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.

 

We Never Think Alone: The Distribution of Human Knowledge

May 3rd, 2017

Only a small portion of the knowledge that humans have acquired resides in your head. Even the brightest of us is mostly ignorant. Despite this fact, we all suffer from the illusion that we know more than we actually do. We suffer from the “knowledge illusion,” in part, because we fail to draw accurate boundaries between the knowledge that we carry in our own heads and the knowledge that resides in the world around us and the minds of others. A wonderful new book by two cognitive scientists, Steven Sloman and Philip Fernback, titled The Knowledge Illusion: Why We Never Think Alone, describes the distributed nature of human knowledge and suggests how we can make better use of it.

The Knowledge Illusion

The following four excerpts from the book provide a sense of the authors’ argument:

The human mind is both genius and pathetic, brilliant and idiotic. People are capable of the most remarkable feats, achievements that defy the gods…And yet we are equally capable of the most remarkable demonstrations of hubris and foolhardiness. Each of us is error-prone, sometimes irrational, and often ignorant…And yet human society works amazingly well…

The secret of our success is that we live in a world in which knowledge is all around us. It is in the things we make, in our bodies and workspaces, and in other people. We live in a community of knowledge.

The human mind is not like a desktop computer, designed to hold reams of information. The mind is a flexible problem solver that evolved to extract only the most useful information to guide decisions in new situations. As a consequence, individuals store very little detailed information about the world in their heads. In that sense, people are like bees and society a beehive: Our intelligence resides not in individual brains but in the collective mind.

Being smart is about having the ability to extract deeper, more abstract information from the flood of data that comes into our senses…The mind is busy trying to choose actions by picking out the most useful stuff and leaving the rest behind. Remembering everything gets in the way of focusing on the deeper principles that allow us to recognize how a new situation resembles past situations and what kinds of actions will be effective.

In a world with rapidly increasing stores of information, it is critical that we learn how to find the best information (the signals) among the mounds of meaningless, erroneous, or irrelevant information (the noise) that surrounds us. Individually, we can only be experts in a few domains, so we must identify and rely on the best expertise in other domains. We don’t benefit from more knowledge; we benefit from valid and useful knowledge. One of the great challenges of our time is to find ways to identify, bring together, and encourage the best of what we know.

The power of crowdsourcing and the promise of collaborative platforms suggest that the place to look for real superintelligence is not in a futuristic machine that can outsmart human beings. The superintelligence that is changing the world is the community of knowledge. The great advances in technology aren’t to be found in creating machines with superhuman horsepower; instead, they’ll come from helping information flow smoothly through ever-bigger communities of knowledge and by making collaboration easier. Intelligent technology is not replacing people so much as connecting them.

 This book is well written and accessible. It provided me with many valuable insights. I’m confident that it will do the same for you.

Take care,

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Can VR Enhance Data Visualization?

May 1st, 2017

In addition to the growing hype about AI (artificial intelligence) and NLP (natural language processing) as enhancers of data visualization, VR (virtual reality) is now making the same erroneous claim. How does VR enhance data visualization? Here’s an answer that was recently given by Sony Green, the head of business development for a startup named Kineviz:

For a lot of things, 2D is still the best solution. But VR offers a lot of advantages over existing data visualization solutions, especially for certain kinds of data. When you get into really high dimensional data, something like 100 different dimensions per node. It’s difficult to keep track of all that info with lots of 2D graphics and it becomes a very large cognitive load for people to track them on multiple screens at once.

VR allows us to tap into our natural ability to process special information. Without looking around, we have an innate understanding of the spaces we are in because that’s how our brains are wired. In a simulated environment created by VR, we use these natural ways of processing information that a 2D screen can’t offer.

Furthermore, VR opens up use cases that were previously impossible by lowering the barrier for common users. You don’t have to be a data scientist: anyone who can play a game can use VR to explore data science in a way that is intuitive.

TechNode, Emma Lee, April 28, 2017

So, VR supposedly “offers a lot of advantages.” What are these advantages? According to Green, VR makes it possible for our brains to process “100 different dimensions.” This isn’t true. VR adds a simulation of a single spatial dimension: depth. I can think of no way that VR can enable our brains to process more than one additional dimension of data compared to what we can process using 2-D displays. Plus, the simulation of depth is of little benefit, for we don’t perceive depth well, unlike our perception of 2-D space (up and down, left and right). And let us not forget that we can only hold from three to four chunks of visual information in working memory at once, so even if VR could add many more dimensions of data in some way, it would be of no use to our limited brains if we weren’t able to process all of those dimensions simultaneously.

