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© 2009 Stephen Few, Perceptual Edge 1

It is not enough to mine the meanings that exist in your data. The value of

information is not realized until\ you use it to do something. Used properly, it can

make the world a better place.


This poem by Edna St. Vincent Millay eloquently and poignantly describes our

situation today. Our problem is not a lack of data, but rather our inability to make

sense and use of what we have.


The amount of information that is available to us has grown much faster than

our ability to make use of it. We lack both skills in data analysis and tools that

can be used to productively support the process.


According to Richards J. Heuer, Jr.:

Once an experienced analyst has the minimum information necessary to

make an informed judgment, obtaining additional information generally

does not improve the accuracy of his or her estimates.

(Psychology of Intelligence Analysis, 1999)


Most of the data analysis that is needed in the normal course of business requires

relatively simple data visualization techniques, leaving little that requires the

sophisticated techniques of statistical and financial analysis.

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If you search for resources that teach data analysis skills, you’ll find many books

and courses that present the sophisticated techniques needed by the few, but few

resources if any that teach the simple techniques that most of us need to make

sense of business data. The skills that most of us need to infuse our businesses

with needed insights can be learned without a background in statistics, but these

skills don’t come naturally – they must be learned. You must develop expertise, but

it is expertise that can be easily learned with the proper direction and practice. You

must learn to see particular patterns in data that are meaningful.

People can learn pattern-detection skills, although the ease of gaining these

skills will depend on the specific nature of the patterns involved. Experts do

indeed have special expertise. The radiologist interpreting an X-ray, the

meteorologist interpreting radar, and the statistician interpreting a scatter plot will

each bring a differently tuned visual system to bear on his or her particular

problem. People who work with visualizations must learn the skill of seeing

patterns in data.

(Information Visualization: Perception for Design, Second Edition, Colin Ware,

Morgan Kaufmann Publishers, 2004, page 209)

© 2009 Stephen Few, Perceptual Edge

7© 2009 Stephen Few, Perceptual Edge

Good tools, from stones for crushing or cutting to computers for augmenting

cognition, can set us free and make the world a better place, but only when used

properly. When misused, they make us lazy, dumb, slaves. The choice is ours.


To date, business intelligence has mostly focused on technology and project methodology, resulting in great advances. As a result, we have huge and fast warehouses of information. It is now time to focus on the true essence of business intelligence—important, meaningful, and actionable information—and the most powerful resources for tapping into its value are those that engage the tremendous capacities of human perception and intelligence to make sense of and communicate information.

Many organizations aren't effectively analyzing the data they do have to improve their business.

What's more troubling, perhaps, is many companies that purchase powerful analysis and business-intelligence tools don't use them effectively. Users of these products often generate the most basic and obvious reports and never get their hands dirty with the deep-analysis tools.

By ignoring these products' deep-analysis capabilities, organizations could be missing important trends—information that might show, for example, where a company is losing business.

Companies might also see that business decisions that were made based on basic information were wrong and ended up costing money in the long run.

(eWeek, ―With Data Analysis, Less Isn’t More, Jim Rapoza, Ziff Davis Media Incorporated, June 7, 2004)

Today much of science and engineering takes a machine-centered view of the design of machines and, for that matter, the understanding of people. As a result, the technology that is intended to aid human cognition and enjoyment more often interferes and confuses than aids and clarifies.

It will take extra effort do design systems that complement human processing needs. It will not always be easy, but it can be done. If people insisted, it would be done. But people don’t insist: Somehow, we have learned to accept the machine-dominated world. If a system is to accommodate human needs, it has to be designed by people who are sensitive to and understand human needs. I would have hoped such a statement was an unnecessary truism. Alas, it is not.

(Things That Make Us Smart, Donald A. Norman, Basic Books, New York, 1993, page s 9 and 227)


Data visualization has the potential to help business intelligence fulfill its promise of

helping organizations function intelligently.


Data visualization is the loom that will weave the data that we collect into the fabric

of understanding. Pictures of data can make visible the meanings that might forever

otherwise remain hidden.


