lots of fancy spreads on large properties. Properties in Watertown are also on the older side, mostly built between 1860 and 1950, but are much smaller—which, again, fits with that city’s working-class history as the home of a major nineteenth-century armory. In Waltham, most of the houses are between 1,000 and 2,500 square feet, and were built between 1945 and 1970—reflecting that city’s history as one of the many suburbs where lots of ranch-type housing sprang up right after World War II. Finally, there’s Brookline, which has lots of very small but very new and very expensive housing—a legacy of the wave of high-end condominiums built in that town from 1980 onward.
If you examine the left side of the screen shot, you’ll see a few of the tools DecisionIris provides to users who want to try out different views of a dataset. As Shen-Hsieh and Crawley showed me in two separate demos, the software makes it possible to filter data along any dimension, or all of them at once. So, for example, if you were house-shopping and you were only interested in properties with at least two bedrooms built after 1960 and costing no more than $1 million, you could move a few sliders and check or uncheck a few boxes and watch while the graph instantly responds, showing which cities offer the most choices fitting your criteria. (Looks like you’re headed to Waltham, in this case.) By mousing over an individual dot, you can get a pop-up window giving you the address and price of that particular house—yet more data packed into a single view.
In the real estate example, it’s obvious how DecisionIris can, in a single view, convey insights that would take hours to arrive at if you were limited to the narrow, fussy, and largely-text based search interfaces available at most online property-search databases. But the housing data is only one illustration of what Visual I|O’s software can do. Most of the company’s actual clients are in healthcare and pharmaceutical companies like Merck and Johnson & Johnson, who use the timeline-based views available in DecisionIris to get a handle on things like product development life cycles. (See the chart at left.)
I don’t have time or space to describe the examples that Shen-Hsieh and Crawley showed me; suffice it to say that if you’re a drug company and you have 17 potential products in the works, DecisionIris can help you determine things like whether they’ll be hitting the market in a reasonably distributed manner, or whether you’ll have long drought years between rollouts. Based on that, you can examine how many people and how much money you’re putting toward each product, and re-prioritize if necessary. Problems and issues jump out at you, just the way the real estate trends do in the previous example. But equally important, you can derive such insights by playing with the data in real-time, rather than relying on staff analysts to create big, ponderous, static PowerPoint presentations, which inherently limit the kinds of questions that executives bother to ask.
Ultimately, it’s all about visual experimentation—a combination of play and serious thought. “Architecture involves the left and right brain merging,” as Shen-Hsieh puts it. “First you have to understand how people are going to experience a space or an object, then translate that into how it’s going to be built. That is the same approach we take to visualization.”
Visual I|O collected its one and only round of venture financing from Switzerland-based Logispring in 2006, and has 20 employees. Shen-Hsieh says that awareness of DecisionIris is spreading fast in the life sciences industry—revenues should double this year—and that company engineers are thinking about how to apply the visualization tools to other industries.
After Shen-Hsieh’s visit, I requested a more detailed demo of DecisionIris, which was graciously provided via Web meeting by John Crawley, a senior solutions engineer at the company. Before we finished, I asked Crawley—a Brit who has been with Visual I|O for about a year—what had attracted him to the company.
“I come from the world of databases and systems, but I have somewhat of a natural flair for artistic things,” Crawley says. “When I first talked to Mark and Angela about the way they use things like color schemes to communicate hidden information, it was the first time I’d ever come across a business intelligence company that actually pays attention to the aesthetics. Most companies I’ve worked for are very focused on ‘whacking and stacking’ the data. But Mark and Angela think in ways that, I dare say, most business intelligence companies are simply unable to think.”
Which goes straight back to the founders’ training as architects. Despite years spent working with software developers and database engineers, Shen-Hsieh says she’s still an a conceptual artist at heart. “But my medium is commerce, not paint or plaster,” she says. “It’s all about, how can you find new ways to utilize visual and experiential media to communicate ideas, concepts, information, and data.”
