the third uses only one slot, for exactly the same data.

While you can’t do much to add more slots to your working memory, you can have an active role at the design of those chunks and by that greatly improve the way you handle data and reduce the danger of information overload.

There’s a thread in Edward Tufte’s forums where he discusses Miller’s classic paper, “The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information“. Tufte rightly argues that:

… the deep point of Miller’s paper is to suggest strategies, such as placing information within a context, that extend the reach of memory beyond tiny clumps of data.

By providing context or some sort of linking between “tiny clumps of data” you can create a single chunk of data. This is a basic strategy for information management and visualization.

Just be aware of the limits of our working memory and understand what you can do to maximize its capacity. This is a great starting point to design better charts.

Simple tip: avoid a back-and-forth movement

Minimizing the need for a back-and-forth movement is a practical application of the these principles.

We decode a chart with multiple series by reading the legend and storing the meaning in our working memory. If there are more series than the available memory, a pendular eye movement between the legend and the plot area occurs. Try to prevent that by directly labeling the series (specially in line and pie charts) or make sure that you really need all those series. If you do, a panel could be a better option.

When you have two related charts in two different slides in a presentation your audience will probably want to compare them, and a back-and-forth movement between slides happens again. Try to change the presentation design so that both charts are placed in a single slide, making comparisons easier.

(Why the sunflower?)

Photo credit: catd mitchell

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Sparklines, Excel, Crystal Xcelsius and stealth-mode charts

by Jorge

You struggle every single day to design the best, the most eye-catching chart for the next presentation. If your goal is to impress your boss, you can stop reading, I have nothing new to offer, I can’t even impress mine. But if you really want to understand your data, there is some (hopefully) interesting stuff below.

Charts 101: the visual metaphor

Charts use visual metaphors to convey abstract data. Those visual metaphors can themselves be of a more concrete or more abstract nature:

Visual metaphores

Concrete metaphors (pies, skylines) are very powerful, because users can relate to their daily life objects and experiences, so usage and meaning are easily inferred. By contrary, abstract metaphors must be learned to be decoded. You can’t find a daily life equivalent of a scatter plot.

There is a consensus that more than 5-6 data points make a pie chart unreadable, but you can plot hundreds or even thousands points in a XY chart. A sparse – dense continuum seems to follow the same direction of the concrete – abstract continuum.

From a marketing point of view, using concrete metaphors makes a lot of sense if you are selling to an undifferentiated target. This is what people know (and love?), and if the charts look familiar, pleasant and easy to understand they will want the software.

The logical next step is to be even more concrete (= more users, more sales). Enter the dashboard metaphor. Everyone knows gauges and traffic lights. The metaphor is simple (driving your company is like driving your car) yet appealing (you are in control), so let’s add the look and feel of real gauges and traffic lights. Even pie charts are no longer something that vaguely reassembles a pie anymore. They become a “real” and very polished 3D object that reflects light, like in the physical world, exactly like the ones you find in the magazines. You are entering the realm of professional design…

The role of texture

Texture is what gives these objects their real-worldness quality. It is one of the six “visual variables” (position, size, shape, value, color, orientation and texture) listed by Jacques Bertin and it has a fundamental role in scientific visualization. But scientific visualization deals with “objects” (the DNA helix, for example). If an engineer wants to know what happens when a force is applied to an object she needs texture to see how the object is changing.

Information visualization deals with abstract concepts like “market share”. You can’t point at an object and say “this is a market share”.

The problem with texture is that it requires a lot of space. Take a look at these two scatter plots from Crystal XCelsius. See that gorgeously(!) textured data points in the first one? And do you see the texture in the second chart? Look closely, I just changed the symbol size, it is still there…

If you have a complex series, with more data points, you can’t use the default symbol size. It must be much smaller. But then you are hiding the texture and the chart becomes very similar to an Excel chart. So what’s the point of using Xcelsius? Why don’t you just do it in Excel?

Eye-catching charts vs. stealth-mode charts

In a finite space (the chart area), a data value coded with a point (no dimensions) take less space than another coded with a line (one dimension) and much less than another coded with a polygon (two dimensions). Obviously.

Eye-catching charts, like the ones you get from Crystal Xcelsius and similar applications, are textured, polygon-based charts. By using a polygon to encode a data value you are cutting down the amount of data you can display in a single screen, like in a dashboard. In a data-rich environment this can be a problem and an opportunity. It can be a problem if you can’t fit the data into the available space. But it can be an opportunity for you to exercise your judgment when selecting the relevant data.

I would say that eye-catching charts are also eye-caught charts. They stay longer in the retina and they have a hard time trying to deliver their simple messages to the brain, and sometimes they fail miserably…

Stealth-mode charts, on the other hand, avoid to catch or be caught by the eye. They are smaller, use less dimensions, muted gridlines and soft colors, allowing them to pack more data. In a split second they are preattentively processed and the data is delivered to the brain even before the eye notices them (I am taking too much liberties here, preattentive processing will be more formally discussed in a forthcoming post).

Please bear in mind that showing data to your boss using stealth-mode charts is a mistake. He’ll be vaguely aware of a deeper knowledge but he will not link it to your oh-so-simple-charts, so don’t expect a promotion. Also, they make you lazy, since you (may think that you) don’t have to actively select the relevant data, and that the screen real estate is more than enough.

You can design your stealth-mode charts in Excel or in any other application that don’t use texture-based charts. Sparklines are great for stealth-mode charts but there are some issues that must be addressed (to be discussed in a forthcoming post also).

I coined the expression “stealth-mode charts” as an umbrella for a set of principles in information visualization that emphasizes the need for an alternative to the “eye-catching charts” that emphasizes insights, knowledge and higher return on the investment made in the data.

What do you think? Is there a place for stealth-mode charts in the corporate sector or are we forever stuck in all these boring eye-catching charts? Please share your thoughts in the comments.

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