Though data visualization has become a popular tool of business intelligence

only recently, people have been using graphs to display data visually for a long

time. In 1786, a roguish Scot—William Playfair—published a small atlas that

introduced or greatly improved most of the quantitative graphs that we use

today. Prior to this, graphs of quantitative data were little known.


Today, 220 years later, graphs are commonplace, fully integrated into the fabric

of modern communication. Surprisingly, however, Playfair’s innovative efforts—

spring from meager precedent—are superior to most of the graphs produced

today.


Hans Rosling of Gapminder.org has become one of the real stars of information

visualization in the last couple of years. When Rosling took the stage at the TED

conference for the first time in 2006, he managed to get people up on the edges of

their seats to watch—believe it or not—a bubble plot that used animation (motion) to

show change through time. When he finished, the crowd rose to their feet to give

Rosling a standing ovation. For most of the people there, data presentation had

never been so compelling.

Rosling has used relatively simple visualization techniques, featuring animated

plots, to tell statistical stories that are compelling, not only because they are told

with great charisma, but because they reveal important truths about the world, such

as the changing relationship between wealth and child mortality. I applaud his

success, in part because it is success that we can share in, for it illustrates to the

world at large what infovis can do when it is done well and it is used for worthwhile

purposes.


When Al Gore rode a scissor crane up to the top of the CO2 emissions graph in the

film An Inconvenient Truth, he became a superstar of visual communications. He

compellingly used graphs to tell the story of global warming, which helped public

opinion in America to finally reach the tipping point.

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Data visualization begins with (1) searching through the data to discover potentially

meaningful facts, then involves (2) examining that data more closely to understand

it, including what caused it to occur, so that you can then (3) explain what you’ve

learned to those who can use that knowledge to make good decisions. Most of what

we need to recognize and understand in our business data is not all that

complicated.


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Human perception is amazing. I cherish all five of the senses that connect us to the world, that allow us to experience beauty and an inexhaustible and diverse wealth of sensation. But of all the senses, one stands out dramatically as our primary and most powerful channel of input from the world around us, and that is vision. Approximately 70% of the body’s sense receptors reside in the eye.

Perhaps the world’s top expert in visual perception and how its power can be harnessed for the effective display of information is Colin Ware, who has convincingly described the importance of data visualization. He asks:

Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated, which is the reason why the words ‘understanding’ and ‘seeing’ are synonymous. However, the visual system has its own rules. We can easily see patterns presented in certain ways, but if they are presented in other ways, they become invisible…The more general point is that when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading.

(Information Visualization: Perception for Design, Second Edition, Colin Ware, Morgan Kaufmann Publishers, 2004, page xxi)

Perhaps the best known expert in data visualization, Edward Tufte, says: ―Clear and precise seeing becomes as one with clear and precise thinking.‖ (Visual Explanations, Edward R. Tufte, Graphics Press: Cheshire, CT.1997 page 53)


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The presentation of data as text, such as you see in this table, is perfect when you

need precise values or when the purpose is to look up or compare individual values,

but not when you wish to see patterns, trends, and exceptions, or to make

comparisons. When this is your goal, visualizations work best.

When data is presented visually, it is given visible form, and from this we can easily

glean insights that would take a long time to piece together from the same data

presented textually, if ever. This graph of the same data that appears in the table

makes brings to light several of the stories contained in the data that weren’t

obvious before, and it did so instantly.

When] we visualize the data effectively and suddenly, there is what Joseph

Berkson called ‘interocular traumatic impact’: a conclusion that hits us between

the eyes.

(Visualizing Data, William S. Cleveland, Hobart Press, 1993, page 12)

Modern data graphics can do much more than simply substitute for small

statistical tables. At their best, graphics are instruments for reasoning about

quantitative information. Often the most effective way to describe, explore, and

summarize a set of numbers – even a very large set – is to look at pictures of

those numbers. Furthermore, of all methods for analyzing and communicating

statistical information, well-designed data graphics are usually the simplest and

at the same time the most powerful.

(The Visual Display of Quantitative Information, Edward R. Tufte, Graphics

Press: Cheshire, CT 1983, Introduction)



So much of what is called ―data visualization‖ gives it a bad name and causes

confusion about what it is, how it works, and what can be accomplished when it is

properly done.