What else can VR do? “VR allows us to tap into our natural ability to process special information.” Apparently, this special information has something to do with spatial awareness, but how does this help us visualize data? According to Sony Green, we’d better figure it out and get on board, because, with VR, data exploration and analysis can be done by anyone who can play a game. Who knew that data analysis was so easy? The claim that “without looking around, we have an innate understanding of the spaces we are in” is humorous. We have no understanding of the spaces that we’re in without looking around or exploring them in some other way, such as by touch.

VR attempts to simulate the 3-D world that we live in. In the actual world, I can place data visualizations throughout a room on various screens or printed pages, and I can then walk up to and examine one at a time. Similarly, VR can place data visualizations throughout a virtual room, and when it does I must still virtually walk around to view them one at a time. Are the data visualizations themselves enhanced? Not in the least. Making the graphs appear more three-dimensional than they appear on a flat screen adds no real value.

Years ago I was approached by someone who was creating data visualizations for the VR environment Second Life. She was enthusiastic about her work. When I took a look, I found a collection of 3-D bar graphs, line graphs, scatterplots, etc., which I could walk around and even upon, looking down from the lofty heights of tall bars and lines, and with virtual superpowers I could even fly around them, but this actually made the graphs harder to read. It is much easier and efficient to sit still and view 2-D data visualizations on my desktop monitor.

Just to make sure that I haven’t missed any new uses of VR for data visualization, I did a quick search and found nothing but more of the same. In the example below, the Wall Street Journal allows us to ride along a line graph of the NASDAQ, much like riding a roller coaster:

WSJ VR

Imagine that you’re viewing this using a VR headset. What useless fun! And in the example below, Nirvana Labs allows us to view a map (currently off the bottom of the screen), a bar graph (the transparent vertical cylinders), and a line graph (the bottom edge appears at the top of the screen), but they are much harder to read in VR than they would be as a 2-D screen display. A VR headset makes it possible for us to walk around the graphs, but that isn’t useful.

Nirvana VR

I have seen 3-D displays of physical objects that are actually useful, but 3-D displays of graphs are almost never useful, and placing them in VR doesn’t change this fact.

Don’t let yourself be suckered in by false marketing claims. Software vendors are always looking for some new way to separate us from our money. When you encounter people who claim that VR adds value to data visualization, ask them to prove it. Request an example of VR that works better than a 2-D display of the same data. Look past the cool factor and attempt to make sense of the data. If you come across a beneficial use case for data visualization in VR, I’d love to see it.

Take care,

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Do tooltips reduce the need for precision in graphs?

April 18th, 2017

This blog entry was written by Nick Desbarats of Perceptual Edge.

Should you include grid lines in your graph? If so, how many? Is it O.K. to add another variable to your graph by encoding it using size (for example, by varying the size of points in a scatterplot) or color intensity (for example, by representing lower values as a lighter shade of blue and higher ones as darker)? How small can you make your graph before it becomes problematic from a perceptual perspective? These and many other important graph design decisions depend, in part, on the degree of precision that we think that our audience will require. In other words, they depend on how precisely we think that our audience will need to be able to “eyeball” (i.e., visually estimate) the numerical values of bars, lines, points, etc. in our graph. If we think that our audience will require no more than approximate precision for our graph to be useful to them, then we can probably safely do away with the gridlines and add that other variable as color intensity or size. If we have limited space in which to include our graph on a page or screen, we could safely make it quite small since we know that, in this particular case, the reduction in precision that occurs when reducing a graph’s size wouldn’t be a problem.

Small Graph

Our audience won’t be able to eyeball values all that precisely (what exactly are Gross Sales or Profit for the South region?), though such a design would provide enough precision for our audience to see that, for example, the West had the highest Gross Sales, and that it was about four times greater than the East, which had relatively low Profit, etc. Despite its lack of high precision, this graph contains many useful insights and may be sufficient for our audience’s needs.

If, on the other hand, we’ve spoken to members of our audience and have realized that they’ll need to visually estimate values in the graph much more precisely in order for the graph to be useful to them, then we’re going to have to make some design changes to increase the precision of this graph. We might need to add gridlines, break the quantitative scale into smaller intervals, find a more precise way to represent Profit (for example, by encoding it using the varying lengths of bars or the 2D positions of points), and perhaps make our graph larger on the screen or page.

