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.

 

There Is No Science of Data

January 23rd, 2017

“Data Science” is a misnomer. Science, in general, is a set of methods for learning about the world. Specific sciences are the application of these methods to particular areas of study. Physics is a science: it is the study of physical phenomena. Psychology is a science: it is the study of the psyche (i.e., the human mind). There is no science of data.

Data is a collection of facts. Data, in general, is not the subject of study. Data about something in particular, such as physical phenomena or the human mind, provide the content of study. To call oneself a “data scientist” makes no sense. One cannot study data in general. One can only study data about something in particular.

Most people who call themselves data scientists are rarely involved in science at all. Instead, their work primarily involves mathematics, and usually the branch of mathematics called statistics. They are statisticians or mathematicians, not data scientists. A few years ago, Hal Varian of Google declared that “statistician” had become the sexy job of our data-focused age. Apparently, Varian’s invitation to hold up their heads in pride was not enough for some statisticians, so they coined a new term. When something loses its luster, what do you do? Some choose to give it a new name. Thus, statisticians become data scientists and data becomes “Big Data.” New names, in and of themselves, change nothing but perception; nothing of substance is gained. Only by learning to engage in data sensemaking well will we do good for the world. Only by doing actual good for the world will we find contentment.

So, you might be wondering why anyone should care if statisticians choose to call themselves data scientists, a nonsensical name. I care because people who strive to make sense of data should, more than most, be sensitive to the deafening noise that currently makes the knowledge that resides in data so difficult to find. The term “data scientist” is just another example of noise. It adds confusion to an overly and increasingly complicated world.

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P.S. I realize that the term “data science” is only one of many misnomers that confuse the realm of data sensemaking. I myself am guilty of using another: “business intelligence.” This term is a misnomer (and an oxymoron as well) in that, as with data science, when it is practiced effectively, business intelligence is little more than another name for statistics. It has rarely been practiced effectively, however. Most of the work and products that bear the name business intelligence have delivered overwhelming mounds of data that is almost entirely noise.

The Least Amount of Information

January 12th, 2017

As its default mode of operation, the human brain uses the least amount of information necessary to make sense of the world before making decisions. This product of evolution was an efficient and effective strategy when we lived in a simple, familiar world. We no longer live in that world. We can still use this strategy to make sense of those aspects of our world that remain relatively simple and familiar (i.e., walking from point A to point B without tripping or falling into a hole), but we must use more advanced strategies when navigating the complex and/or unfamiliar. The default mode of thinking, which is intuitive, feeling-based, and fast, utilizing efficient heuristics (rules of thumb), is called System 1 thinking. The more advanced and more recently evolved mode of thinking, which is reflective, rational, and slow, is called System 2 thinking. Both are valid and useful. The trick is knowing when to shift from System 1 to System 2.

In my opinion, many of the problems that we suffer from today occur because we fail to shift from System 1 to System 2 when needed. For instance, electing the president of the most powerful nation on Earth requires System 2 thinking. That’s obvious, I suppose, but even such a mundane task as grocery shopping requires System 2 thinking to avoid choices that are fueled merely by marketing.

Defaults are automatic and largely unconscious. A single mode-of-thinking default doesn’t work when life sometimes requires System 1 and at other times requires System 2. Instead, rather than a default mode of thinking, we would benefit from a default of shifting into one or the other mode depending on the situation. This default doesn’t exist, but it could be developed, to an extent, through a great deal of practice over a great deal of time. Only by repeating the conscious act of shifting from System 1 to System 2, when necessary, over and over again, will we eventually reach the point where the shift will become automatic.

For now, we can learn to bring our mode of thinking when making decisions into conscious awareness and create the moments that are necessary to effect the System 1 to System 2 shift when it’s needed. Otherwise, we will remain the victims of hunter-gatherer thinking in a modern world that demands complex and sometimes unfamiliar choices, many of which come with significant, potentially harmful consequences. How do we make this happen? This is a question that deserves careful (i.e., System 2) study. One thing I can say for sure, however, is that we can learn to pause. The simple act of stopping and taking a moment to ask, “Is this one of those situations that, because it is complex or unfamiliar, requires reflection?”, is a good start.

Take care,

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Confessions of a Bipolar Technologist

January 5th, 2017

Last June I celebrated my 62nd birthday. As I look back on my life, my early years seem like distant memories of a different age, yet the years also seem to have flown by in an instant. Our lives are brief when superimposed on history, but they can be rich if we find a way to contribute to history. I feel that my work in the field of data visualization has provided that opportunity, and I’m incredibly grateful.

I have worked as an information technologist for 33 years. Similar to many other thoughtful IT professionals, I have a love-hate relationship with technologies. My feelings about them range from ecstasy to depression and disgust. I love technologies that are useful and work well, but I dislike all else, which includes most of the IT products on the market.

We humans are distinguished from other species in part by our creation and use of tools (a.k.a., technologies). Our relationship with these technologies has changed considerably since the hunter-gatherer days, especially since the advent of the computer. The human condition is increasingly affected for both good and ill by our technologies. We need to evaluate them with increasing awareness and moral judgment. We need to invite them into our lives and the lives of our children with greater care.