Software vendors are competing to out dazzle one another with silly visual effects

that treat data visualization like it’s a video game.


This notion of data visualization is not about understanding and communication, it’s

about bling.


Dashboards are notorious for featuring graphical glitz over substance. Too many

dashboard vendors and designers have lost sight of the bottom line: communication.

They emphasize graphical glitz over clear and meaningful content. For every item of

information on the screen the designer should ask the question: ―How can I display

this information in the most meaningful, clear, and efficient way possible?‖

The graphics in this dashboard from Business Objects, created with Xcelsius, are

beautifully rendered, but is the information effectively displayed? The Xcelsius team

clearly possesses exceptionable graphical skill. This isn’t surprising, given the fact

that most of the original team of developers formerly developed video games.

Unfortunately, they failed to make the transition from video games to data

visualization.

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Data visualization essentially helps us to do two things: (1) think about information

more effectively so we can understand it means, and then (2) tell its story clearly

and accurately to others.



My first two books addressed the communication aspects of data visualization.


Data visualization is much more than just graphical reporting, more than

dashboards. Beyond its use for communicating information that cannot be

communicated with tabular data, its greatest potential is exhibited in its use for

analysis. The best techniques for making sense of business data are visual

techniques, which extend our ability to find and understand meaningful patterns in

data by offloading much of the work traditionally performed by the conscious mind to

preconscious and parallel processors in the brain’s visual cortex. Most BI vendors

provide some graphical functionality in their software, but few actually support visual

analysis in more than rudimentary ways.


During the last year I wrote a new book, Now You See It, to help people develop the

fundamental skills that are needed to make sense of quantitative information.

27

Visual analysis involves comparing the magnitudes of values, but not just one to

another. We must compare many values. To do so, we must see how they relate to

one another to form patterns. We not only compare the magnitudes of values; we

also compare patterns formed by sets of values. We look for how they are similar

and we look for how they are different, especially differences that appear to be

dramatic departures from the norm. When we spot these visual characteristics in the

data, we then interact with the data to find out why these things have happened.



Many of the ways that visual perception work are not intuitive.

Looking at these two sets of objects, we naturally see those on the left as convex

and on those the right as concave.


The effect has now been reversed: we see the objects on the left as concave and

those on the right as convex. All I did, however, was turn each sets of objects

upside down—I didn’t switch them. The reason that we now see those on the left

as concave is because, through eons of evolution, visual perception learned to

assume that light was shining from above, which causes us to see the objects on

the left as concave, because the shadows are on the top, and those on the right

as convex, because the shadows are on the bottom.

32

Despite how differently they look in the original image, squares A and B are

exactly the same color. What we see is not a simple recording of what is actually

out there. Seeing is an active process that involves interpretations by our brains

of data that is sensed by our eyes in an effort to make sense of it in context. The

presence of the cylinder and its shadow in the image of the checkerboard triggers

an adjustment in our minds to perceive the square labeled B as lighter than it

actually is. The illusion is also created by the fact that the sensors in our eyes do

not register actual color but rather the difference in color between something and

what’s nearby. The contrast between square A and the light squares that surround

it and square B and the dark squares that surround it cause us to perceive

squares A and B quite differently, even though they are actually the same color,

as you can clearly see above after all of the surrounding context has been

removed.

The ability to use graphs effectively requires a basic understanding of how we

unconsciously interpret what we see.


33

This image illustrates the surprising effect that a simple change in the lightness of

the background alone has on our perception of color. The large rectangle displays

a simple color gradient of a gray-scale from fully light to fully dark. The small

rectangle is the same exact color everywhere it appears, but it doesn’t look that

way because our brains perceive visual differences rather than absolute values,

in this case between the color of the small rectangle and the color that

immediately surrounds it.

Among other things, understanding this should tell us that using a color gradient

as the background of a graph should be avoided.


34

There is a distinct image that has been worked into the picture of the rose, which

isn’t noticeable unless we know to look for it. Once primed with the image of the

dolphin, however, we can easily spot it in the rose.

Data visualizations must encode meaningful information as patterns that we can

learn to spot and understand.

(Note: The image of the rose was found at www.coolbubble.com.)