Side-by-side graphs

As you probably noticed, adding gridlines, making the graph larger, and breaking the quantitative scale into smaller intervals all came at a cost. While these changes did increase the precision of our graph, they also made it busier and bigger. Being forced to make these types of design trade-offs is, of course, very common. In nearly every graph that we design, we must struggle to balance the goal of providing the required level of precision with other, competing goals such as producing a clean, compact, uncluttered, and quick-to-read design.

What if we know, however, that our graph will only ever be viewed in a software application that supports tooltips, i.e., that allows our audience to hover their mouse cursor or finger over any bar, line, point, etc. to see its exact textual value(s) in a small popup box?

Small graph with tooltip

In this case, perfect precision is always available if the audience ever needs to know the exact value of any bar, point, etc. via what researcher Ben Shneiderman termed a “details-on-demand” feature. In the 1990’s, Shneiderman noted that suppressing details from a visualization and showing specific details only when the user requests them enables the user to see a potentially large amount of information without being overwhelmed by an overly detailed display. A well-designed visualization enables users to see where interesting details may lie and the details-on-demand feature then enables them to see those details when they’re needed, but then quickly hide them again so that they can return to an uncluttered view and look for other potentially interesting details.

So, does the availability of details-on-demand tooltips mean that we can stop worrying about precision when making design decisions and optimize solely for other considerations such as cleanness? Can we set the “precision vs. other design considerations” trade-off aside in this case? I think that the answer to this question is a conditional “yes.” We can worry less about precision if we know all of the following:

  1. Our graph will only be viewed in a software application that supports tooltips (which most data visualization products now support and enable by default). If we think that there’s anything more than a small risk that our audience will, for example, share the graph with others by taking a screenshot of it or printing it (thereby disabling the tooltips), then precision must become one of our primary design considerations again.
  2. Our audience is aware of the tooltips feature.
  3. Our audience will only need to know precise values of the bars, points, lines, etc. in our graph occasionally. If we think that our audience will frequently need to know the precise values, giving them a lower-precision graph with tooltips will force them to hover over elements too often, which would obviously be far from ideal. In my experience, however, it’s rare that audiences really do need to know the precise values of elements in a graph very often—although they may claim that they do.

If we don’t know if all three of these conditions will be true for a given graph, we don’t necessarily have to ramp up its size, add gridlines, etc. in order to increase its precision, though. If we have a little more screen or page real estate with which to work, another solution is to show a clean, compact, lower-precision version of our graph, but then add the textual values just below or to the side of it. If the audience requires a precise value for a given bar, point, etc. in our graph, it’s available just below or beside the graph.

Small graph and table
Graph with columns of text

If we think that, for example, our audience is going to be embedding this graph into a PDF for a management meeting (thus disabling the tooltips) and that higher precision will be required by the meeting attendees, this would be a reasonable solution. For some graphs, however, the set of textual values may end up being bigger than a higher-precision of version of the graph, in which case the higher-precision graph may actually be more compact.

As with so many other data visualization design decisions, knowing how to balance precision versus other design considerations requires knowing your audience, what they’re going to be using your visualizations for, and—particularly in this case—what devices, applications or media they’ll be using to view the visualization.

Nick Desbarats

Prove It, If You Can

April 10th, 2017

Despite the popular notion that we live in the Information Age, most organizations still base most of their decisions on gut instinct rather than information (i.e., evidence of what’s actually happening and what actually works). Intuition, based on true, hard-won expertise, has a role in organizational decision making, but many decisions, especially the most important decisions, should be rooted in evidence. In their excellent book, Hard Facts, Dangerous Half-Truths and Total Nonsense, Jeffrey Pfeffer and Robert I. Sutton wrote:

Business decisions, as many of our colleagues in business and your own experience can attest, are frequently based on hope or fear, what others seem to be doing, what senior leaders have done and believe has worked in the past, and their clearly held ideologies—in short, on lots of things other than facts…If doctors practiced medicine the way many companies practice management, there would be far more sick and dead patients, and many more doctors would be in jail.

We cannot know how well we’re performing without evidence of what’s going on and what works. To improve performance, we must measure it. Relatively few organizations can prove that they’re performing well, but they certainly spin great yarns trying to convince us and even themselves. According to Stacey Barr, high-performance organizations can prove their success with facts. In her efforts to help organizations enter the realm of high performance, she has written a new book to make the case for evidence-based leadership titled Prove It! How to Create a High-Performance Culture and Measurable Success.

Prove It Cover

Here is the opening paragraph of Chapter 1:

Almost any organisation can prove that it does things. It can prove that it hires people, that those people carry out different tasks, and that money is earned and spent. But what many organisations cannot prove is the most important thing: whether they are fulfilling their purpose or not. High-performance organisations don’t just do stuff. They have an impact—ideally, the impact they exist to make. And they can prove how much impact they create.