In the early days of my IT career, I spent a decade working in the world of finance. I was employed by one of the financial institutions that later contributed to the meltdown of 2007 and 2008. In fact, If I’m not mistaken, my employer invented the infamous reverse-interest mortgage loan. I was a manager in the loan service department at a time when a large group of employees had the job of explaining to customers why their loan balances were increasing. Fortunately, I never had to answer those questions myself, which I would have found intolerable.

During those years, I remember learning about the famous 80/20 rule (a.k.a., the Pareto Principle), but what I learned at the time was a perversion of the principle that says a lot about the culture in which I worked. I was told that the 80/20 rule meant that we should only work to satisfy 80% of the need, for the remaining 20% wasn’t worth the effort. When we built IT systems, we attempted to address only 80% of what was needed with tools that worked only 80% of the time. Excellence was never the goal; we sought “good enough.” But good enough for what? For most technology companies, the answer is “good enough to maximize revenues for the next few quarters.” A product that is only 80% good or less can be camouflaged for awhile by deceitful marketing. By the time customers discover the truth, it will be too late: their investment will have already been made and those who made it will never admit their error, lest they be held responsible.

Traditional theories of economics assume rational behavior. A relatively recent newcomer, Behavioral Economics, has shown, however, that human economic behavior is often far from rational. The same can be said of the human production of and use of technologies. When our progenitors became tool users and eventually tool creators, for eons those tools always arose from real need and they rarely caught on unless they worked. This is no longer true, especially of information technologies. Much that we do with computers today did not emerge in response to real needs, is often misapplied in ways that produce little or no benefit, and far too often works poorly, if at all. This suggests that a new scientific discipline may be needed to study these technologies to improve their usefulness and to diminish their waste and harmful effects. I propose that we call this new field of study Itology (i.e., IT-ology, pronounced eye-tology). Its focus would be on the responsible creation and use of information technologies. Whether the name “Itology” is adopted doesn’t matter, but making this area of study integral to IT certainly does.

Take care,

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Algorithms as WMDs

January 2nd, 2017

For data sensemakers and others who are concerned with the integrity of data sensemaking and its outcomes, the most important book published in 2016 was Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil. This book is much more than a clever title. It is a clarion call of imminent necessity.

WMD Cover

Data can be used in harmful ways. This fact has become magnified to an extreme in the so-called realm of Big Data, fueled by an indiscriminate trust in information technologies, a reliance on fallacious correlations, and an effort to gain efficiencies no matter the cost in human suffering. In Weapons of Math Destruction, O’Neil reveals the dangers of data-sensemaking algorithms that employ statistics to score people and institutions for various purposes in ways that are unsound, unfair, and yes, destructive. Her argument is cogent and articulate, the product of deep expertise in data sensemaking directed by a clear sense of morality. Possessing a Ph.D. in mathematics from Harvard and having worked for many years herself as a data scientist developing algorithms, she is well qualified to understand the potential dangers of algorithms.

O’Neil defines WMDs as algorithms that exhibit three characteristics:

  1. They are opaque (i.e., inscrutable black boxes). What they do and how they do it remains invisible to us.
  2. They are destructive. They are designed to work against the subjects’ best interests in favor of the interests of those who use them.
  3. They scale. They grow exponentially. They scale not only in the sense of affecting many lives but also by affecting many aspects of people’s lives. For example, an algorithm that rejects you as a potential employee can start a series of dominoes in your life tumbling toward disaster.

O’Neill identifies several striking examples of WMDs in various realms, including evaluating teachers, identifying potential criminals, screening job applicants and college admissions candidates, targeting people for expensive payday loans, and pricing loans and insurance variably to take advantage of those who are most vulnerable.

During the Occupy Wall Street movement, following the financial meltdown that was caused in part by WMDs, O’Neill became increasingly concerned that the so-called Big Data movement could lead to harm. She writes:

More and more, I worried about the separation between technical models and real people, and about the moral repercussions of that separation. In fact, I saw the same pattern emerging that I’d witnessed in finance: a false sense of security was leading to widespread use of imperfect models, self-serving definitions of success, and growing feedback loops. Those who objected were regarded as nostalgic Luddites.

I wondered what the analogue to the credit crisis might be in Big Data. Instead of a bust, I saw a growing dystopia, with inequality rising. The algorithms would make sure that those deemed losers would remain that way. A lucky minority would gain ever more control over the data economy, raking in outrageous fortunes and convincing themselves all the while that they deserved it.

WMDs are misuses of computers and data. She writes:

WMDs…tend to favor efficiency. By their very nature they feed on data that can be measured and counted. But fairness is squishy and hard to quantify. It is a concept. And computers, for all of their advances in language and logic, still struggle mightily with concepts…And the concept of fairness utterly escapes them…So fairness isn’t calculated into WMDs. And the result is massive, industrial production of unfairness.

WMDs are sometimes the result of good intentions, and they are passionately defended by their creators, but that doesn’t excuse them.