35

From this set of six playing cards, select one and remember it. I will now identify and

remove the card that you’ve selected, then rearrange those that remain. As I

advance to the next slide, you’ll discover that your card has been eliminated.


36

Amazing. And I can do this again and again. If you go back to the previous slide and

again pick a card, when you return to this slide you will see that I’ve once again

eliminated it.

Actually, as I’m sure you realize, this card trick is an illusion that makes use of the

limitations of short-term memory. None of the cards on the second screen are the

same as the cards on the first screen, but you probably didn’t notice this because

you only remembered the card that you selected, not the others.


37

In addition to understanding visual perception, visual analysis tools must also be

rooted in an understanding of how people think. Only then can they recognize and

support the cognitive operations that are necessary to make sense of information.

Memory plays an important role in human cognition. Because memory suffers from

certain limitations, visual analysis tools must be able to augment memory.

The example above illustrates one of the limitations of working memory. We only

remember that to which we attend. Any part of this image that never gets our

attention will not be missed when we shift to another version of the image that lacks

that particular part. If we don’t attend to it, we might notice the change from one

version of the image to the next, but only if the transition shift immediately from one

to another, without even a split second of blank space between them.

In addition to not remembering, we also don’t clearly see that on which we don’t

focus. To see something clearly, we must focus on it, for only a small area of

receptors on the retinas of our eyes are designed for high-resolution vision.

(Source: This demonstration of change blindness was prepared by Ronald A.

Rensink of the University of British Columbia. Several other examples of this visual

phenomenon can be found at

http://www.psych.ubc.ca/%7erensink/flicker/download/index.html.)



When we think about things, trying to make sense of them, the place where

information is temporarily stored to support this process is called working

memory. Working memory is a lot like RAM (random access memory) in a

computer in that it is limited in capacity and designed for temporary storage.

Compared to that hard disk drive, which is built into your computer or attached

to it externally, RAM seems very limited, but compared to working memory in

the human brain, RAM seems enormous. Only around three chunks of visual

information can be stored in working memory at any one time. Information that

comes in through our eyes or that is retrieved from long-term memory in the

moment of thought is extremely limited in capacity. If all four storage slots are

occupied, you must let something go to allow something new to come in. When

you release information from working memory, it can take one of two possible

routes on its way out: 1) it can be stored permanently in long-term memory by

means of a rehearsal process that we call memorization, or 2) it can simply be

forgotten.


To compare facts, you must hold them in working memory simultaneously.

Because we can hold so little in working memory at any one time, however, to

do analysis effectively, we must rely on external aids to memory. This is an

ideal job for a computer. Even a piece of paper that you jot down notes on to

keep track of information as you’re analyzing data is an external memory aid

that is quite powerful despite being low-tech. A computer running properly

designed software, however, can augment our ability to think about information

much better than pencil and paper.


Good visual analysis software can help us overcome the limitations of working

memory in several ways. The goal is to enable as many meaningful

comparisons as possible. Good tools can help us increase:

• The amount of information that we can compare (that is, greater

quantity)

• The range of information that we can compare (that is, more

dimensions)

• The different views of the information that we can compare (that is,

multiple perspectives)


Traditional BI relies mostly on tabular data displays. Tables are wonderful if you

need to look up individual values, compare a single value to another, or know

values precisely, but they don’t display patterns or trends. This is a problem,

because data analysis relies heavily on our ability to spot and make sense of

patterns and trends in data. Take a look at the table and compare it to this line

graph, which displays the same data. Relying on the table to discern the ups

and downs of sales through time and to compare the patterns of change from

region to region would yield very little of the information that is obvious in this

graph. Visual representations give form to data, making pattern, trends, and

exceptions easy to see.

Another advantage of properly designed graphs over tables for analytical

purposes is less obvious. If you needed to remember information in the table,

you could hold only about four of the values (that is, four of the monthly sales

numbers) in working memory at any one time. But by relying on the graph, 12

values are combined into each of the four lines to form a pattern that you could

hold entirely as a single chunk in working memory. Simply by giving visual form

to the values, you can hold much more information in memory.


You can extend the benefits of data visualization further by arranging several

graphs on the screen at the same time, such as shown in this visual crosstab.

