Bringing Numbers to Life

2. Articulating the Firehose

by John Armitage
Quantity has a quality all it’s own
- Joseph Stalin

As the windsurfing example shows, more and more of the world and its activity is being recorded, measured, and quantified with information technology. Business transactions have long been recorded in detail, and in fact some of the earliest surviving samples of writing from ancient Sumeria in the form of recorded business transactions, such as receipts and inventories, pressed into clay tablets with a wooden stylus. Today, as the cost of digital recording, storage, and delivery drops, activities of less immediate or obvious value are also being meticulously documented and saved. The recording pace and scope, alongside technological change in general, is accelerating dramatically. Personal and organizational publishing via the Web, social media, business transactions, sensors embedded within the built and natural world – the IoT – and wearable devices like Google Glass (for better or worse) are collectively writing a real-time empirical history of existence on earth. Video surveillance, internet browsing behavior, weather readings, industrial machinery metrics, and an endless stream of trivial operational facts known as “business exhaust” contribute to the giant pile of bits awaiting some sort of analysis.

While this ever-increasing collection of data can improve the understanding, control, and improvement of many aspects of business and life, it’s no secret that the sheer volume of data being generated is unprecedented, and exceeding our ability to view, understand, and properly apply it. Interestingly, this state of affairs is not new:

“In his Atlas of 1786, William Playfair wrote of the increasing complexity of modern commercial life. He pointed out that when life was simpler and data were less abundant, an understanding of economic structure was both more difficult to formulate and less important for success. But by the end of the eighteenth century, this was no longer true. Statistical offices had been established and had begun to collect a wide variety of data from which political and commercial leaders could base their decisions. Yet the complexity of these data precluded their easy access by any but the most diligent.”

“Playfair’s genius was in surmounting this difficulty through his marvelous invention of statistical graphs and charts. In the explanation of his innovation he tells the viewer: ‘On inspecting any one of these Charts attentively, a sufficiently distinct impression will be made, to remain unimpaired for a considerable time, and the idea which does remain will be simple and complete, at once including the duration and the amount.’”

From Foreword, Howard Wainer, Jacques Bertin, The Semiology of Graphics.

My intention in writing this book is to help make relevant quantitative information more accessible and useful to more people, enabling them to better understand and reason about their activities and decisions. While an ambitious goal, the current technology infrastructure now enables us to bring greater quantitative literacy into mainstream thought. While it’s notoriously easy to be intentionally or unintentionally deceived by statistics, as well as to be consumed by an unhealthily quantitative approach to life, it’s hard to argue against the use of relevant measurements to inform our lives.

The familiar pantheon of charts and graphs that Playfair invented – since greatly expanded to a variety of variations and extensions, in addition to diagrams and other expository illustrations – make up what we now call information visualization. The subset of this category that portrays strictly quantitative data for purposes of exposition – the charts and graphs part – is visual analytics. The application of quantitative data analysis in support of business enterprise operations is called business intelligence, or BI. BI has been my primary focus as a product designer and user experience, or UX, design manager since 2004.

I think the evolution of our collective relationship to numbers is accelerating in pace. In comparison to writing and image-making, the foundation of today’s staple chart forms and use of quantitative analytics to understand phenomena was pioneered relatively recently. I find it amazing that such staple conventions as line and bar charts are only 250 years old. It’s even more amazing that they have evolved so little over that time period. While a long tail distribution of exotic, specialized quantitative information display types has been created over the years, the vast majority of types used today are tables, pie charts, bar charts, line charts, and scatter plots. The basic model of how a visual analytic expression is presented – as a relatively static, fixed-format illustration amid a larger text narrative – has not changed much over the years.

Communication content, and all designed artifacts to a certain extent, exists as a mix of three basic dimensions of intent:

  1. The Persuasive intent, to change opinion with bias.
  2. The Poetic intent, to entertain or inspire.
  3. The Informational intent, to teach or convey facts.