Injustice, whether based on greed or prejudice, has been with us forever. And you could argue that WMDs are no worse than the human nastiness of the recent past. In many cases, after all, a loan officer or hiring manager would routinely exclude entire races, not to mention an entire gender, from being considered for a mortgage or a job offer. Even the worst mathematical models, many would argue, aren’t nearly that bad.

But human decision making, while often flawed, has one chief virtue. It can evolve. As human beings learn and adapt, we change, and so do our processes. Automated systems, by contrast, stay stuck in time until engineers dive in to change them…Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide.

This book is more than an exposé. O’Neil goes on to suggest what we can do to prevent WMDs. It is incredibly important that we take these steps and do so starting now. Many of the industrial revolution’s abuses were eventually curtailed through a heightened moral awareness and thoughtful regulation. Now is the time to clean up the abuses of algorithms much as we once cleaned up the abuses of slavery and sweatshops. I heartily recommend this book.

Take care,

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Is there a Role for Natural Language Processing in Data Visualization?

December 6th, 2016

Several software vendors are integrating natural language processing (NLP) into data visualization tools these days, which should cause us to question the merits of this feature. In most cases, NLP is being used as an input interface—a way to specify what you would like to see—but some vendors are now proposing a reverse application of NLP as an output interface to express in words what already appears in a data visualization. In my opinion, NLP has limited usefulness in the context of data visualization. It is one of those features that vendors love to tout for the cool factor alone.

We express ourselves and interact with the world through multiple modes of communication, primarily through verbal language (i.e., spoken and written words), visual images, and physical gestures (i.e., movements of the body). These modes are not interchangeable. Each exists because different types of information are best expressed using specific modes. Even if you consider yourself a “word” person, you can only communicate some information effectively using images, and vice versa. Similarly, sometimes a subtle lift of the brow can say what we wish in a way that neither words nor pictures could ever equal. We don’t communicate effectively if we stick with the mode that we prefer when a different mode is better suited for the task.

NLP is computer-processed verbal language. Are words an appropriate means to specify what you want to see in data (i.e., input) or to explain what has already been expressed in images (i.e., output)? Let’s consider this.

First, we’ll begin with the usefulness of NLP as a means of input. Let’s sneak up on this topic by first recognizing that words are not always the most effective or efficient means of input. Just because you can get a computer to process words as a means of input doesn’t mean that it’s useful to do so. Would you use words to drive your car? (Please note that I’m not talking about the brief input that you would provide a self-driving car.) The commands that we issue to our cars to tell them where and how fast to go are best handled through a manual interface—one that today involves movements of our hands and feet. We could never equal with words what we can communicate to our cars immediately and precisely with simple movements. This is but one of many examples of situations that are best suited to physical gestures as the means of input. So, are words ever an appropriate means to specify what you’d like to see in data? Rarely, at best. NLP would only be useful as a means of input either in situations when the data visualization tool that you’re using has a horribly designed interface but a well-designed NLP interface (this tool doesn’t exist) or when you need to use a tool but have not yet learned its interface.

The second situation above corresponds to the “self-service” business intelligence or data analysis model that software vendors love to promote but can never actually provide. You cannot effectively make sense of data without first developing a basic set of data analysis skills. If you’ve already developed this basic set of skills, you would never choose NLP as your means of input, for a well-designed interface that you manipulate using manual gestures will almost always be more efficient and precise. Consequently, the only time that NLP is useful as a data visualization input interface is when people with no analytical skills want to view data. For example, a CEO could type or say “Show me sales revenues in U.S. dollars for the last year by product and by month” and the tool could potentially produce a line graph that the CEO could successfully read. Simple input such as this could certainly be handled by NLP. Chances are, however, that the simple requests that this CEO makes of data are already handled by predesigned reports that are readily available. Most likely, what the CEO would like to request using words would be something more complex, which NLP would not handle very well, and even if it could, the CEO might misunderstand once the results are displayed due to a lack of statistical knowledge. It isn’t useful to enable people to request visualizations that they cannot understand.

Now let’s consider the use of NLP as a means of expressing in words what appears in a data visualization. When properly done, we visualize data to present information that cannot be expressed at all or as well using words or numbers. For example, we visualize data to reveal patterns or to make rapid comparisons, which could never be done based solely on words or statistics. If the information can only be properly understood when expressed visually, using NLP to decipher the visualization and attempt to put it into words makes no sense. The only possible situation that I can imagine when this would provide any value at all would be for people who are visually impaired, rendering them unable to see the visualization. The caveat in this case, however, is the fact that words would never provide for someone who is visually impaired what an image could provide if the person could see. So, however cool it might seem when a vendor claims to apply NLP for this purpose, it’s a silly feature without substance.

You might argue, however, that NLP algorithms could be used to supplement a visualization by providing a narrative explanation, much as a presenter might explain the primary message of a chart and point out specific features of interest. Do you really believe that software developers can write computer algorithms that successfully supplement data visualizations in this manner, without human intervention? I suspect that only simple charts could be successfully interpreted using algorithms today.

This is not a topic that I’ve explored extensively, so it is certainly possible that I’m missing potential uses of NLP. If you believe that there is more to this than I’m seeing, please let me know. I will gladly revise my position based on good evidence.

Take care,

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