Here you can see 24 small graphs arranged in familiar crosstab fashion to

present sales across four different dimensions at once: products within product

types by row, regions by column, and market size by the color of the line. Not

only does this approach make a great deal of data available to your eyes, it

does so across several dimensions, thus expanding the dimensionality of the

data well beyond traditional graphical displays.


To understand something, we often have to examine it from many angles and

focus on many parts. Too much business data analysis involves looking only for

one thing in particular. Is revenue going up? The answer is ―yes‖ or ―no‖—end

of story. Perhaps, however, you ought to look at revenues, expenses, profits,

marketing campaigns, seasonality, composition of the sales force, new product

introductions, and the competition to understand the richer story that your data

has to tell.


There’s an old folktale that you’ve probably all heard about three blind men who encounter an elephant one day for the first time and do their best to learn about it by touch alone. The experience of each is unique because each touches a different part of the elephant. This ancient story, originally from China, can teach us something important today about business intelligence (BI). According to the original Chinese tale, the first man touches the elephant’s ear, the second his legs, and the third his tail. From this point, here’s how the story goes:

The three blind men then went their way. Each one was secretly excited over the experience and had a lot to say, yet all walked rapidly without saying a word.

"Let's sit down and have a discussion about this queer animal," the second blind man said, breaking the silence.

"A very good idea. Very good." the other two agreed for they also had this in mind. Without waiting for anyone to be properly seated, the second one blurted out, "This queer animal is like our straw fans swinging back and forth to give us a breeze. However, it's not so big or well made. The main portion is rather wispy."

"No, no!" the first blind man shouted in disagreement. "This queer animal resembles two big trees without any branches."

"You're both wrong." the third man replied. "This queer animal is similar to a snake; it's long and round, and very strong."

How they argued! Each one insisted that he alone was correct. Of course, there was no conclusion for not one had thoroughly examined the whole elephant. How can anyone describe the whole until he has learned the total of the parts.

If I retold this story today to teach a lesson about BI, I might call it ―Three blind analysts and a data warehouse.‖ Business people struggle every day to make sense of data, stumbling blindly, touching only small parts of the information, and coming away with a narrow and fragmented understanding of what it means.


The tabular model forces us to view small slices of information one piece at a

time, which cannot possibly be stitched together in our brains to tell the whole

story.

46

The process of visual data analysis involves several common interactions with data to uncover what’s meaningful. Here are

some of the primary interactions:

• Sorting. The act of sorting data, especially by the magnitude of the values from high to low or low to high, features the ranking

relationship between those values and makes it easier to compare the magnitude of value to the next.

• Adding/removing variables. You might need to view different variable at different times during the analysis process, so it is

common to add or remove field of data from view as necessary

• Filtering. When you want to focus on a subset of data, nothing makes it easier to do so than filtering—the removal from view of

everything your not interested in at the moment.

• Highlighting. Sometimes you want to focus on a subset of information, but do so in a way that allows you to maintain a sense of

how that subset relates to the whole. Rather than filtering out the data that falls outside your range of focus, you can simply

reduce its visual salience or increase the visual salience of the data you wish to focus on. This allows you to focus on the subset

with less distraction from the whole in a way that allow you to remain aware of the whole. This is one way of achieving what’s

called a focus+context view.

• Aggregating/Disaggregating. Analysis often requires that you examine data a different levels of detail. Aggregation involves

viewing data at a higher level of summarization. Disaggregation involves viewing data at a lower level of detail.

• Drilling. Similar to disaggregation, drilling involves viewing data at a lower level of detail, but in a specific manner. Drilling also

means that you are changing the view to the next level in a defined hierarchy, and excluding from view all data that is not directly

related to the specific data value that you chose to drill into. For instance, if you drill into a particular product family, your next

view only products that belong to that product family. In other words, a form of filtering is involved.

• Grouping. Sometimes it is useful to combine members of a variable together, treating them as a single member of the variable.

This may take the form of combining some members and leaving others as they are, or of creating an entirely new variable that

combines all members of an existing variable into a groups to form members of a higher level variable.