Designers tend to specialize in one of these dimensions. Regarding visual communication, the first is represented by advertising, the second by fine art, and the third by information & interaction design. While some Communicators practice within one dimension, in fact most combine elements from all dimensions. Most of my professional career has been firmly within the Informational dimension, trying to understand factual content about a subject or how a system or program operates, and communicate this clearly and without bias. This specialized design activity is what Richard Saul Wurman coined Information Design. While communication with informational intent can of course be persuasive and beautiful, and needs subtle tactics associated with these other dimensions to succeed, it is the intent to convey facts effectively on a grand scale that drives me, devoid of promotional distractions and personal expressive urges. In our world of ever-increasing quantitative communication, we need ways to eliminate bias and boost effectiveness in communicating quantitative facts.

As a veteran designer of applications in the service of people engaged in work, I’ve repeatedly bumped into the limits of display technology, system performance, data access, and the interest but overall disinclination of customers and users to invest in, imagine, and adopt new ways of visualizing work content and activities. Based on my three recent years of exploratory research and design, I believe the current technical landscape enables big change for the better in how we understand numbers and in their impact on our workplaces and lives. More people have more access to more information than ever before, in particular quantitative information. Thanks in part to social media and personalization technologies, the potential for this information to be filtered, targeted, and made relevant is, in theory, increasing. There is also a corresponding increase in quantitative literacy and proficiency in presenting, consuming, and directly acting upon this data, helped by new rendering and display technologies. And with mobile computing, of course, these capabilities are becoming available at more times and places. Currently, however, most visual analytic solutions reflect previous efforts to serve large high-paying enterprise customers, and are thus bloated with features designed for highly trained – and high-paying – specialists. As Clayton Christenson’s principle, and associated book titled The Innovator’s Dilemma tells us, companies in such high-margin businesses are beholden to serving their large customers, and thus leave the low-end of the business exposed to inroads by newcomers to the market. Visual analytic market leaders are facing such a dilemma today.

For the past ten years, in my employment with business intelligence software provider BusinessObjects, or BOBJ, and then enterprise resource planning software – or ERP – provider SAP, I’ve specialized in the creation of software tools and applications for the design and consumption of visual analytics. SAP acquired BOBJ in 2007, to add its specialized business intelligence capabilities to SAP’s broad suite of business software. BI enables an organization to collect and consume quantitative data about its business so as to better understand its performance and operations.

In the first four years with BOBJ, my role was to introduce the user experience, or UX, discipline to the company, establish its practice in the development organization, and build a team of designers, researchers, and managers to upgrade and maintain UX quality across a suite of products. After a stint in SAP’s advanced UX design team in Palo Alto, I returned to SAP’s analytics practice in 2010 to specialize in a combination research/design/evangelism role to envision next-generation visual analytic solutions. The result of this effort was a number of prototype projects that led to LAVA, a design language for visual analytic environments intended for broad application across the SAP product suite. The key driver behind LAVA was simplicity and low cost, which translated into some fundamental innovations that, with the backing of a large company like SAP, stand to improve the clarity and reach of visual analytic consumption in the workplace and beyond.

Having the ability to rationally understand and cross-compare quantitative facts at scale is not only good for society, but a sound business to be in as well. Our means of showing quantitative data have not kept pace with its supply and need, and need a thorough and basic re-design to take full advantage of the new potential. Current charting conventions are becoming obsolete, yet we see surprisingly little innovative design leadership in the industry. During LAVA’s development, I found myself surprised that nobody had before arrived at our fairly simple and basic conclusions. The resulting suspicions made me extra careful to check and re-check the viability of our recommendations, and to search for any precedents to our emerging intellectual property. When no serious barriers were revealed, we pressed on.

2.0.1 REFERENCES

2.1 | The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (book) | Clayton M. Christensen | HarperBusiness Essentials

2.2 | The Semiology of Graphics (book) | Jacques Bertin | Esri Press