Software // Information Management
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11/16/2009
07:00 AM
Seth Grimes
Seth Grimes
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True BI for the Masses

BI for the Masses is overused marketing-speak meant to suggest that Vendor X's break-out Product Y is going to enable/deliver business intelligence beyond the 15%-20% of knowledge workers who currently do BI. Well, I have my own notion of BI for the Masses, and it is NOT what it's typically claimed to be. What it is is illustrated by a couple of recent New York Times data visualizations...

BI for the Masses is overused marketing-speak meant to suggest that Vendor X's break-out Product Y is going to enable/deliver business intelligence beyond the 15%-20% of knowledge workers who currently do BI. (I got that estimate from a chat with industry veteran Dave Wells, who says the figure becomes 40% if you include Excel.) Well, I have my own notion of BI for the Masses, and it is NOT:

  • Some slick, supposedly easier-to-use dashboard
  • Reports routed to mobile devices.
  • Excel, no matter how many new capabilities Microsoft and third parties stuff in there.
BI for the Masses is accessible, to-the-point BI delivered via everyday channels. Analytical functionality is stripped down to essentials that suit the user, data, and medium. IT is at arm's length. It's BI where the user -- the consumer -- may not even know he or she is doing BI. And it's illustrated by a couple of recent New York Times data visualizations that I'll describe for you now.Let's first reinforce basic points: BI is about a lot more than dashboards, reports, pivot tables (a.k.a. OLAP), and charts. BI is equally about information content and about the business processes that produce and consume BI-derived insights that drive decision making. Good BI contextualizes information; it helps users see relationships, trends, and connections. And while BI usually involves structured, numerical data, there are notable exceptions where information is sourced and/or rendered in qualitative form.

Two BI visualizations

Given these premises, I hope you will see the New York Times's November 9, 2009 Berlin Wall photo gallery as BI.New York Times photo -- Berlin WallThe Times pairs pictures from before the 1989 destruction of the wall with recent photos of the same locations. Paired photos share spaces on the page. You reveal more of one photo and less of the other by sliding an in-frame control left and right. If you're like me, you'll spend time studying the photos, more time than if the pictures were positioned side-by-side the way before-and-after shots typically are. The experience is immediate and engaging and it is very effective at conveying comparative information.

A second Times page lets you explore U.S. unemployment data. The Jobless Rate for People Like You shows that "not all groups have felt the recession equally" by juxtaposing chart lines for different population subgroups.New York Times unemployment chartYou can see the unemployment rate over time along four, other analysis dimensions (in addition to time): race, age range, sex, and education level. For each, you can select all values or a particular value. With each selection, the dataset is filtered to provide a line for your choice. At the same time, you see traces of lines for other subgroup choices with a mouse-over effect that highlights them, which helps you compare values for different subgroups.

Selectors for all four analysis dimensions are displayed simultaneously on the page. While this arrangement might well predate the Web, it resembles most a faceted search results interface similar to what you see at a site such as Newssift.com. You don't navigate hierarchies; you just click and see. The interface and the information formatting are simple, but they are not simplistic.

This latter statement is true of both Times examples. In both cases, the presentation suits the data, the channel, and the user. Both examples also relate to mass-market interests and are mass-market available, but that's almost incidental. More germane is that both help tell a story, to which end they're embedded in pages with explanatory narrative and, in the case of the Berlin Wall photos, with small maps that show the photos' locations. The sum of these factors make these examples of true BI for the Masses.

Collective but not collaborative

Check out one other Times Berlin Wall feature, The View From the Wall, which offers an array of reader-submitted photos and memories of the wall, before and after its fall. This interactive feature is raw data (in photographic form) that, while edited -- curated, if you prefer -- unlike the Berlin Wall photo gallery I cite above, does not deliver significant analytical insight. For this reason, this latter feature is not BI, just as IBM's worthy Many Eyes site is not.

The two sites lack the narrative thread(s) that can transform unorganized information into knowledge. They fall short because while they aggregate material from varied sources, they do not deliver added value, any larger point. They are collective but they are not collaborative. The wholes, in these cases, are no greater than the sums of the parts. The two do, however, point to an alternative route to true BI for the Masses: use of open social channels to collect thematically unified information and collaboratively transform it into (business) intelligence.

A plug for Information Aesthetics

As an aside: I learned about the New York Times visualizations from the Information Aesthetics blog (@infosthetics on Twitter; I'm @sethgrimes there myself), which is a great resource for keeping up with cutting-edge visualizations from intricate, creative big-data presentations to others that are stunning for their ability to convey information via the most simple but effective of user interfaces like those in the Times. My thanks to Andrew Vande Moere for his blog!BI for the Masses is overused marketing-speak meant to suggest that Vendor X's break-out Product Y is going to enable/deliver business intelligence beyond the 15%-20% of knowledge workers who currently do BI. Well, I have my own notion of BI for the Masses, and it is NOT what it's typically claimed to be. What it is is illustrated by a couple of recent New York Times data visualizations...

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