• Zooming/Panning. When a data visualization contains so much that it is difficult to clearly see all the data at once, it is useful to

zoom in on that portion that you want to see more clearly. Panning involves moving around (for example, up, down, right, or left)

in a zoomed view to focus on a different part of the larger visualization.

• Re-visualizing. No one visual representation of data can show you everything there is to see, so visual analysis involves shifting

from one type of visualization to another to explore data from various perspectives.

• Re-expressing. Sometimes it is useful to express a quantitative variable as a different unit of measure, such as expressing

dollars as percentages.

• Re-scaling. No single quantitative scale on a graph can serve every analytical need. Rescaling involves changing the range of

the quantitative scale to make it easier to see particular patterns and sometimes even changing the nature of the scale, such as

from a normal scale to a logarithmic scale.



Direct dynamic interaction with the properly visualized data allows us to see

discover meaningful patterns, trends, and exceptions in the display and to interact

with it directly to filter out what we don’t need, drill into details, combine multiple

variables for comparison, etc., in ways that promote a smooth flow between seeing

something, thinking about it, and manipulating it, with no distracting lags in between.

This is what I call ―visual analysis at the speed of thought.‖


When new recruits by intelligence organizations are trained in spy craft, they are

taught a method of observation that begins by getting an overview of the scene

around them while being sensitive to things that appear abnormal, not quite right,

which they should then focus in on for close observation and analysis.

A visual information-seeking mantra for designers: ‘Overview first, zoom and

filter, then details-on-demand.’

(Readings in Information Visualization: Using Vision to Think, Stuart K. Card,

Jock D. Mackinlay, and Ben Shneiderman, Academic Press, San Diego,

California, 1999, page 625)

Having an overview is very important. It reduces search, allows the detection of

overall patterns, and aids the user in choosing the next move. A general heuristic

of visualization design, therefore, is to start with an overview. But it is also

necessary for the user to access details rapidly. One solution is overview +

detail: to provide multiple views, an overview for orientation, and a detailed view

for further work.

(Ibid., page 285)

Users often try to make a ‘good’ choice by deciding first what they do not want,

i.e. they first try to reduce the data set to a smaller, more manageable size. After

some iterations, it is easier to make the final selection(s) from the reduced data

set. This iterative refinement or progressive querying of data sets is sometimes

known as hierarchical decision-making.

(Ibid., page 295)


Shneiderman’s technique begins with an overview of the data—the big picture. Let

your eyes search for particular points of interest in the whole.


When you see a particular point of interest, then zoom in on it.


Once you’ve zoomed in on it, you can examine it more closely and in greater detail.


Often you must remove data that is extraneous to your investigation to better focus

on the relevant data.


Filtering out extraneous data removes distractions from the data under investigation.


Visual data analysis relies mostly on the shape of the data to provide needed

insights, but there are still times when you need to see the details behind the shape

of the data. Having a means to easily see the details when you need them, without

having them in the way when you don’t works best.


Information cannot speak for itself. It needs our help. It relies on us to give it a voice.

When we do, information can tell its story, and will thus become knowledge. The

ultimate goal, however, isn’t knowledge; it is wisdom. Knowledge becomes wisdom

when it is used to do something good. Only when we use what we know to make

the world a better place has information served its purpose and we have done our

job.

Our networks are awash in data. A little of it is information. A smidgen of this

shows up as knowledge. Combined with ideas, some of that is actually useful.

Mix in experience, context, compassion, discipline, humor, tolerance, and

humility, and perhaps knowledge becomes wisdom.

Turning Numbers into Knowledge, Jonathan G. Koomey, 2001, Analytics Press:

Oakland, CA page 5, quoting Clifford Stoll.


O perpetual revolution of configured stars,

O perpetual recurrence of determined seasons,

O world of spring and autumn, birth and dying!

The endless cycle of idea and action,

Endless invention, endless experiment,

Brings knowledge of motion, but not of stillness;

Knowledge of speech, but not of silence;

Knowledge of words, and ignorance of The Word.

All our knowledge brings us nearer to our ignorance,

All our ignorance brings us nearer to death,

But nearness to death no nearer to God.

Where is the Life we have lost in living?

Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?

Excerpt from The Rock, 1930, T.S. Elliot

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