"The effort was none other than to do the hard work of bridging numbers and people, and building that work into products that entice, even seduce, skeptical users to invest in an unfamiliar activity."
Mr. Armitage’s book has been published online and free-of-charge by the Interaction Design Foundation. The Foundation’s founder, Mads Soegaard, shared their vision: "We offer complete, unrestricted and free access to our chapters/books/textbooks in online versions. Everything we do is a labour of love and not a business model. We walk the talk of altruism.” The IDF hosts a wealth of free materials and textbooks, with contributions from top academics and professionals.
The book is a worthy subject delivered by a worthy organization. I was happy to share my thoughts.
"Bringing Numbers to Life" describes the how, what, and why of a “design-led innovation in visual analytics.” The author is a lead designer at SAP (update: John Armitage is now head of UX design for Host Analytics), the software behemoth and vendor for such business intelligence products as BusinessObjects, Lumira, Roambi, and Crystal Reports. He was charged with creating a unified design model across these products to make them more effective in the communication of data. In his words:
“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."
The analyst’s office is filled with books by Edward Tufte, Stephen Few, and Alberto Cairo providing guidance on how to visualize data. But what about the designer or developer of analytical solutions? Their challenge is in many ways more complex.
Armitage makes an important distinction between “Artisanal" and “Production" solutions. It is one thing to craft a one-off visualization solution for a specific purpose. In these cases, the author knows the data, the audience, and the specific message they want to convey. His challenge is to develop a system that can repeatedly deliver high quality data visualization and analytical tools. It is the difference between the carpenter who can craft a single table and the engineer who can create a factory that delivers a thousand tables. The requirements, skills, and frameworks are very different.
Armitage has researched this topic thoroughly. Over many years, he worked with internal SAP teams and consultants to define and refine his LAVA (Lightweight Applied Visual Analytics) framework. His framework describes important pieces for any analytical solution, and how these pieces should fit together. While he defines a specific nomenclature, the elements are universal building blocks. A few examples:
- Charts can exist at different levels of detail and fidelity. When we want to represent a concept, it may appear as a single number or sparkling (“micro chart”) or a fully-labeled trend chart (“chart”) or as part of a multi-component, interactive visualization (“meta chart”).
- Analytical tools need a hierarchy of components that allow a user to consider a broad concept, drill into more detailed topics, and explore specific data.
- Modern analytical applications need to offer much more than the visualization of data. Features for sharing, collecting insights, and personalization are necessary to deliver a complete analytical tool.
- The traditional single-page dashboard design is antiquated. "Scrolling effectively increases the virtual size of your display outside the borders of your window or device, and is a basic convention for digital content consumption that has been ignored in traditional dashboard design."
Armitage is also a critic of the increasingly complex and bloated analytical solutions — something I hear more and more from companies frustrated with visual analytics tools like Tableau.
"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."
Despite my fondness for the topic and appreciation of Armitage’s evident research, the book has issues that I found hard to overlook.
Armitage indulges in lengthy tangents, personal biography, and an obsession with the specifics of the SAP landscape and politics — providing us with sentences like this:
“I even produced a farcical off-site video on the theme of multinational collaboration, and a comedy routine – based on the famous “Who’s on First?” from Abbot and Costello – to poke fun at working in multinational teams. I performed the latter live with Jay Xiong at our 2012 office holiday party in Shanghai."
He also appears to be fighting a public battle with the leadership of SAP to adopt his LAVA model in the pages of his book:
"Although LAVA, in particular the Lattice, pointed to similar effects for quantitative data, it was difficult for some people who were particularly close to BI to acknowledge its potential. The definitive objection from this faction was that LAVA “does not match Our metaphor”, which was of course precisely the point. LAVA is a new metaphor, and one that’s necessary for achieving SAP’s product aspirations."
I have a deeper concern with some of the recommendations for visual solutions. One of the features concepts is a "Lattice chart”. I found the example provided to be confusing and complex. Designing visualizations is an exercise in finding simplicity and accessibility for your audience. The image below is crammed full of information, but lacks the legend or space for a typical user to know what it all means.
Perhaps my biggest concern is with Armitage’s long-term vision. He expresses a desire to create an analytics world where human intervention isn’t necessary to communicate data effectively.
With this scalable framework in place, we can start to dream of efficiencies on a large scale, with entire Board Sets generated automatically from adequately-provisioned data warehouses. Filters, Panels, and Lattices can be determined with a rules engine automatically from combinations of Measures and Dimensions. Galleries can be populated by algorithm-generated charts derived from the most relevant Lattice Layers. Points can be created from data set indexing and data mining, and populated with major category representations and outliers. Multiple data sets can be organized into groups and presented as Board Sets, or one giant data set can be subdivided into Boards according to individual or sets of Measures or Dimensions, with these Boards further subdivided into Categories and Lattice stacks according to the depth and complexity of the data. These efficiencies will allow more people to use data to make decisions, and require fewer people to support them.
In my experience, data communication needs to start with an intimate knowledge of the business context, an appreciation for your audience, and an understanding of what it takes to make them more effective in their jobs. These aren’t things that can be scraped from a database.
Philosophical issues aside, this is a book about Armitage’s journey in trying to change how an organization delivers analytics, his process and research, and the quite-useful framework that came from his journey. As someone who has tackled the same design challenges in creating a new kind of tool for visualizing data, I appreciated being able to get an up-close view of how another professional wrestled with common challenges.
In describing his conclusions, John Armitage notes, "During LAVA’s development, I found myself surprised that nobody had before arrived at our fairly simple and basic conclusions."
Don’t worry, John, you’re not alone.