She doesn’t just make the case for evidence-based leadership, but also explains in concise, clear, and readable prose how to achieve it. This book is short, sweet, and practical. It is also incredibly smart. If you are an information worker, but are frustrated because you work in an organization that doesn’t base its decisions on evidence, despite what it claims, this is the book that you should place in the hands of your organization’s leaders. Let Stacey Barr make the case for organizational improvement through better evidence-based leadership for you. Without buy-in from your executives, you don’t have a chance.

Take care,

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Confusion about Line Graphs

April 5th, 2017

You might consider the way a humble and common line graph works to be obvious. If you learned to use them in the workplace, chances are that you think of line graphs as a conventional means to display one or more sets of time-series values. This is the graphical use of lines that typically bears the name “line graph.” Confusion is created, however, by the fact that lines can be used for various purposes in graphs. One common use is lines or curves of best fit, also known as trend lines or fit models. These lines summarize patterns in quantitative data sets rather than representing the actual values on which that summary is based. Lines are also used to represent multivariate data in the form of parallel coordinates plots, which work quite differently from regular line graphs. There is another graphical use of lines that you have been exposed to if you’ve studied mathematics beyond a basic level: equation graphs and function graphs. These graphs do not represent actual values that have been collected, but instead model mathematical equations and functions, representing visually what can also be expressed using mathematical notation. If you’ve been exposed extensively to this graphical use of lines, you might find regular lines graphs a bit confusing and misleading, despite their simplicity.

Every once in a while when teaching my Show Me the Numbers course about table and graph selection and design, a participant expresses concern that my use of line graphs is misleading. I’ll illustrate this situation with an example. The graph below is a simple, typically designed line graph that displays one set of time-series values.

Typical Line Graph

This line graph displays only 10 quantitative values. The values are positioned as points along the line, one for each year. The line connects the 10 data points as straight segments from one data point to the next. For this reason, what I’m calling a typical line graph is sometimes referred to more specifically as a segmented line graph. The purpose of the line is to clearly display the pattern of change along the time series. The quantitative values are aggregates—in this case sums of profits—for each interval of time along the X axis. The slope of each line segment represents the nature of the change between each contiguous set of intervals (e.g., between 2009 and 2010). Nowhere between contiguous data points do any other values exist. Stated differently, no values can be read along a line apart from a single data point for each interval.

The objection that has been expressed in my classes a handful of times usually goes something like this: “That line graph is misleading. It claims that from 2009 to 2010 the profits decreased by a constant amount during that period of time, but there was probably a great deal of variation in daily profits.” This objection is usually based on a misunderstanding that stems from experience using lines to graph mathematical equations and functions. When a line is used for that purpose, we don’t usually call it a line graph. Instead, it is a “graph of an equation” or a “graph of a function,” among other potential terms. With graphs of equations or functions, every possible position along the line represents two values, one associated with the X axis and another associated with the Y axis. The scales along both axes are continuous quantitative scales. Given this mathematical use of lines in graphs, we can certainly understand why someone might find the way that a regular line graphs work for displaying time series to be confusing, resulting in the concern that they sometimes express. If you are one of those who shares this concern, just realize that lines are used for various purposes in graphs and that, in regular line graphs for displaying time series, they work differently than for mathematical equations and functions.

In a regular line graph of a time series, the scale along the X axis is what’s called an interval scale. It functions as a type of categorical scale that labels what the values represent. An interval scale begins as a range of quantitative values, which is then subdivided into a set of equally sized intervals, and a label is associated with each to identify it (e.g., >=20 & <30, >=30 & <40, etc.). Time is quantitative in nature. By this I mean that you can sum the number of hours, days, weeks, months, etc., to quantify the duration of time that has transpired between any two points in time. When we subdivide time into intervals of equal size, we express time as a categorical scale of an interval type. (Even though months are not all equal in size, when we display a times series by month, we treat it as if the months are equal when they are close enough in size to suit our purpose.) The intervals are not specific points in time, but are instead specific ranges of time. In a line graph that displays a single set of time-series values, one and only one value appears for each interval, which usually consists of an aggregate value per interval (most often a sum, and secondarily an average), but on occasions, rather than aggregates, the value for each interval is a measurement taken at a particular point in time (e.g., the closing price of a stock with daily intervals).

A great deal of confusion results from the many ways that we graphically represent data and the sloppy terms that we use to describe them. We would all benefit from a better understanding of the fundamentals and more clearly defined terms.

Take care,

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